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Python for Data Science: How to Scrape Website Data via the Internet's Top 300 APIs

In the bull market, when BTC goes up and breaks its all-time high (ATH), the accumulation process for a new ATH is underway. This is good for altcoins, which go up next. SOL and ETH are the first altcoins to start moving, just like many other Layer 1’s. Investors are looking for ways to maximize their returns on ETH because of gas fees and exploring alternative options, which brings us to Layer 2. Projects like BASE & ARBITRIUM, DOGECHAIN, OPTIMISM, POLYGON, and others that are built on ETH are starting to grow too. Once these altcoins start to gain traction, investors start looking for ways to diversify their profits. Things started to change this year. Once ETFs were approved for BTC, altcoins didn't see much growth. They rarely took off. While some meme tokens saw huge gains (up to 10x), altcoins tried to triple their value and then fell back to their original levels. The same thing happened again after the second big spike, this time with an ETF for ETH. Once again, while ETH peaked, altcoins stayed at the bottom. And yes, meme tokens were making waves again. Crypto wallet activity is at an all-time high, and it is still on the rise. Over the course of the year, it has tripled, compared to previous cycles that saw a tenfold increase. I'm seeing the greatest increase in wallet activity on Solana and Base. This suggests that the alt season hasn't started yet, and could indirectly confirm the BTC supercycle theory. The rise of decentralized launchpads has been a major driver of the growth of these networks. You don't need to know any coding to create a token. But launchpads have some technical limitations, they're great for creating template tokens. It's worth noting that the only category of template tokens that can attract a large community are meme tokens! That's why the most successful and first Launchpads is based on Solana and was designed to run meme tokens. Solana is where a lot of memes started. Is that just a coincidence? It's not a coincidence. It's a trend. Many memes launched on pump.fun are later launched as analogs on other networks, where some of them become more successful than the original projects. The second most popular moonshot launchpad from dexscreener also started on Solana and now supports Base. ETH saw little change in developers’ activity from year to year (though it's still the leader in this area). BTC and its L2s, on the other hand, have seen a significant increase in development activity, doubling in the past year. This growth is likely to continue and set new trends in the coming year. To avoid confusion, I will refer to the new Meme tokens currently trending in the short-term market as "Memes 2.0". There are several differences between Memes 2.0 and Meme tokens from previous cycles. The main difference is how the tokens were launched. Memes 2.0 launch on launchpads using the Bonding Curve, which we call a fair launch. What makes this launch different is how the tokens are distributed after they're listed on the dex. Specifically, only a small percentage of the supply is available for purchase in the liquidity pool. This means that if you try to sell a large number of tokens, the price will drop dramatically. In fact, 99.999% of these tokens going to 0 after being listed on dex. This is an important point. I like to think of this as natural selection. If meme 2.0 has a strong narrative and a healthy community by the time when whales and/or developers are out, a CTO (community token owned) is created. The community takes ownership of the project's development. With a strong community, the project is already more likely to succeed than at the Bonding Curve level. A short-term speculative meme 2.0 can become a long-term meme. So the main things that differentiate memes 2.0 from those of previous cycles are how popular the CTOs are, how the tokens are distributed, and how they're launched. Some good examples are GOAT, Michi, FWOG, and MOODENG. On top of that, memes 2.0 have become a kind of tokenized representation of everything that happens in the world. It's like a news channel. If you want to see what's trending, just go to pump.fun and look at the top active tokens :) Murad's theory of the memcoin supercycle suggests that we may soon see a cycle in which memcoins surpass traditional coins. This will be based on community sentiment, emotion, and the speculative lure of getting rich quickly. Memcoins are different from traditional projects because they grow through hype and the ability to rally large communities. The main things to watch out for are: The popularity of meme launchpads (pump.fun, moonshot) and the fact that you can deploy memtokens almost for free without any programming skills or blockchain knowledge.
Many moonboys on X and other socials
More and more people are looking to invest in high risk assets like memcoins due to the instability in the world and high inflation across the board
We're seeing a shift where communities driven by profit are replacing those driven by blockchain and technology.
Major exchanges like Binance and Coinbase are listing more and more meme tokens, fueling a speculative frenzy. Meme token supercycle theory has no real fundamentals like memcoins itself. They attract traders with the promise of quick profits. There's no real utility or technology behind them. It's just people chasing the dream of becoming a millionaire with just $10. It reminds me of the ICO boom I was a part of. Even the most obvious scams raised money. They had fake profiles of team members, renamed social networks, made beautiful websites, and that was it. They just sold the standard tokens and out. Yes, there were some real projects, but only a few. Investors were just as fixated on quick profits as they are today. After the whole system crashed, most of them lost their money. Then they started thinking about technology and utility. It seems that the new generation of investors hasn’t learned from our mistakes and is falling into the same traps we did. Memcoins like Dogecoin, Pepe, and Shiba Inu will probably benefit from this hype around memes either way. On the other hand, most Meme 2.0 will experience a rug pool, not a supercycle:) Given the current market conditions, it's likely that the surviving 2.0 memtokens will see zero value due to their low liquidity and high FDV. References State of Crypto Report 2024
The case for a 2024 bull run In the bull market, when BTC goes up and breaks its all-time high (ATH), the accumulation process for a new ATH is underway. This is good for altcoins, which go up next. SOL and ETH are the first altcoins to start moving, just like many other Layer 1’s. Investors are looking for ways to maximize their returns on ETH because of gas fees and exploring alternative options, which brings us to Layer 2. Projects like BASE & ARBITRIUM, DOGECHAIN, OPTIMISM, POLYGON, and others that are built on ETH are starting to grow too. Once these altcoins start to gain traction, investors start looking for ways to diversify their profits. Things started to change this year. Once ETFs were approved for BTC , altcoins didn't see much growth. They rarely took off. While some meme tokens saw huge gains (up to 10x), altcoins tried to triple their value and then fell back to their original levels. The same thing happened again after the second big spike, this time with an ETF for ETH. Once again, while ETH peaked, altcoins stayed at the bottom. And yes, meme tokens were making waves again. BTC Crypto wallet activity is at an all-time high, and it is still on the rise. Over the course of the year, it has tripled, compared to previous cycles that saw a tenfold increase. I'm seeing the greatest increase in wallet activity on Solana and Base. This suggests that the alt season hasn't started yet, and could indirectly confirm the BTC supercycle theory. The rise of decentralized launchpads has been a major driver of the growth of these networks. You don't need to know any coding to create a token. But launchpads have some technical limitations, they're great for creating template tokens. It's worth noting that the only category of template tokens that can attract a large community are meme tokens! That's why the most successful and first Launchpads is based on Solana and was designed to run meme tokens. Solana is where a lot of memes started. Is that just a coincidence? It's not a coincidence. It's a trend. Many memes launched on pump.fun are later launched as analogs on other networks, where some of them become more successful than the original projects. The second most popular moonshot launchpad from dexscreener also started on Solana and now supports Base. ETH saw little change in developers’ activity from year to year (though it's still the leader in this area). BTC and its L2s, on the other hand, have seen a significant increase in development activity, doubling in the past year. This growth is likely to continue and set new trends in the coming year. To avoid confusion, I will refer to the new Meme tokens currently trending in the short-term market as "Memes 2.0". There are several differences between Memes 2.0 and Meme tokens from previous cycles. The main difference is how the tokens were launched. Memes 2.0 launch on launchpads using the Bonding Curve, which we call a fair launch. What makes this launch different is how the tokens are distributed after they're listed on the dex. Specifically, only a small percentage of the supply is available for purchase in the liquidity pool. This means that if you try to sell a large number of tokens, the price will drop dramatically. In fact, 99.999% of these tokens going to 0 after being listed on dex. This is an important point. I like to think of this as natural selection. If meme 2.0 has a strong narrative and a healthy community by the time when whales and/or developers are out, a CTO (community token owned) is created. The community takes ownership of the project's development. With a strong community, the project is already more likely to succeed than at the Bonding Curve level. A short-term speculative meme 2.0 can become a long-term meme. So the main things that differentiate memes 2.0 from those of previous cycles are how popular the CTOs are, how the tokens are distributed, and how they're launched. Some good examples are GOAT, Michi, FWOG, and MOODENG. On top of that, memes 2.0 have become a kind of tokenized representation of everything that happens in the world. It's like a news channel. If you want to see what's trending, just go to pump.fun and look at the top active tokens :) Murad's theory of the memcoin supercycle suggests that we may soon see a cycle in which memcoins surpass traditional coins. This will be based on community sentiment, emotion, and the speculative lure of getting rich quickly. Memcoins are different from traditional projects because they grow through hype and the ability to rally large communities. The main things to watch out for are: The popularity of meme launchpads (pump.fun, moonshot) and the fact that you can deploy memtokens almost for free without any programming skills or blockchain knowledge. Many moonboys on X and other socials More and more people are looking to invest in high risk assets like memcoins due to the instability in the world and high inflation across the board We're seeing a shift where communities driven by profit are replacing those driven by blockchain and technology. Major exchanges like Binance and Coinbase are listing more and more meme tokens, fueling a speculative frenzy. The popularity of meme launchpads (pump.fun, moonshot) and the fact that you can deploy memtokens almost for free without any programming skills or blockchain knowledge. Many moonboys on X and other socials More and more people are looking to invest in high risk assets like memcoins due to the instability in the world and high inflation across the board We're seeing a shift where communities driven by profit are replacing those driven by blockchain and technology. Major exchanges like Binance and Coinbase are listing more and more meme tokens, fueling a speculative frenzy. Meme token supercycle theory has no real fundamentals like memcoins itself. They attract traders with the promise of quick profits. There's no real utility or technology behind them. It's just people chasing the dream of becoming a millionaire with just $10. It reminds me of the ICO boom I was a part of. Even the most obvious scams raised money. They had fake profiles of team members, renamed social networks, made beautiful websites, and that was it. They just sold the standard tokens and out. Yes, there were some real projects, but only a few. Investors were just as fixated on quick profits as they are today. After the whole system crashed, most of them lost their money. Then they started thinking about technology and utility. It seems that the new generation of investors hasn’t learned from our mistakes and is falling into the same traps we did. Memcoins like Dogecoin, Pepe, and Shiba Inu will probably benefit from this hype around memes either way. On the other hand, most Meme 2.0 will experience a rug pool, not a supercycle:) Given the current market conditions, it's likely that the surviving 2.0 memtokens will see zero value due to their low liquidity and high FDV. References State of Crypto Report 2024 The case for a 2024 bull run State of Crypto Report 2024 State of Crypto Report 2024 The case for a 2024 bull run The case for a 2024 bull run

Meme Tokens 2.0 or ICO 2.0?

Bitcoin's surge past $70,000 has reignited the memecoin market, with tokens like $GOAT delivering mind-bending returns of 10,000x in just five days. After analyzing trading data from the TOP 25 memecoins, we've uncovered four proven strategies that separate successful traders from the crowd. Here's your comprehensive playbook for navigating this high-reward market. Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR Strategy 1: Master the Holder Distribution Game Understanding holder distribution patterns has become a crucial edge in memecoin trading. My analysis reveals a three-tier system that smart money watches closely. The first tier, comprising addresses exceeding 800,000 holders, is led by SHIB with 1.51M addresses, followed by DOG, DOGE, MEME, and DEGEN. These tokens consistently demonstrate higher trading volumes and market stability. The second tier, ranging from 300,000 to 800,000 holders, includes BONK and BRETT, each maintaining approximately 700,000 addresses. Notably within this tier, PEPE and WIF have shown growth trajectories, with 30-day increases of 3.02% and 4.86% respectively, outpacing other larger counterparts. The third tier encompasses tokens with 50,000 to 300,000 holders. MEW and MIGGLES stand out with over 100,000 addresses each, while HIGHER, FLOKI, POPCAT, MOCHI, PONKE, MOODENG, and MYRO occupy the 50,000 to 100,000 range. This tier exhibits the highest volatility but also presents significant growth potential. Strategy 2: Decode Liquidity Patterns for Optimal Entry Memecoin trading demands sophisticated liquidity analysis. According to bitsCrunch data, SHIB and WIF currently lead with seven-day volumes reaching $70.2B and $67.3B respectively, but raw volume only tells part of the story. The real profit opportunity lies in understanding the evolving nature of memecoin liquidity. Large-cap memecoins are increasingly becoming pseudo-stable assets, attracting strategic "buy the dip" behavior. Meanwhile, small-cap tokens maintain their role as speculation vehicles, offering higher risk-reward ratios. A prime example is TRUMP's recent performance, showing a buy/sell ratio of 1.66 and 15.9% price increase during the US election events. This demonstrates how traders leverage news catalysts for enhanced returns. Strategy 3: Implement the Advanced Security Matrix Successful memecoin trading requires mastering three critical security dimensions. First, monitor authority concentration metrics to spot red flags early. Second, verify liquidity lock status to protect against rug pulls. Third, analyze address overlap patterns to identify potential market manipulation. My research shows that projects with founder allocations below 5% demonstrate significantly higher success rates. This metric alone has proven invaluable for early identification of promising investments. The emergence of sophisticated trading patterns requires constant vigilance. Watch for complex fund flows between large holders - these often precede significant price movements. Projects showing unusual 24-hour gains warrant particular scrutiny, especially when accompanied by suspicious wallet interactions. Strategy 4: Leverage Social Analytics The social landscape of memecoin trading has evolved dramatically. Traditional metrics focusing on KOL followers (previously requiring 3-5 influencers with 100K+ followers) have given way to more nuanced indicators. Current market leaders often emerge from projects with moderate but highly engaged communities rather than those with massive but passive followings. The temporal distribution of social engagement has become a key success predictor - projects gaining steady support consistently outperform those relying on concentrated promotion campaigns. The New Playbook for Success The Meme Tool Ecosystem Landscape presents five core segments of the overall industry: research tools, safety checks, tracking systems, trading platforms, and launch platforms. Success in today's memecoin market demands a sophisticated approach combining these four strategies. First, establish baseline metrics for project evaluation. Successful launches typically achieve $500-1000 hourly volumes within their first trading day. The crucial $100K market cap milestone serves as an early validation point - 87% of successful projects cross this threshold before significant growth phases. Next, implement a comprehensive monitoring system tracking technical indicators, on-chain analytics, and social sentiment. Pay special attention to volume anomalies, position concentration shifts, and liquidity pool dynamics. Finally, maintain strict risk management protocols. Use tiered stop-loss systems, monitor liquidity ratios continuously, and track social sentiment shifts across platforms. Remember, even the best strategies require proper position sizing and risk controls. The memecoin market continues to offer exceptional return opportunities for traders who approach it systematically. By implementing these four strategies while maintaining rigorous risk management, you position yourself to capture the next wave of memecoin profits. Disclaimer: This analysis is for informational purposes only and should not be construed as financial advice. Cryptocurrency investments carry significant risks, and thorough research is essential before making any investment decisions. Bitcoin's surge past $70,000 has reignited the memecoin market, with tokens like $GOAT delivering mind-bending returns of 10,000x in just five days. After analyzing trading data from the TOP 25 memecoins, we've uncovered four proven strategies that separate successful traders from the crowd. Here's your comprehensive playbook for navigating this high-reward market. Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR Editor’s note: This article is for informational purposes only and does not constitute investment advice. Cryptocurrencies are speculative, complex, and involve high risks. This can mean high prices volatility and potential loss of your initial investment. You should consider your financial situation, investment purposes, and consult with a financial advisor before making any investment decisions. The HackerNoon editorial team has only verified the story for grammatical accuracy and does not endorse or guarantee the accuracy, reliability, or completeness of the information stated in this article. #DYOR Strategy 1: Master the Holder Distribution Game Understanding holder distribution patterns has become a crucial edge in memecoin trading. My analysis reveals a three-tier system that smart money watches closely. holder distribution patterns The first tier, comprising addresses exceeding 800,000 holders, is led by SHIB with 1.51M addresses, followed by DOG, DOGE, MEME, and DEGEN. These tokens consistently demonstrate higher trading volumes and market stability. The second tier, ranging from 300,000 to 800,000 holders, includes BONK and BRETT, each maintaining approximately 700,000 addresses. Notably within this tier, PEPE and WIF have shown growth trajectories, with 30-day increases of 3.02% and 4.86% respectively, outpacing other larger counterparts. The third tier encompasses tokens with 50,000 to 300,000 holders. MEW and MIGGLES stand out with over 100,000 addresses each, while HIGHER, FLOKI, POPCAT, MOCHI, PONKE, MOODENG, and MYRO occupy the 50,000 to 100,000 range. This tier exhibits the highest volatility but also presents significant growth potential. Strategy 2: Decode Liquidity Patterns for Optimal Entry Memecoin trading demands sophisticated liquidity analysis. According to bitsCrunch data, SHIB and WIF currently lead with seven-day volumes reaching $70.2B and $67.3B respectively, but raw volume only tells part of the story. The real profit opportunity lies in understanding the evolving nature of memecoin liquidity. Large-cap memecoins are increasingly becoming pseudo-stable assets, attracting strategic "buy the dip" behavior. Meanwhile, small-cap tokens maintain their role as speculation vehicles, offering higher risk-reward ratios. The real profit opportunity lies in understanding the evolving nature of memecoin liquidity. A prime example is TRUMP's recent performance, showing a buy/sell ratio of 1.66 and 15.9% price increase during the US election events. This demonstrates how traders leverage news catalysts for enhanced returns. Strategy 3: Implement the Advanced Security Matrix Successful memecoin trading requires mastering three critical security dimensions. First, monitor authority concentration metrics to spot red flags early. Second, verify liquidity lock status to protect against rug pulls. Third, analyze address overlap patterns to identify potential market manipulation. My research shows that projects with founder allocations below 5% demonstrate significantly higher success rates. This metric alone has proven invaluable for early identification of promising investments. The emergence of sophisticated trading patterns requires constant vigilance. Watch for complex fund flows between large holders - these often precede significant price movements. Projects showing unusual 24-hour gains warrant particular scrutiny, especially when accompanied by suspicious wallet interactions. Strategy 4: Leverage Social Analytics The social landscape of memecoin trading has evolved dramatically. Traditional metrics focusing on KOL followers (previously requiring 3-5 influencers with 100K+ followers) have given way to more nuanced indicators. Current market leaders often emerge from projects with moderate but highly engaged communities rather than those with massive but passive followings. The temporal distribution of social engagement has become a key success predictor - projects gaining steady support consistently outperform those relying on concentrated promotion campaigns. The New Playbook for Success The Meme Tool Ecosystem Landscape presents five core segments of the overall industry: research tools, safety checks, tracking systems, trading platforms, and launch platforms. research tools, safety checks, tracking systems, trading platforms, and launch platforms. Success in today's memecoin market demands a sophisticated approach combining these four strategies. First, establish baseline metrics for project evaluation. Successful launches typically achieve $500-1000 hourly volumes within their first trading day. The crucial $100K market cap milestone serves as an early validation point - 87% of successful projects cross this threshold before significant growth phases. Next, implement a comprehensive monitoring system tracking technical indicators, on-chain analytics, and social sentiment. Pay special attention to volume anomalies, position concentration shifts, and liquidity pool dynamics. Finally, maintain strict risk management protocols. Use tiered stop-loss systems, monitor liquidity ratios continuously, and track social sentiment shifts across platforms. Remember, even the best strategies require proper position sizing and risk controls. The memecoin market continues to offer exceptional return opportunities for traders who approach it systematically. By implementing these four strategies while maintaining rigorous risk management, you position yourself to capture the next wave of memecoin profits. Disclaimer: This analysis is for informational purposes only and should not be construed as financial advice. Cryptocurrency investments carry significant risks, and thorough research is essential before making any investment decisions.

Memecoin Market Analysis: 4 Strategies for Better Returns in 2024

Ethereum co-founder Vitalik Buterin has found himself in the spotlight again, this time, over his charitable engagement with meme coin projects. Buterin recently acknowledged two meme coins, EBULL and MOODENG, for their charitable contributions, stating that any digital tokens sent to him would be donated to charity. This statement has sparked conversations within the crypto community, particularly regarding the potential risks for investors. Vitalik's Acknowledgment of Meme Coins In a tweet shared this week, Vitalik Buterin expressed appreciation for meme coin projects that are donating portions of their supply directly to charity. He specifically mentioned the EBULL and MOODENG tokens, acknowledging their efforts in supporting charitable causes, and noted that digital tokens sent to him would be donated. The 10 billion MOODENG tokens, for example, were directed towards anti-airborne disease technology. While Buterin's gesture appeared genuine, it did not go unnoticed by the crypto community, where questions arose regarding the impact of such public endorsements. The acknowledgment of meme coin projects that may not have undergone thorough scrutiny has raised concerns among investors and market analysts. Concerns From the Community One notable figure in the crypto space, known as Crypto Rug Muncher, who has built a reputation for exposing potential cryptocurrency scams, voiced concerns  about Buterin's actions. Crypto Rug Muncher specifically raised alarms since September this year, about the EBULL token, claiming it could be a potential scam, and is warning now that public endorsements from influential figures could inadvertently legitimize projects that may not have transparent operations and could even be fraudulent. According to Crypto Rug Muncher, the acknowledgment of EBULL meme coins contributed to a price surge in this digital token, attracting new crypto investors. However, he warned that such recognition could allow insiders to capitalize on the elevated prices by selling off their holdings, potentially leaving unsuspecting buyers with losses. Crypto Rug Muncher emphasized that charitable donations are commendable and should not come at the cost of investor safety. He argued that projects might misuse the charity angle to gain credibility and attract more investors, all while masking potentially deceptive practices. His specific concerns about the EBULL token highlight the risks of boosting projects without thorough due diligence. Ethical Questions About Promotion and Due Diligence The situation has also prompted broader discussions on the ethical responsibility of public figures and crypto platforms when promoting crypto projects, even indirectly. The crypto industry is no stranger to scams, and cryptocurrency investors often rely on the endorsements of trusted figures. This makes it critical for those with influence to exercise caution and conduct due diligence before publicly acknowledging any projects. Many in the community believe that even charitable gestures can have unintended consequences, especially if the promoted projects turn out to be fraudulent. The key question that arises is: Should influential figures be more cautious when accepting tokens for charity from projects that may not have undergone proper vetting? Conclusion Vitalik Buterin's recent actions have brought to light the delicate balance between supporting charitable causes and maintaining investor trust. While Buterin's intentions may have been noble, the reaction from the community underscores the importance of thorough due diligence, even when engaging in charity. As the crypto market continues to evolve, transparency and caution are essential to prevent any unintended harm to investors. Ethereum co-founder Vitalik Buterin has found himself in the spotlight again, this time, over his charitable engagement with meme coin projects. Buterin recently acknowledged two meme coins, EBULL and MOODENG, for their charitable contributions, stating that any digital tokens sent to him would be donated to charity. This statement has sparked conversations within the crypto community, particularly regarding the potential risks for investors. Vitalik's Acknowledgment of Meme Coins In a tweet shared this week, Vitalik Buterin expressed appreciation for meme coin projects that are donating portions of their supply directly to charity. He specifically mentioned the EBULL and MOODENG tokens, acknowledging their efforts in supporting charitable causes, and noted that digital tokens sent to him would be donated. The 10 billion MOODENG tokens, for example, were directed towards anti-airborne disease technology. tweet While Buterin's gesture appeared genuine, it did not go unnoticed by the crypto community, where questions arose regarding the impact of such public endorsements. The acknowledgment of meme coin projects that may not have undergone thorough scrutiny has raised concerns among investors and market analysts. Concerns From the Community One notable figure in the crypto space, known as Crypto Rug Muncher, who has built a reputation for exposing potential cryptocurrency scams, voiced concerns about Buterin's actions. Crypto Rug Muncher specifically raised alarms since September this year, about the EBULL token, claiming it could be a potential scam, and is warning now that public endorsements from influential figures could inadvertently legitimize projects that may not have transparent operations and could even be fraudulent. voiced concerns According to Crypto Rug Muncher, the acknowledgment of EBULL meme coins contributed to a price surge in this digital token, attracting new crypto investors. However, he warned that such recognition could allow insiders to capitalize on the elevated prices by selling off their holdings, potentially leaving unsuspecting buyers with losses. Crypto Rug Muncher emphasized that charitable donations are commendable and should not come at the cost of investor safety. He argued that projects might misuse the charity angle to gain credibility and attract more investors, all while masking potentially deceptive practices. His specific concerns about the EBULL token highlight the risks of boosting projects without thorough due diligence. Ethical Questions About Promotion and Due Diligence The situation has also prompted broader discussions on the ethical responsibility of public figures and crypto platforms when promoting crypto projects, even indirectly. The crypto industry is no stranger to scams, and cryptocurrency investors often rely on the endorsements of trusted figures. This makes it critical for those with influence to exercise caution and conduct due diligence before publicly acknowledging any projects. broader discussions Many in the community believe that even charitable gestures can have unintended consequences, especially if the promoted projects turn out to be fraudulent. The key question that arises is: Should influential figures be more cautious when accepting tokens for charity from projects that may not have undergone proper vetting? Conclusion Vitalik Buterin's recent actions have brought to light the delicate balance between supporting charitable causes and maintaining investor trust. While Buterin's intentions may have been noble, the reaction from the community underscores the importance of thorough due diligence, even when engaging in charity. As the crypto market continues to evolve, transparency and caution are essential to prevent any unintended harm to investors.

Vitalik Buterin's Charity Meme Coins Donations Raise Questions in Crypto Community

Authors:
(1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk);
(2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk);
(3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction
Course information
Motivation and intervention
Findings
Discussion
Conclusion and References 5 Discussion The purpose of this study was to investigate the effects of a gamification element in a mathematical sciences course taught in flipped format. The study compared levels of engagement with pre-lecture quizzes before and after the gamification intervention was introduced. Comparisons were made across semesters (different courses, same students) and across academic years (same course, different students). Our study found that in both cases the level of engagement (measured by average percentage of students completing pre-lecture quizzes prior to the associated in-person sessions) was significantly higher when compared to a course where Level Up! was not implemented. Although engagement levels did drop over the course of each semester, they remained substantially higher for each week when compared to the previous semester or year. These results provide convincing evidence that introducing a gamification element to a course taught in flipped format increases students motivation and engagement to complete work outside of scheduled class time. When comparing engagement levels within students between two semesters, we found a general increase in engagement across both semesters but more interestingly found that some students with very low engagement levels in semester 1 went on the have some of the highest engagement levels in semester 2. For these students it seems that the gamification element drastically improved their motivation and engagement levels and is evidence that including a gamification element to a course may encourage engagement from students who would otherwise not engage. Our results also found that this increase in engagement could lead to an increase in performance with 82% of students receiving a grade of A, B or C in the semester 1 course of 2023-24 compared to 60% in 2022-23. Although we cannot say for sure what led to this increase in achievement and there are many factors which could influence this (such as cohort effect, different exam paper, etc.), an increase in engagement throughout a semester is compatible with an increase in performance since students are more likely to be distributing their learning over the course of a semester. This is especially beneficial for mathematical sciences courses where learning is often thought of as linear with each topic building on the previous one and poor understanding of early material can have a detrimental effect later in the course [Ireland and Mouthaan, 2020]. This study focuses on Year 1 undergraduate students who typically fall into a post-secondary education age range. The transition to university for students can be overwhelming, with changes in teaching delivery, workload and cognitive demands ([Blair, 2017]). Our results show that gamification can improve the learning experience, which may help with this transition, in line with observations by [Zaric et al., 2017]. This being said, [Kim and Castelli, 2021] looked at the effects of gamification across three age cohorts and found that university level participants found the least significant results in terms of positive interaction when compared to secondary school and adult learners, indicating that younger and older learners may have more interest in gamified factors. Currently, gamification is only present within the Year 1 modules of the undergraduate programme, and is not implemented within other modules on the programme. There is scope to explore the effects of gamification on a more mature cohort to observe if we see similar patterns of engagement. This study has considered student engagement levels in terms of completing pre-lecture quizzes, though it is important to note that we see meaningful engagement instead of cases where students are simply trying to ‘game’ the leaderboard. Our results are based solely on participation on formative reading quizzes where participation is identified through criteria being met, as this material was found to show that genuine participation took place. There are other activities in the course, such as textbook readings, which simply require a manual completion indicator and could be exposed to non-meaningful engagement. We feel that a reasonable performance on the quizzes indicates engagement with the other activities available (or indeed, of students independently finding their own resources for study). The results presented here are for a self-selected subset of students. However, course instructors were able to view results for the entire cohort of students to inform their future decision making on the learning and teaching approach for this course and others. The results presented here were found to be similar to the results for the entire cohort and the general conclusions made would not change if data on the entire cohort of students were considered. Although studies in gamification have reflected positive outcomes in terms of increased motivation and engagement in tasks, this teaching strategy has potential downsides. Effects such as increased competition between students ([Hakulinen et al., 2013]) and task evaluation difficulties, where certain tasks may not be suitable for a game based element or may have ways of being marked as complete without proper engagement in the learning materials ([Domínguez et al., 2013]) may become issues. The design of game based elements within a course must also be considered with a balance of reward and level of work and engagement required. Tasks which are overly complex can lead to a lack of engagement from learners ([Dong et al., 2012]). There is also potential for demotivation through the full gamification process, particulaly if students find themselves placed low on the points leaderboard. The competency levels of students may lead to negative consequences, including frustration and loss of self confidence ([Alomari et al., 2019]). A study by [Ding et al., 2018] showed that some students in a gamified learning activity may require more time to obtain the necessary understanding to progress through learning process. Engagement in gamification not only requires active participation from students, but time and attention from staff to implement the system effectively. From our experience using Level Up!, it takes time to set up the system. Several considerations have to be made at the set-up stage, including the allocation of experience points to certain tasks, and allocating points in such a way as to reflect the level of engagement required from the student. Levels were attainable for students which were split by weekly tasks, which requires manual intervention to tally the available points over a given week. For example, students could receive half points for completing certain tasks after a deadline which requires manual intervention from the course lecturer to reset the points awarded to a different total after each deadline. Though our study explored gamification using Level Up!, there are a host of other plug-ins which can be adapted in virtual learning environments such as Quizventure[4] and Block Game[5]. [Heilbrunn et al., 2017] provide a detailed review of tools for gamification analytics. Finally, we highlight the importance of using learning analytics, where possible, to access data from students on a course when measuring the success of a new teaching method or intervention. If the instructors of the two courses under consideration here had relied on feedback from students to inform teaching strategy - which was consistently positive even before the implementation of Level Up! - the true extent of the lack of engagement in the pre-lecture materials may never have been identified. This paper is available on arxiv under CC BY 4.0 DEED license. [4] https://github.com/xow/moodle-mod_quizgame [5] https://moodle.org/plugins/block_game Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Authors: Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction Course information Motivation and intervention Findings Discussion Conclusion and References Abstract and Introduction Abstract and Introduction Course information Course information Motivation and intervention Motivation and intervention Findings Findings Discussion Discussion Conclusion and References Conclusion and References 5 Discussion The purpose of this study was to investigate the effects of a gamification element in a mathematical sciences course taught in flipped format. The study compared levels of engagement with pre-lecture quizzes before and after the gamification intervention was introduced. Comparisons were made across semesters (different courses, same students) and across academic years (same course, different students). Our study found that in both cases the level of engagement (measured by average percentage of students completing pre-lecture quizzes prior to the associated in-person sessions) was significantly higher when compared to a course where Level Up! was not implemented. Although engagement levels did drop over the course of each semester, they remained substantially higher for each week when compared to the previous semester or year. These results provide convincing evidence that introducing a gamification element to a course taught in flipped format increases students motivation and engagement to complete work outside of scheduled class time. When comparing engagement levels within students between two semesters, we found a general increase in engagement across both semesters but more interestingly found that some students with very low engagement levels in semester 1 went on the have some of the highest engagement levels in semester 2. For these students it seems that the gamification element drastically improved their motivation and engagement levels and is evidence that including a gamification element to a course may encourage engagement from students who would otherwise not engage. Our results also found that this increase in engagement could lead to an increase in performance with 82% of students receiving a grade of A, B or C in the semester 1 course of 2023-24 compared to 60% in 2022-23. Although we cannot say for sure what led to this increase in achievement and there are many factors which could influence this (such as cohort effect, different exam paper, etc.), an increase in engagement throughout a semester is compatible with an increase in performance since students are more likely to be distributing their learning over the course of a semester. This is especially beneficial for mathematical sciences courses where learning is often thought of as linear with each topic building on the previous one and poor understanding of early material can have a detrimental effect later in the course [Ireland and Mouthaan, 2020]. This study focuses on Year 1 undergraduate students who typically fall into a post-secondary education age range. The transition to university for students can be overwhelming, with changes in teaching delivery, workload and cognitive demands ([Blair, 2017]). Our results show that gamification can improve the learning experience, which may help with this transition, in line with observations by [Zaric et al., 2017]. This being said, [Kim and Castelli, 2021] looked at the effects of gamification across three age cohorts and found that university level participants found the least significant results in terms of positive interaction when compared to secondary school and adult learners, indicating that younger and older learners may have more interest in gamified factors. Currently, gamification is only present within the Year 1 modules of the undergraduate programme, and is not implemented within other modules on the programme. There is scope to explore the effects of gamification on a more mature cohort to observe if we see similar patterns of engagement. This study has considered student engagement levels in terms of completing pre-lecture quizzes, though it is important to note that we see meaningful engagement instead of cases where students are simply trying to ‘game’ the leaderboard. Our results are based solely on participation on formative reading quizzes where participation is identified through criteria being met, as this material was found to show that genuine participation took place. There are other activities in the course, such as textbook readings, which simply require a manual completion indicator and could be exposed to non-meaningful engagement. We feel that a reasonable performance on the quizzes indicates engagement with the other activities available (or indeed, of students independently finding their own resources for study). The results presented here are for a self-selected subset of students. However, course instructors were able to view results for the entire cohort of students to inform their future decision making on the learning and teaching approach for this course and others. The results presented here were found to be similar to the results for the entire cohort and the general conclusions made would not change if data on the entire cohort of students were considered. Although studies in gamification have reflected positive outcomes in terms of increased motivation and engagement in tasks, this teaching strategy has potential downsides. Effects such as increased competition between students ([Hakulinen et al., 2013]) and task evaluation difficulties, where certain tasks may not be suitable for a game based element or may have ways of being marked as complete without proper engagement in the learning materials ([Domínguez et al., 2013]) may become issues. The design of game based elements within a course must also be considered with a balance of reward and level of work and engagement required. Tasks which are overly complex can lead to a lack of engagement from learners ([Dong et al., 2012]). There is also potential for demotivation through the full gamification process, particulaly if students find themselves placed low on the points leaderboard. The competency levels of students may lead to negative consequences, including frustration and loss of self confidence ([Alomari et al., 2019]). A study by [Ding et al., 2018] showed that some students in a gamified learning activity may require more time to obtain the necessary understanding to progress through learning process. Engagement in gamification not only requires active participation from students, but time and attention from staff to implement the system effectively. From our experience using Level Up!, it takes time to set up the system. Several considerations have to be made at the set-up stage, including the allocation of experience points to certain tasks, and allocating points in such a way as to reflect the level of engagement required from the student. Levels were attainable for students which were split by weekly tasks, which requires manual intervention to tally the available points over a given week. For example, students could receive half points for completing certain tasks after a deadline which requires manual intervention from the course lecturer to reset the points awarded to a different total after each deadline. Though our study explored gamification using Level Up!, there are a host of other plug-ins which can be adapted in virtual learning environments such as Quizventure[4] and Block Game[5]. [Heilbrunn et al., 2017] provide a detailed review of tools for gamification analytics. Finally, we highlight the importance of using learning analytics, where possible, to access data from students on a course when measuring the success of a new teaching method or intervention. If the instructors of the two courses under consideration here had relied on feedback from students to inform teaching strategy - which was consistently positive even before the implementation of Level Up! - the true extent of the lack of engagement in the pre-lecture materials may never have been identified. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [4] https://github.com/xow/moodle-mod_quizgame [5] https://moodle.org/plugins/block_game

Levelling Up Learning: Exploring the Impact of Gamification in Flipped Classrooms: Discussion

Authors:
(1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk);
(2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk);
(3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction
Course information
Motivation and intervention
Findings
Discussion
Conclusion and References 3 Motivation and intervention The pre-lecture activities were made available to students via the University of Glasgow’s virtual learning environment Moodle [2]. As such, it is straightforward for course instructors to check if students are engaging with these activities and crucially, given the flipped learning approach, whether they are engaging with them in advance of the in-person sessions. As discussed in Section 2.1, the University of Glasgow Learning Analytics Policy allows staff to undertake learning analytics activities which enable interventions to assist students learning. From the year in which the flipped learning approach was introduced (2020-21) staff on the course were therefore monitoring engagement in pre-lecture activities through activity logs on Moodle. For the purpose of this study, only engagement with the formative reading quizzes (pre-lecture quizzes) is measured, where activity completion can be indicated by certain conditions being met (e.g. whether a student has received a grade for a quiz), rather than students being able to manually mark completion (e.g. for link to reading from course textbook). This means that we cannot measure and compare engagement across all pre-lecture activities and we may be misrepresenting true engagement as some students may choose to read the textbook but not complete the reading quiz (or vice versa). However, this should give a more reliable estimate of meaningful engagement in activities since students have to engage somewhat with the activity (e.g. start and submit the quiz) rather than being able to manually mark that the activity is complete by checking a button. This does not fully guard against non-meaningful engagement (e.g. a student could begin the quiz, complete no questions, submit, and still be counted as having completed this activity) but performance in these activities (e.g. questions attempted and marks achieved) was also checked to understand the extent of meaningful engagement. Despite students indicating that they enjoyed the flipped learning approach through course evaluation forms, when looking at the data it was clear that engagement in the pre-lecture quizzes was extremely low. For example, the average completion of pre-lecture quizzes in advance of the active learning session taking place was 6.88% in semester 1 of 2022-23. This was of concern to course instructors given the in-class sessions involved spending less time presenting material and more time engaging in active learning activities as per the goal of flipped learning. This pattern was persistent over a number of years. Given that the introduction of the new courses coincided with the Covid-19 pandemic, it was also difficult to disentangle the lack of engagement with the flipped learning material, and the general lack of engagement HE institutions saw over the course of the pandemic ([Wester et al., 2021]). The academic year of 2022-23 saw a return to campus for the majority of teaching but the issue of low engagement in pre-lecture activities persisted. This is demonstrated in Figure 1 (a) which shows the cumulative mean percentage of students engaging in the pre-lecture quizzes for each week across an 11 week period in semester 1 of 2022-23. Each coloured line represents the cumulative average percentage of students who have engaged in the collection of quizzes associated with one week of teaching over the course of the semester (an average is shown here since each week may have a number of quizzes for students to complete). Of most interest is the level of engagement in the pre-lecture quizzes for the week in which the material is covered in class, which is represented by the first point for each weekly line in Figure 1 (a). These figures are also presented in column 1 of Table 1. This figure starts off low (around 27%) for week 1 and continues to drop for each week of material, before reaching a point around week 7 where less than 5% of students have engaged with the pre-lecture quizzes on the week in which the material is covered in class. Another interesting feature of Figure 1 (a) is the pattern of engagement for each collection of weekly quizzes over the semester. Although the engagement in the weekly material does increase slightly over the semester, at most only about half of the students in the class have ever engaged with the material at all on average for week 1, and after week 8 this drops to less than 25%. Interestingly, for all weeks an increase in average engagement can be seen between weeks 10 and 11 and we believe this to be related to exam preparation as the exam takes place immediately after the course finishes. These features are clearly of huge concern to the course instructors as they indicate that not only are students not engaging with the pre-lecture quizzes ‘on time’ to benefit from a flipped approach, the majority of students aren’t engaging with the materials at all across the entire duration of the course. In an attempt to tackle this low engagement the decision was made to add an element of gamification to the course which would encourage students to engage in the pre-lecture activities. This was implemented through the the Level Up! Moodle plug-in[3] which was introduced to the course in semester 2 of 2022-23. Level Up! allows students to gain points by participating in the course via Moodle. Points were awarded for the completion for a variety of course activities including the pre-lecture activities, tutorial questions, lab materials, and assessed quizzes and points were awarded based on completion only, i.e. the points system was endeavour-based and not merit-based. In an attempt to explicitly tackle engagement in the pre-lecture activities, full points were only awarded if the activity was completed before the associated lecture/tutorial/lab with half-points being awarded thereafter. Figure 2 shows how this plug-in is integrated within the course Moodle page with the left-hand image showing the Level Up! block that students can see on the main course page and the right-hand image showing the leaderboard view which shows students where they rank individually on an anonymous leaderboard throughout the semester. There is also an option to view where the students’ tutorial group rank in a team leaderboard. Level Up! was initially introduced in an attempt to increase engagement in pre-lecture activities in semester 2 of the 2022-23 academic year and, after initial success, introduced from semester 1 of the 2023-24 academic year. This allows us to make a number of comparisons which form our questions of interest: How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! between the same students across two courses.


How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! within the same course across two academic years. This paper is available on arxiv under CC BY 4.0 DEED license. [2] https://moodle.org/ [3] https://moodle.org/plugins/block_xp Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Authors: Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction Course information Motivation and intervention Findings Discussion Conclusion and References Abstract and Introduction Abstract and Introduction Course information Course information Motivation and intervention Motivation and intervention Findings Findings Discussion Discussion Conclusion and References Conclusion and References 3 Motivation and intervention The pre-lecture activities were made available to students via the University of Glasgow’s virtual learning environment Moodle [2]. As such, it is straightforward for course instructors to check if students are engaging with these activities and crucially, given the flipped learning approach, whether they are engaging with them in advance of the in-person sessions. As discussed in Section 2.1, the University of Glasgow Learning Analytics Policy allows staff to undertake learning analytics activities which enable interventions to assist students learning. From the year in which the flipped learning approach was introduced (2020-21) staff on the course were therefore monitoring engagement in pre-lecture activities through activity logs on Moodle. For the purpose of this study, only engagement with the formative reading quizzes (pre-lecture quizzes) is measured, where activity completion can be indicated by certain conditions being met (e.g. whether a student has received a grade for a quiz), rather than students being able to manually mark completion (e.g. for link to reading from course textbook). This means that we cannot measure and compare engagement across all pre-lecture activities and we may be misrepresenting true engagement as some students may choose to read the textbook but not complete the reading quiz (or vice versa). However, this should give a more reliable estimate of meaningful engagement in activities since students have to engage somewhat with the activity (e.g. start and submit the quiz) rather than being able to manually mark that the activity is complete by checking a button. This does not fully guard against non-meaningful engagement (e.g. a student could begin the quiz, complete no questions, submit, and still be counted as having completed this activity) but performance in these activities (e.g. questions attempted and marks achieved) was also checked to understand the extent of meaningful engagement. Despite students indicating that they enjoyed the flipped learning approach through course evaluation forms, when looking at the data it was clear that engagement in the pre-lecture quizzes was extremely low. For example, the average completion of pre-lecture quizzes in advance of the active learning session taking place was 6.88% in semester 1 of 2022-23. This was of concern to course instructors given the in-class sessions involved spending less time presenting material and more time engaging in active learning activities as per the goal of flipped learning. This pattern was persistent over a number of years. Given that the introduction of the new courses coincided with the Covid-19 pandemic, it was also difficult to disentangle the lack of engagement with the flipped learning material, and the general lack of engagement HE institutions saw over the course of the pandemic ([Wester et al., 2021]). The academic year of 2022-23 saw a return to campus for the majority of teaching but the issue of low engagement in pre-lecture activities persisted. This is demonstrated in Figure 1 (a) which shows the cumulative mean percentage of students engaging in the pre-lecture quizzes for each week across an 11 week period in semester 1 of 2022-23. Each coloured line represents the cumulative average percentage of students who have engaged in the collection of quizzes associated with one week of teaching over the course of the semester (an average is shown here since each week may have a number of quizzes for students to complete). Of most interest is the level of engagement in the pre-lecture quizzes for the week in which the material is covered in class, which is represented by the first point for each weekly line in Figure 1 (a). These figures are also presented in column 1 of Table 1. This figure starts off low (around 27%) for week 1 and continues to drop for each week of material, before reaching a point around week 7 where less than 5% of students have engaged with the pre-lecture quizzes on the week in which the material is covered in class. Another interesting feature of Figure 1 (a) is the pattern of engagement for each collection of weekly quizzes over the semester. Although the engagement in the weekly material does increase slightly over the semester, at most only about half of the students in the class have ever engaged with the material at all on average for week 1, and after week 8 this drops to less than 25%. Interestingly, for all weeks an increase in average engagement can be seen between weeks 10 and 11 and we believe this to be related to exam preparation as the exam takes place immediately after the course finishes. These features are clearly of huge concern to the course instructors as they indicate that not only are students not engaging with the pre-lecture quizzes ‘on time’ to benefit from a flipped approach, the majority of students aren’t engaging with the materials at all across the entire duration of the course. In an attempt to tackle this low engagement the decision was made to add an element of gamification to the course which would encourage students to engage in the pre-lecture activities. This was implemented through the the Level Up! Moodle plug-in[3] which was introduced to the course in semester 2 of 2022-23. Level Up! allows students to gain points by participating in the course via Moodle. Points were awarded for the completion for a variety of course activities including the pre-lecture activities, tutorial questions, lab materials, and assessed quizzes and points were awarded based on completion only, i.e. the points system was endeavour-based and not merit-based. In an attempt to explicitly tackle engagement in the pre-lecture activities, full points were only awarded if the activity was completed before the associated lecture/tutorial/lab with half-points being awarded thereafter. Figure 2 shows how this plug-in is integrated within the course Moodle page with the left-hand image showing the Level Up! block that students can see on the main course page and the right-hand image showing the leaderboard view which shows students where they rank individually on an anonymous leaderboard throughout the semester. There is also an option to view where the students’ tutorial group rank in a team leaderboard. Level Up! was initially introduced in an attempt to increase engagement in pre-lecture activities in semester 2 of the 2022-23 academic year and, after initial success, introduced from semester 1 of the 2023-24 academic year. This allows us to make a number of comparisons which form our questions of interest: How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! between the same students across two courses. How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! within the same course across two academic years. How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! between the same students across two courses. How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! between the same students across two courses. How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! within the same course across two academic years. How do engagement levels in pre-lecture quizzes compare after the implementation of Level Up! within the same course across two academic years. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [2] https://moodle.org/ [3] https://moodle.org/plugins/block_xp

Exploring the Impact of Gamification in Flipped Classrooms: Motivation and intervention

Authors:
(1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk);
(2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk);
(3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction
Course information
Motivation and intervention
Findings
Discussion
Conclusion and References 2 Course information At the University of Glasgow, the School of Mathematics and Statistics offer courses in introductory Statistics to undergraduate students across the University. These level 1 courses are typically taken in year 1 or 2 (pre-honours) of a student’s university career, which typically lasts 4 years. In year 1, single honours students will take courses in three subjects, one of which will be the subject they have chosen to specialise in. Any of these three courses can be considered in year 2, and students have the option to switch their chosen degree programme to any of these subjects conditional on meeting the requirements for progression. The purpose of the level 1 statistics courses is predominately to prepare students who are interested in studying statistics at higher levels and, given that most students will have little experience of statistics when they arrive at university, introduce students to the subject with the hope that students will choose to continue studying statistics in future years. There are two level 1 courses taught over two semesters (semester 1: Sep-Dec and semester 2: Jan-Mar) with final exams taking place at the end of each semester. Given the growing awareness of the importance of statistics across many disciplines, the courses have become increasingly popular over the past few years with 200+ students enrolled. To align with the University of Glasgow Learning and Teaching strategy, the courses were re-designed for 2020-21 with the aim of evolving the approach to learning and teaching towards student-centered, active learning. The approach taken for these courses was to introduce a flipped learning model, with a variety of online learning materials for students to complete before live teaching sessions. In-person sessions were then used to consolidate learning through a variety of interactive activities for students to complete individually or in small groups. For the purpose of this study we will call these online learning materials, ‘pre-lecture activities’. 2.1 Data The data for this study are taken from Moodle activity logs. These can be used to view all student interactions with Moodle, including engagement with the activities of interest for the courses under consideration here. Data such as these are becoming increasingly available and the use of data in the support of student learning is commonly referred to as learning analytics ([Leitner et al., 2017]). At this institution, staff may undertake learning analytics activities which enable interventions to assist students through the analysis of their own data. However, when such projects are intended for external dissemination they also require ethical approval. This study falls into the latter category and ethical approval was sought and granted in advance of this study taking place (application number 300220006). The research ethics associated with this study required students to consent to their data being used as part of this project. Consent was requested during live teaching sessions and was granted for n = 100 (out of a total of 257) students in 2022-23 and n = 77 (out of a total of 280) students in 2023-24. Unfortunately we were unable to obtain retrospective ethical approval to access data from 2020-21 (when flipped learning was introduced) and 2021-22 due to the opt-in nature of the study. We can therefore only provide results and comparisons for engagement using the 2022-23 and 2023-24 cohorts. Students attended three in-person sessions a week, and before each of these were asked to complete the following pre-lecture activities: • Textbook readings - students were asked to read a section of the course textbook in advance of the associated in-person session. The purpose of the readings was to introduce students to the course material. Completion of these can only be tracked through students manually marking completion via activity tracking on Moodle. • Formative reading quizzes - students were asked to complete a short quiz associated with after reading the corresponding textbook section. The purpose of the quizzes was for students to consolidate their learning and test their understanding of the course material in advance of the corresponding in-person session. Information on attempts for all quizzes is automatically stored in Moodle. It is therefore possible to determine which students have completed a quiz, when they started/finished the quiz, and their performance on the quiz. We can therefore track engagement in the quizzes and compare this with different cohorts as well as monitoring performance to inform the active learning sessions. These will be referred to as pre-lecture quizzes. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Authors: Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction Course information Motivation and intervention Findings Discussion Conclusion and References Abstract and Introduction Abstract and Introduction Course information Course information Motivation and intervention Motivation and intervention Findings Findings Discussion Discussion Conclusion and References Conclusion and References 2 Course information At the University of Glasgow, the School of Mathematics and Statistics offer courses in introductory Statistics to undergraduate students across the University. These level 1 courses are typically taken in year 1 or 2 (pre-honours) of a student’s university career, which typically lasts 4 years. In year 1, single honours students will take courses in three subjects, one of which will be the subject they have chosen to specialise in. Any of these three courses can be considered in year 2, and students have the option to switch their chosen degree programme to any of these subjects conditional on meeting the requirements for progression. The purpose of the level 1 statistics courses is predominately to prepare students who are interested in studying statistics at higher levels and, given that most students will have little experience of statistics when they arrive at university, introduce students to the subject with the hope that students will choose to continue studying statistics in future years. There are two level 1 courses taught over two semesters (semester 1: Sep-Dec and semester 2: Jan-Mar) with final exams taking place at the end of each semester. Given the growing awareness of the importance of statistics across many disciplines, the courses have become increasingly popular over the past few years with 200+ students enrolled. To align with the University of Glasgow Learning and Teaching strategy, the courses were re-designed for 2020-21 with the aim of evolving the approach to learning and teaching towards student-centered, active learning. The approach taken for these courses was to introduce a flipped learning model, with a variety of online learning materials for students to complete before live teaching sessions. In-person sessions were then used to consolidate learning through a variety of interactive activities for students to complete individually or in small groups. For the purpose of this study we will call these online learning materials, ‘pre-lecture activities’. 2.1 Data The data for this study are taken from Moodle activity logs. These can be used to view all student interactions with Moodle, including engagement with the activities of interest for the courses under consideration here. Data such as these are becoming increasingly available and the use of data in the support of student learning is commonly referred to as learning analytics ([Leitner et al., 2017]). At this institution, staff may undertake learning analytics activities which enable interventions to assist students through the analysis of their own data. However, when such projects are intended for external dissemination they also require ethical approval. This study falls into the latter category and ethical approval was sought and granted in advance of this study taking place (application number 300220006). The research ethics associated with this study required students to consent to their data being used as part of this project. Consent was requested during live teaching sessions and was granted for n = 100 (out of a total of 257) students in 2022-23 and n = 77 (out of a total of 280) students in 2023-24. Unfortunately we were unable to obtain retrospective ethical approval to access data from 2020-21 (when flipped learning was introduced) and 2021-22 due to the opt-in nature of the study. We can therefore only provide results and comparisons for engagement using the 2022-23 and 2023-24 cohorts. Students attended three in-person sessions a week, and before each of these were asked to complete the following pre-lecture activities: • Textbook readings - students were asked to read a section of the course textbook in advance of the associated in-person session. The purpose of the readings was to introduce students to the course material. Completion of these can only be tracked through students manually marking completion via activity tracking on Moodle. • Textbook readings • Formative reading quizzes - students were asked to complete a short quiz associated with after reading the corresponding textbook section. The purpose of the quizzes was for students to consolidate their learning and test their understanding of the course material in advance of the corresponding in-person session. Information on attempts for all quizzes is automatically stored in Moodle. It is therefore possible to determine which students have completed a quiz, when they started/finished the quiz, and their performance on the quiz. We can therefore track engagement in the quizzes and compare this with different cohorts as well as monitoring performance to inform the active learning sessions. These will be referred to as pre-lecture quizzes. • Formative reading quizzes This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Levelling Up Learning:Exploring the Impact of Gamification in Flipped Classrooms: Course information

The field of education faces daunting challenges, with alarming statistics to underscore its struggles. Since the onset of the pandemic, approximately half and a staggering 93% of educators report . Moreover, there has been a 33% decline in teaching credentials over the past decade, signaling a need for innovation and more comprehensive support. In the midst of these challenges, the demand for resources to assist educators and school mental health professionals in aiding students has grown significantly. a million teachers have left the profession wrestling with burnout The modern educational landscape has undergone a profound transformation with the integration of digital technologies. This transformation, although in progress before the pandemic, has gained momentum due to recent challenges, revealing both the potential and pitfalls of educational technology (EdTech). While Learning Management Systems (LMS) like Canvas, Moodle, and Blackboard have become prevalent, facilitating coursework organization, homework assignments, and student progress tracking, there's a noticeable gap in comparable advancements for school-based social, emotional, and mental health support. Educators enter the profession driven by a desire to make a difference, but when they encounter challenges without clear solutions, they often feel overwhelmed, contributing to the burnout epidemic. has emerged as a promising resource. This AI-powered platform aims to connect educators and mental health professionals with content specifically designed to promote student well-being. Acknowledging the mounting pressures these professionals face, SageSpace strives to provide valuable resources to those navigating the ever-evolving educational landscape. SageSpace EdTech has predominantly focused on whole-class social-emotional learning, but there's an increasing need to address students' holistic well-being, encompassing mental health, emotional resilience, and behavioral management. SageSpace recognizes this need and offers tailored content and resources for professionals to address these crucial aspects. For mental health professionals, the search for relevant research and content can be a time-consuming and daunting task. The lack of clarity about trusted sources compounds this challenge. SageSpace's initial research highlights that 70% of educators turn to Google search and Chat GPT, often yielding unvetted and potentially inaccurate results. This presents a significant concern when educators rely on such sources to support students in crisis. SageSpace aims to simplify this process by providing access to well-vetted, research-supported mental health resources. Educators grappling with diverse student behaviors can turn to SageSpace for resources that can be immediately applied in behavioral interventions. SageSpace employs a business-to-business (B2B) and business-to-consumer (B2C) software as a service (SaaS) model. This strategy aims to engage educational institutions directly while also serving individual professionals. Jennifer Athanacio, a veteran with 14 years of experience in education and school-based mental health, brings invaluable insights to SageSpace. Her background includes active involvement in mental health strategies and crisis intervention at the Fremont Unified School District. These experiences have profoundly influenced the development of SageSpace. Complementing Athanacio's vision is co-founder Chris Swanson, who serves as Chief Technology Officer (CTO). With decades of diverse experience in the technology industry, ranging from startups to Fortune 500 companies, Chris has held roles spanning hands-on engineering to director and executive positions. His previous involvement in projects related to health and well-being equips him to leverage his expertise in realizing SageSpace's mission. The ever-evolving educational landscape presents an array of challenges for educators and mental health professionals, including the convenience of quick Google or Chat GPT searches, but with the inherent risk of unverified information. Platforms like SageSpace reflect the tech industry's commitment to providing solutions that address the current gaps in resources and support. As the education sector continues its transformation, platforms like SageSpace will play a pivotal role in ensuring that professionals are equipped to meet students' comprehensive needs.

Addressing the Challenges in Education: SageSpace's Quest to Support Struggling Educators and Mental

Data privacy breaches have dominated the news over the past year. From Twitter’s (now X) data leaks to Zoom’s AI privacy entanglement, these high-profile incidents have underscored the urgent need for organizations to prioritize data protection and cultivate a strong sense of vigilance. These cautionary tales remind us that the consequences of data privacy neglect can be severe, not only in terms of financial losses and reputational damage but also in legal repercussions. To safeguard customer trust and ensure regulatory compliance, organizations must build a robust data privacy culture. In this article, we will explore 7 key strategies to foster a data privacy culture within your organization, along with some pro-tips and recommended tools to help you on your journey. 1. Set the Tone from the Leaders The foundation of any successful data privacy culture begins with leadership commitment. Executives and senior management must demonstrate a genuine commitment to data protection. This involves not only advocating for privacy but also integrating it into the company's values and mission. Here's a pro-tip: Delegate a Chief Privacy Officer (CPO) or Data Protection Officer (DPO) to take charge. This designated individual should report directly to top management and have the authority to enforce privacy policies. Use privacy management platforms like OneTrust to help your CPO manage compliance, assess risks, and streamline privacy-related tasks. 2. Employee Training and Awareness While there’s the CPO or DPO to pave the way, data privacy is still everyone's responsibility. To build a data privacy culture, it's crucial to educate and raise awareness among employees about the significance of safeguarding sensitive information. Conduct regular training sessions to ensure that all employees understand the principles and practices of data privacy. Here's a pro-tip: Develop an interactive e-learning module to allow you to create engaging and customized privacy training courses, equipping your entire workforce with the fundamentals of honing a privacy culture. Use Learning Management Systems (LMS) like Moodle to manage and deliver privacy training efficiently. 3. Embrace AI and Automation Harnessing the power of artificial intelligence (AI) and automation tools can significantly enhance your organization's data privacy efforts. AI can help in identifying potential threats and vulnerabilities, while automation can streamline privacy-related tasks and ensure consistent compliance. Here’s a pro-tip: Invest in automation-driven privacy, security, and compliance platforms and let it do the heavy lifting so you no longer have to. Tools like MineOS are an excellent addition to your tech stack as they offer an easy, no-code yet intelligent procedure for data mapping and privacy requests, to name a few, without the hundreds of hours of manual work that other solutions require. 4. Clear Privacy Policies and Procedures Having well-defined privacy policies and procedures is essential. These documents should be accessible and easy to understand. What’s more, it should reiterate how data is collected, processed, stored, then deleted. Here's a pro-tip: Use privacy policy generators such as Termly to create comprehensive and legally compliant privacy policies for your organization. What’s more, you can also implement Document Management Systems like SharePoint or Google Workspace to organize and distribute privacy policies and procedures effectively. 5. Data Minimization and Access Control Data privacy culture thrives when organizations adopt a "need-to-know" approach. Only collect and retain the data necessary for your operations, and limit access to authorized personnel. Implement robust access controls and regularly review and update permissions to ensure that sensitive data is accessible only to those who require it. Here's a pro-tip: Use data classification tools like Varonis to automatically identify and categorize sensitive data within your organization. This makes it easier to implement access controls. You can also employ Identity and Access Management (IAM) solutions such as Okta or Azure Active Directory to manage user access efficiently. 6. Incident Response and Transparency Here’s the tough reality: no organization is immune to data breaches or privacy incidents. However, how an organization responds to such incidents can greatly impact its reputation and customer trust. A culture of data privacy should include well-defined incident response procedures that are communicated to all employees. Here's a pro-tip: Conduct tabletop exercises for data breach scenarios and help your team practice effective incident response. Consider incident management and response platforms like CyberSponse to streamline your response efforts and maintain transparency throughout the process. 7. Regular Auditing and Compliance Monitoring To ensure that your data privacy practices are effective and compliant with regulations, it's essential to conduct regular audits and monitoring. Audits help identify weaknesses and areas that require improvement, while ongoing compliance monitoring ensures that your organization adapts to changing regulations Here's a pro-tip: Use compliance management software to reduce the administrative burden on your team. Data loss prevention (DLP) solutions such as Broadcom’s Symantec DLP can help you monitor data flows and protect sensitive information from accidental exposure. In a Nutshell Building a data privacy culture is not a one-time task but an ongoing commitment that requires dedication, resources, and the right tools. By following these 7 strategies and utilizing recommended tools, your organization can create an environment where data privacy is not just a policy but a way of life. This culture not only protects your organization from costly breaches and regulatory penalties but also builds trust with your customers and partners. Remember that data privacy is not just a legal requirement but a moral and ethical imperative. Embrace it as part of your organizational DNA, and your data privacy culture will become a competitive advantage in an increasingly data-conscious world.

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