How CS academics view student engagement

Morgan, Michael J.; Butler, Matthew; Thota, Neena; Sinclair, Jane

doi:10.1145/3197091.3197092

Cited by 14 publications

(3 citation statements)

References 8 publications

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“…In order to build an HMM, we utilize the manifest features for student performance, challenge, endurance, and attendance gap. We pick the manifest features by performing an extensive thematic analysis [84,116] across the literature about student engagement [43,50,73]. The features we use are as follows.…”

Section: Students' Engagement Moods (Rq1) 61 Model Constructionmentioning

confidence: 99%

Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websites

Mogavi,

Ma,

Hui

2021

Preprint

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Problem-Based Learning (PBL) is a popular approach to instruction that supports students to get hands-on training by solving problems. Question Pool websites (QPs) such as LeetCode, Code Chef, and Math Playground help PBL by supplying authentic, diverse, and contextualized questions to students. Nonetheless, empirical findings suggest that 40% to 80% of students registered in QPs drop out in less than two months. This research is the first attempt to understand and predict student dropouts from QPs via exploiting students' engagement moods. Adopting a data-driven approach, we identify five different engagement moods for QP students, which are namely challenge-seeker, subject-seeker, interest-seeker, joy-seeker, and non-seeker. We find that students have collective preferences for answering questions in each engagement mood, and deviation from those preferences increases their probability of dropping out significantly. Last but not least, this paper contributes by introducing a new hybrid machine learning model (we call Dropout-Plus) for predicting student dropouts in QPs. The test results on a popular QP in China, with nearly 10K students, show that Dropout-Plus can exceed the rival algorithms' dropout prediction performance in terms of accuracy, F1-measure, and AUC. We wrap up our work by giving some design suggestions to QP managers and online learning professionals to reduce their student dropouts.CCS Concepts: • Human-centered computing → HCI theory, concepts and models; • Applied computing → Interactive learning environments; • Computing methodologies → Machine learning approaches.

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Section: Students' Engagement Moods (Rq1) 61 Model Constructionmentioning

confidence: 99%

Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websites

Mogavi,

Ma,

Hui

2021

Preprint

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“…Especially in Computer Science there are several ongoing discussions on how academics view the concept of engagement (e.g. [12,13]), and how students' engagement in computer science classes can be improved [14]. This is even more important when we are dealing with first year students in introductory Computer Science modules [15,16].…”

Section: Students' Engagement In Muvementioning

confidence: 99%

The Relationship Between Students' Engagement and the Development of Transactive Memory Systems in MUVE

Nisiotis

Kleanthous

2019

Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education

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Student engagement is a very important topic in higher education hence, it drew a lot of research interest over the years. The use of educational MultiUser Virtual Environments (MUVEs) that provide synchronous interaction, dynamic, interactive and social learning experiences have the potential to increase student engagement and contribute to their learning experience. Due to increased social and cognitive presence, the use of such environments can result in greater student engagement when compared to traditional asynchronous learning environments. In this work, we hypothesized that students' engagement in collaborative learning activities will increase if Transactive Memory System (TMS) constructs are present. Thus, we employed the theory of TMS that emphasizes the importance of Specialization, Coordination and Credibility between members in a team. The results show that there is a significant correlation between the development of TMS and students' engagement. In addition, further quantitative and observation analysis reveals some interesting facts about students' engagement with respect to their collaboration in group activities.

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“…Problem and project-based learning, inverted classroom, peer instruction, games, and gamification, are some of those methodological alternatives. Common to all of them is the need for greater student engagement, moving from passive to active participation, allowing them to become the protagonists of the(ir) learning process [5] [10].…”

Section: Introductionmentioning

confidence: 99%

Exploring Creativity and Learning through the Construction of (Non-Digital) Board Games in HCI Courses

Silveira

2020

Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education

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Several non-technical skills -such as teamwork, verbal and written communication, time management, problem-solving, and critical thinking -are currently being highlighted in discussions about curriculum recommendations for the computing field. In the same way, methodological alternatives are discussed in order to support the development of these skills and engage students in the learning process. In this scenario, we report an experience carried out in three distinct Human-Computer Interaction (HCI) courses, in graduate and undergraduate programs, where the students created their own (non-digital) board games to explore specific HCI concepts. The experience involved 84 students. We observed student engagement during the proposed activities and their impressions of the impact of those activities on their learning process.

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How CS academics view student engagement

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 14 publications

References 8 publications

Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websites

Characterizing Student Engagement Moods for Dropout Prediction in Question Pool Websites

The Relationship Between Students' Engagement and the Development of Transactive Memory Systems in MUVE

Exploring Creativity and Learning through the Construction of (Non-Digital) Board Games in HCI Courses

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