Related Work

Table of Links

Abstract and 1 Introduction

2 Related Work

3 A Virtual Learning Experience

3.1 The Team and 3.2 Course Overview

3.3 Pilot 1

3.4 Pilot 2

4 Feedback

4.1 Relentless Feedback

4.2 Detailed Student Feedback

5 Lessons Learned

6 Summary and Future Work, Acknowledgements, and References

A. Appendix: Three Stars and a Wish

2 Related Work

There are two aspects that we consider of importance in relation to this work, the course content, Natural Language Processing (NLP), and the environment, teaching online during a pandemic. In this section we explore both topics.

NLP educators choose which aspects to teach based on multiple constraints such as class length, student experience, recent advancements, program focus, and even personal interest.

Our TDM course is fundamentally designed to be cross-disciplinary as we are teaching NLP and coding to students from multiple schools and backgrounds including linguistics, social sciences and business. Jurgens and Li (2018) point out that NLP courses are designed to reflect, amongst other things, the background and experience of the students. Agarwal (2013) explains that in courses such as these the majority of students, who he calls “newbies in Computer Science”, have never programmed before. He highlights that we can increase experience through homework tasks which we did both before the course and in-between each session. Hearst (2005) states that in these circumstances it is not important to place too much emphasis on the theoretical underpinnings of NLP but to focus on providing instructions for students on what is possible and how they can use it on their own in the future. We based our approach on using the NLTK[2] and spaCy[3] Python libraries as well as used examples inspired by Bird et al. (2009, 2005). We aim to explain how text analysis works step-bystep using clear and simple examples. We thereby aspire to develop and broaden humanities and social science students’ data-driven training and give them an understanding of how things work inside the box, something for which there is still a significant need in their core disciplines (McGillivray et al., 2020).

Teaching text analysis to non-computer scientists has been explored in texts such as Hovy (2020). For our course we had to consider the variety of backgrounds and experiences that this would encompass and needed to use a pre-course learning task and office hours to provide a more level knowledge starting point. We also had to design the course to keep more advanced students engaged while not intimidating learners who may find it more challenging. We used core material to explain principle concepts (such as tokens, tokenisation, and partof-speech (POS) tagging etc.) but with a hands-on approach. We avoided too much technical detail and put the material in the context of projects we have worked on ourselves to demonstrate how each analysis step becomes useful in practice.

As we taught our TDM course online in the context of a worldwide pandemic, we also report on related work in the area of online teaching, and with respect to the challenges in which we are teaching. Massive open online courses (MOOC) generally focus on providing online access to learning resources to a large number and wide range of participants. This has led to a desire to automate teaching and innovate digital interaction techniques in order to engage with large numbers of students. Whilst our intention was to teach a limited number of students, we hoped to use and draw upon innovation in this area in order to improve the experience for our students. E-learning and technology should not be seen as an attempt to replace or automate human teaching, although this can often be a fear articulated by teachers. In a discussion of automation within teaching Bayne (2015) argues that we can design online teaching and still place human communication at the centre with technology enhancing the learning of the student. Bayne suggests that the human teacher, the student and the technology can be intertwined. We asked students to engage with digital objects and the technology to enhance their learning journey. As teachers we do not merely support the digital learner but we remain at the centre of teaching the course.

Fawns et al. (2019) point out that online learning is a key growth area in higher education, which is even more true since the pandemic started, but that it is harder to form relationships in online courses. Therefore, we saw it as important to develop online dialogue between students in order to form communities which can improve these relationships. Building a community online can be harder but it is possible. We tried to achieve this through using a combination of traditional learning such as lectures and task-based learning such as pair programming exercises. Online learning tends to be interrupted as we are in our homes or elsewhere and have responsibilities that can take us away from the online space, bandwidth issues, dropping children at school, flatmates interrupting, phone calls, even the door bell ringing. Our teaching practices needed to be accepting of and adapted to this context.

Ross et al. (2013) discuss the issues of presence and distance in online learning. Interruptions in students’ concentration are a common event when learning online and we must use resilience strategies to maintain a ‘nearness’ to our students. This includes recognising that these events are normal and that engaging is an effort, identifying affinities and creating a socialness, valuing that distraction can change our perspective and this is helpful and designing openings, events that allow and encourage student to come together and engage. Whilst designing the course we kept these ideas in focus in order to allow us to develop and enhance our online relationships and our students’ learning.

[2] https://www.nltk.org

[3] https://spacy.io