Predicting Learners’ Emotions in Mobile MOOC Learning via a Multimodal Intelligent Tutor

Pham, Phuong; Wang, Jingtao

doi:10.1007/978-3-319-91464-0_15

Cited by 15 publications

(7 citation statements)

References 21 publications

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“…The fluctuation of emotions is often ignored in technology-enhanced learning research [66]. An important research direction is evaluating the feasibility of employing ubiquitous technologies, such as smartphones and web cameras, to measure learners' photoplethysmography (PPG) signals and facial expressions [67] in a nonintrusive way, thereby supplementing self-reported, clickstream, and forum textual data to enhance emotional engagement. Another promising line of research is applying the techniques of natural learning processing and evaluating the feasibility of adopting a virtual assistant to capture emotional engagement throughout a MOOC [68].…”

Section: Discussionmentioning

confidence: 99%

Emotionally Engaged Learners Are More Satisfied with Online Courses

Deng

2021

Sustainability

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Research on massive open online courses (MOOCs) has tended to focus on outcome indicators valued in traditional higher education settings, particularly achievement and completion. This study highlights the differences between MOOCs and credit-bearing university courses and shifts this focus to an alternative outcome indicator—learner satisfaction. In this study, engagement is identified as an important antecedent of learner satisfaction and is conceptualised and operationalised as a multidimensional construct. This study built three regression models to identify the relative importance of behavioural, cognitive, emotional, and social engagement for learner satisfaction after controlling for personal characteristics unrelated to the criteria of good teaching. The analysis showed that engagement explained approximately 20% of the variance in learner satisfaction with MOOCs. Emotional engagement was more influential for predicting learner satisfaction than cognitive engagement and behavioural engagement. Social engagement had no significant effect on learner satisfaction. Demographics (age, education level, and origin) and motivation were of limited utility in predicting learner satisfaction with MOOCs, accounting for 4% and 2% of variance, respectively. Based on research findings, the article presents the following propositions: (1) configure the MOOC teaching and learning environment in a way that enhances emotional engagement; (2) statistically adjust for age, education level, origin, and motivation when interpreting learner satisfaction results; and (3) monitor the level of emotional engagement and implement educational interventions to provide support for emotional disengagers.

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Section: Discussionmentioning

confidence: 99%

Emotionally Engaged Learners Are More Satisfied with Online Courses

Deng

2021

Sustainability

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“…Another strand of work focused on inferring learners' cognitive and affective states in mobile-based MOOCs captures photoplethysmography (PPG) signals and facial expression data , 2018a, 2018bXiao & Wang, 2015. AttentiveLearner uses on-lens finger gestures on a back camera for video control (i.e., a video plays when the camera lens is covered and pauses if it is uncovered).…”

Section: Online Video Lectures and State-of-the-art La: Challenges An...mentioning

confidence: 99%

“…The initial results related to how instructors can identify confusing topics for learners based on overall feedback (i.e., aggregated cognitive states inferred from PPG signals) were demonstrated. The advanced version of the system, AttentiveLearner 2 , uses both back and front cameras to monitor PPG signals and track learners' facial expressions, respectively, to infer learners' cognitive-affective states in real time (Pham & Wang, 2018b). The two channels are used to achieve more robust emotion detection.…”

Section: Online Video Lectures and State-of-the-art La: Challenges An...mentioning

confidence: 99%

Tcherly:

Chavan

Mitra

2022

Learning Analytics

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The use of online video lectures in universities, primarily for content delivery and learning, is on the rise. Instructors’ ability to recognize and understand student learning experiences with online video lectures, identify particularly difficult or disengaging content and thereby assess overall lecture quality can inform their instructional practice related to such lectures. This paper introduces Tcherly, a teacher-facing dashboard that presents class-level aggregated time series data on students’ self-reported cognitive-affective states they experienced during a lecture. Instructors can use the dashboard to evaluate and improve their instructional practice related to video lectures. We report the detailed iterative prototyping design process of the Tcherly Dashboard involving two stakeholders (instructors and designers) that informed various design decisions of the dashboard, and also provide usability and usefulness data. We demonstrate, with real-life examples of Tcherly Dashboard use generated by the researchers based on data collected from six courses and 11 lectures, how the dashboard can assist instructors in understanding their students’ learning experiences and evaluating the associated instructional materials.

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“…Giving machines the capability to understand human language effectively opens new horizons for human-machine conversation systems [1], tutoring systems [2], and health care [3], to name a few applications.…”

Section: Introductionmentioning

confidence: 99%