How COVID-19 Affected Computer Science MOOC Learner Behavior and Achievements: A Demographic Study

Roy, Anindya; Yee, Michael; Perdue, Meghan; Stein, Julius; Bell, Ana; Carter, Ronisha; Miyagawa, Shigeru

doi:10.1145/3491140.3528328

Cited by 6 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the results of the study in [33] indicate that the pandemic negatively affected the students' interactions with eBooks and the amount of studying in an introductory computer science course. In addition, the certification rate of students of online courses on Python fell short during the pandemic [34]. Moreover, the same study revealed that the learners' behavior changed concerning their interactions with one another on the course discussion forum.…”

Section: Related Workmentioning

confidence: 91%

A Fuzzy-Based Evaluation of E-Learning Acceptance and Effectiveness by Computer Science Students in Greece in the Period of COVID-19

2023

View full text Add to dashboard Cite

In this paper, a fuzzy-based evaluation method is presented for the impact of e-learning on several aspects of the learning lives of academic students of Information and Communication Technologies (ICT) during the COVID-19 pandemic. Specifically, the academic year 2020–2021 was considered when a general lockdown was imposed in Greece and all courses were delivered exclusively through the web. The evaluation criteria are (i) e-learning acceptance, (ii) learning effectiveness, (iii) students’ engagement and (iv) socializing and interpersonal relationships in the educational community. The evaluation was conducted through questionnaires. Ninety two (92) undergraduate and postgraduate students and Ten (10) computer science in-structors of the department of Informatics of the University of Piraeus, Greece, participated in this survey. The questionnaire answers were analyzed using a fuzzy-based mechanism. Particularly, five fuzzy sets were used to describe the 5-point Likert scale answers to the questionnaires with linguistic values, and four other fuzzy sets were used for the description of the evaluation results concerning the four defined evaluation criteria. Moreover, 209 fuzzy rules were created to analyze and calculate the evaluation results per criterion, considering both the students’ answers (119 fuzzy rules) and the instructors’ answers (90 fuzzy rules) to the questionnaires. The gain of this approach is that the analysis of evaluation data with fuzzy rules imitates the human way of thinking and, thus, makes the process more explainable. The evaluation results showed a positive impact of e-learning on student confidence, self-discipline and active participation and a negative impact on student interpersonal relationships. The impact of e-learning on other learning issues was neutral.

show abstract

Section: Related Workmentioning

confidence: 91%

A Fuzzy-Based Evaluation of E-Learning Acceptance and Effectiveness by Computer Science Students in Greece in the Period of COVID-19

2023

View full text Add to dashboard Cite

show abstract

“…In this work, we use learners' verification and certification status, as well as when a particular course ran (i.e., before or during the pandemic) where relevant. For more information on the demographic details of the learners, we refer our readers to the authors' previous work in Roy et al (2022) and Yee et al (2022).…”

Section: Mooc Datasetmentioning

confidence: 99%

AI-assisted analysis of content, structure, and sentiment in MOOC discussion forums

Yee,

Roy,

Perdue

et al. 2023

Front. Educ.

Self Cite

View full text Add to dashboard Cite

Discussion forums are a key component of online learning platforms, allowing learners to ask for help, provide help to others, and connect with others in the learning community. Analyzing patterns of forum usage and their association with course outcomes can provide valuable insight into how learners actually use discussion forums, and suggest strategies for shaping forum dynamics to improve learner experiences and outcomes. However, the fine-grained coding of forum posts required for this kind of analysis is a manually intensive process that can be challenging for large datasets, e.g., those that result from popular MOOCs. To address this issue, we propose an AI-assisted labeling process that uses advanced natural language processing techniques to train machine learning models capable of labeling a large dataset while minimizing human annotation effort. We fine-tune pretrained transformer-based deep learning models on category, structure, and emotion classification tasks. The transformer-based models outperform a more traditional baseline that uses support vector machines and a bag-of-words input representation. The transformer-based models also perform better when we augment the input features for an individual post with additional context from the post's thread (e.g., the thread title). We validate model quality through a combination of internal performance metrics, human auditing, and common-sense checks. For our Python MOOC dataset, we find that annotating approximately 1% of the forum posts achieves performance levels that are reliable for downstream analysis. Using labels from the validated AI models, we investigate the association of learner and course attributes with thread resolution and various forms of forum participation. We find significant differences in how learners of different age groups, gender, and course outcome status ask for help, provide help, and make posts with emotional (positive or negative) sentiment.

show abstract

“…I N recent years, many students worldwide are progressively considering Massive Open Online Courses (MOOCs) as learning platforms, which help them to fill the gaps or acquire new skills required by the industry [1]. During the COVID-19 pandemic, the interest in such courses has seen exponential growth due to people in a great part of countries worldwide going into long quarantine lockdown [2]. However, a great percentage of students who enroll in a MOOC fail to complete the course [3].…”

Section: Introductionmentioning

confidence: 99%

A Time-Aware Approach for MOOC Dropout Prediction Based on Rule Induction and Sequential Three-Way Decisions

Blundo,

Loia,

Orciuoli

2023

IEEE Access

View full text Add to dashboard Cite

Nowadays, Massive Open Online Courses (MOOCs) are adopted by students worldwide. One of the main critical issues often associated to MOOCs is the dropout phenomenon. In other words, the percentage of students abandoning a MOOC-based study path is considered still too high. Therefore, an increasing number of scientific works, coming from several and heterogeneous communities (e.g., computer science, data science, statistics, education) proposes approaches trying to mitigate such a problem. The majority of the aforementioned works focuses on machine learning methods to define classifiers able to be trained and, subsequently, to predict students who are going to abandon a course before it ends. Among such approaches, the ones achieving the best performance use enriched sets of features (to train their models) and produce results that cannot be used to easily clearly characterize the different behaviours of dropping-out and non-dropping-out students. The present work proposes the design of a novel process to train a set of dropout predictors leveraging on a reduced set of features. The underlying idea is to exploit weekly data in order to classify, with acceptable levels of precision, students who are likely going towards dropout or not. In cases of uncertainty, the classification decision is deferred to the next week, when new data is available. Such an approach offers several advantages. The first one is the chance to build a real-time educational decision support systems able to support decision as sufficient information is available. The second one is to preserve resources and avoiding wasting them with students erroneously classified at risk of dropout. The third one is to allow explicit characterization of dropout-conducing behaviour by using a rule mining approach.

show abstract

How COVID-19 Affected Computer Science MOOC Learner Behavior and Achievements: A Demographic Study

Cited by 6 publications

References 7 publications

A Fuzzy-Based Evaluation of E-Learning Acceptance and Effectiveness by Computer Science Students in Greece in the Period of COVID-19

A Fuzzy-Based Evaluation of E-Learning Acceptance and Effectiveness by Computer Science Students in Greece in the Period of COVID-19

AI-assisted analysis of content, structure, and sentiment in MOOC discussion forums

A Time-Aware Approach for MOOC Dropout Prediction Based on Rule Induction and Sequential Three-Way Decisions

Contact Info

Product

Resources

About