Authors:
(1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu);
(2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Table of Links Abstract and I. Introduction II. Overview of PING System and Gridlock III. Design Insights and Educational Impact IV. Conclusion and References Abstract This work-in-progress research-to-practice paper provides ongoing results from the development and testing of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper offer an accessible solution. Furthermore, by combining such a system with a serious game, students are further engaged in interacting with the system. The proposed learning system combines expert-driven structure and lesson planning with computational intelligence methods and gamification to provide students with a fun and educational experience. As the project is ongoing from past years, numerous design iterations have been made on the system based on feedback from students and classroom observations. Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images. For our evaluation, we focus on student data gathered from in-classroom testing in relevant courses, with both educational efficacy results and student observations. To demonstrate the effect of our proposed system, students in an early electrical engineering course were instructed to interact with the system in place of their standard lab assignments. The system would then measure and help them improve their background knowledge before allowing them to complete the lab assignment. As they played through the game, we observed their interactions with the system to gather insights for future developments, which are presented in this work. Additionally, we demonstrate the system’s educational efficacy through early prepost-test results from students who played the game with and without the personalized learning system integration. Keywords—Gamification, Educational Software, Learning Technology, Higher Education, Engineering Education I. INTRODUCTION Despite many recent advancements and paradigm shifts worldwide, classroom education still plays a pivotal role in equipping students with the skills and knowledge required to join the workforce and address real problems. However, larger classrooms can struggle to address student needs and concerns, especially when those needs heavily deviate from the standard one-size-fits-all lesson plan [1, 2]. And as classroom sizes increase, it becomes impossible for instructors to provide support to each individual student based on their needs and background [3]. As such, students often need to resort to excessive studying, self-learning, or external tutoring to gain much-needed knowledge [4]. To take some burden off the instructor while still limiting learning to class time, one possible solution is intelligent tutoring systems (ITSs). ITSs are educational systems that leverage student data to provide personalized educational support and tutoring to students [5]. Unlike human tutors, these systems are not limited by time and resource constraints and can support many students at the same time. By collecting data such as student performance, student actions when interacting with the system, or results on graded assignments, these systems can make informed decisions about what support a student needs to succeed. Furthermore, recent trends in artificial intelligence (AI) and data mining have further bolstered the accuracy and ability of these systems to provide effective support. However, while these systems can provide appropriate support, they can often struggle with engaging students. This is especially prevalent when students are interacting with systems that require them to read large sections of content, watch extended videos, or otherwise undergo non-active learning [6]. Maintaining this engagement is crucial for effective learning. [7, 8]. The solution to the issue of student engagement, as addressed in this paper, is to combine an ITS with a serious game (SG) [9]. Serious games are virtual or physical games that focus primarily on education, training, or other non-entertainment purposes [10]. Due to the gamification aspect of SGs, students are often significantly more engaged in lessons. In a well-designed game, students may not even realize that they are learning. Additionally, virtual SGs have added benefits in that they can create environments, visualizations, or interactive scenarios that students would not otherwise experience through standard lectures or videos. This paper presents results from the ongoing development of Gridlock, a serious game that focuses on binary logic, simple programming, and digital system design. Gridlock is designed to be run in tandem with a lab assignment where students are tasked with designing the logic controller for a traffic light. This task is the goal of the game, and is a common lab assignment for early students in electrical engineering, computer engineering, and computer science. Gridlock further supports student learning by combining a serious game environment with an ITS that supports students on various topics relevant to the overarching task. Using reinforcement learning, an AI method, the system automatically adjusts what support it provides based on student responses. This support then manifests as pop-up prompts, hints, adjusted content, and enabling or disabling certain areas in the game based on student performance. We refer to the combined system as the personalized instruction and need-aware gaming (PING) system, and while this paper focuses on Gridlock, the system is designed as a modular, general-purpose approach for any SG. As the contribution of this work, we provide design insights for both our ITS and our virtual game environment based on both observations of student interactions with the system and interview sessions with students where they provided direct feedback. We also provide educational results from inclassroom testing to verify the educational efficacy of our proposed system. To that end, Section II of this paper gives a more detailed overview of our ITS, Gridlock, and the support provided in the game. Section III provides our development insights and educational results, followed by conclusions in Section IV. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Authors: Authors: (1) Ying Tang, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (tang@rowan.edu); Dept. of Electrical and Computer Engineering, Rowan University, (2) Ryan Hare, Dept. of Electrical and Computer Engineering, Rowan University, Glassboro, NJ, USA (harer6@rowan.edu). Dept. of Electrical and Computer Engineering, Rowan University, Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Overview of PING System and Gridlock II. Overview of PING System and Gridlock III. Design Insights and Educational Impact III. Design Insights and Educational Impact IV. Conclusion and References IV. Conclusion and References Abstract This work-in-progress research-to-practice paper provides ongoing results from the development and testing of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper offer an accessible solution. Furthermore, by combining such a system with a serious game, students are further engaged in interacting with the system. The proposed learning system combines expert-driven structure and lesson planning with computational intelligence methods and gamification to provide students with a fun and educational experience. As the project is ongoing from past years, numerous design iterations have been made on the system based on feedback from students and classroom observations. Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images. For our evaluation, we focus on student data gathered from in-classroom testing in relevant courses, with both educational efficacy results and student observations. This work-in-progress research-to-practice paper provides ongoing results from the development and testing of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper offer an accessible solution. Furthermore, by combining such a system with a serious game, students are further engaged in interacting with the system. The proposed learning system combines expert-driven structure and lesson planning with computational intelligence methods and gamification to provide students with a fun and educational experience. As the project is ongoing from past years, numerous design iterations have been made on the system based on feedback from students and classroom observations. Using computational intelligence, the system adaptively provides support to students based on data collected from both their in-game actions and by estimating their emotional state from webcam images. For our evaluation, we focus on student data gathered from in-classroom testing in relevant courses, with both educational efficacy results and student observations. To demonstrate the effect of our proposed system, students in an early electrical engineering course were instructed to interact with the system in place of their standard lab assignments. The system would then measure and help them improve their background knowledge before allowing them to complete the lab assignment. As they played through the game, we observed their interactions with the system to gather insights for future developments, which are presented in this work. Additionally, we demonstrate the system’s educational efficacy through early prepost-test results from students who played the game with and without the personalized learning system integration. To demonstrate the effect of our proposed system, students in an early electrical engineering course were instructed to interact with the system in place of their standard lab assignments. The system would then measure and help them improve their background knowledge before allowing them to complete the lab assignment. As they played through the game, we observed their interactions with the system to gather insights for future developments, which are presented in this work. Additionally, we demonstrate the system’s educational efficacy through early prepost-test results from students who played the game with and without the personalized learning system integration. Keywords—Gamification, Educational Software, Learning Technology, Higher Education, Engineering Education Keywords—Gamification, Educational Software, Learning Technology, Higher Education, Engineering Education I. INTRODUCTION Despite many recent advancements and paradigm shifts worldwide, classroom education still plays a pivotal role in equipping students with the skills and knowledge required to join the workforce and address real problems. However, larger classrooms can struggle to address student needs and concerns, especially when those needs heavily deviate from the standard one-size-fits-all lesson plan [1, 2]. And as classroom sizes increase, it becomes impossible for instructors to provide support to each individual student based on their needs and background [3]. As such, students often need to resort to excessive studying, self-learning, or external tutoring to gain much-needed knowledge [4]. To take some burden off the instructor while still limiting learning to class time, one possible solution is intelligent tutoring systems (ITSs). ITSs are educational systems that leverage student data to provide personalized educational support and tutoring to students [5]. Unlike human tutors, these systems are not limited by time and resource constraints and can support many students at the same time. By collecting data such as student performance, student actions when interacting with the system, or results on graded assignments, these systems can make informed decisions about what support a student needs to succeed. Furthermore, recent trends in artificial intelligence (AI) and data mining have further bolstered the accuracy and ability of these systems to provide effective support. However, while these systems can provide appropriate support, they can often struggle with engaging students. This is especially prevalent when students are interacting with systems that require them to read large sections of content, watch extended videos, or otherwise undergo non-active learning [6]. Maintaining this engagement is crucial for effective learning. [7, 8]. The solution to the issue of student engagement, as addressed in this paper, is to combine an ITS with a serious game (SG) [9]. Serious games are virtual or physical games that focus primarily on education, training, or other non-entertainment purposes [10]. Due to the gamification aspect of SGs, students are often significantly more engaged in lessons. In a well-designed game, students may not even realize that they are learning. Additionally, virtual SGs have added benefits in that they can create environments, visualizations, or interactive scenarios that students would not otherwise experience through standard lectures or videos. This paper presents results from the ongoing development of Gridlock , a serious game that focuses on binary logic, simple programming, and digital system design. Gridlock is designed to be run in tandem with a lab assignment where students are tasked with designing the logic controller for a traffic light. This task is the goal of the game, and is a common lab assignment for early students in electrical engineering, computer engineering, and computer science. Gridlock further supports student learning by combining a serious game environment with an ITS that supports students on various topics relevant to the overarching task. Using reinforcement learning, an AI method, the system automatically adjusts what support it provides based on student responses. This support then manifests as pop-up prompts, hints, adjusted content, and enabling or disabling certain areas in the game based on student performance. We refer to the combined system as the personalized instruction and need-aware gaming (PING) system, and while this paper focuses on Gridlock, the system is designed as a modular, general-purpose approach for any SG. Gridlock Gridlock Gridlock As the contribution of this work, we provide design insights for both our ITS and our virtual game environment based on both observations of student interactions with the system and interview sessions with students where they provided direct feedback. We also provide educational results from inclassroom testing to verify the educational efficacy of our proposed system. To that end, Section II of this paper gives a more detailed overview of our ITS, Gridlock , and the support provided in the game. Section III provides our development insights and educational results, followed by conclusions in Section IV. Gridlock 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

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Enhancing Learning with Gamified Instruction: Abstract and Introduction

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