Predicting students' emotions using machine learning techniques

Detecting students' real-time emotions has numerous benefits, such as helping lecturers understand their students' learning behaviour and to address problems like confusion and boredom, which undermine students' engagement. One way to detect students' emotions is through their feedback about a lecture. Detecting students' emotions from their feedback, however, is both demanding and time-consuming. For this purpose, we looked at several models that could be used for detecting emotions from students' feedback by training seven different machine learning techniques using real students' feedback. The models with a single emotion performed better than those with multiple emotions. Overall, the best three models were obtained with the CNB classiffier for three emotions: amused, bored and excitement

An Investigation of Metacognitive, Bottom-up and Top-down Strategies in L2 Listening

Listening comprehension is the centerpiece of learning a language and it is also the most difficult modality for student success. This study investigated two processes: top-down and bottom-up processing in second language learning, as well as how metacognitive strategy regulates the learning process. Four participants were selected with varying degrees of second language listening ability; two good listeners and two weak listeners. Qualitative research methods including three data sources: interviews, students’ listening notes and teacher observations were triangulated to explore how learners progressed with language listening strategy instruction. Based on the findings, all participants have gained from the listening strategies instructions. Although the weak listeners in this study showed no improvement in their scores, they all, especially these weak listeners, gained the strategy of listening, as evidenced by the increasing awareness of their own listening process, forming a better listening habit and gaining confidence in listening. The results also showed that learners at different learning stages use top-down and bottom-up processing differently

Recognizing Multidimensional Engagement of E-learners Based on Multi-channel Data in E-learning Environment

Despite recent advances in MOOC, the current e-learning systems have advantages of alleviating barriers by time differences, and geographically spatial separation between teachers and students. However, there has been a 'lack of supervision' problem that e-learner's learning unit state(LUS) can't be supervised automatically. In this paper, we present a fusion framework considering three channel data sources: 1) videos/images from a camera, 2) eye movement information tracked by a low solution eye tracker and 3) mouse movement. Based on these data modalities, we propose a novel approach of multi-channel data fusion to explore the learning unit state recognition. We also propose a method to build a learning state recognition model to avoid manually labeling image data. The experiments were carried on our designed online learning prototype system, and we choose CART, Random Forest and GBDT regression model to predict e-learner's learning state. The results show that multi-channel data fusion model have a better recognition performance in comparison with single channel model. In addition, a best recognition performance can be reached when image, eye movement and mouse movement features are fused.Comment: 4 pages, 4 figures, 2 table

Culturally Responsive Literacy Instruction and Social–Emotional Teaching Practices for Linguistically Diverse Learners in the United States

This dissertation includes two previous research studies with a central theme to sustain and advance culturally and linguistically responsive practices in literacy instruction, which are essential for promoting diversity and equity. This work highlights the need for a more holistic approach to English language teaching that integrates culturally responsive teaching and social–emotional learning. Culturally responsive teaching recognizes the importance of cultural and linguistic diversity in the classroom. Social–emotional learning focuses on developing students’ emotional intelligence and interpersonal skills, which are critical for academic success and overall well-being. Both studies call for a greater diversity of language teacher preparation curricula and teaching practices that incorporates cultural and social–emotional competencies into coursework to empower teachers to effectively engage students from diverse backgrounds and make literacy learning experiences relevant to their communities and meaningful to their cultural identities. The findings of these studies reveal implications for teacher education and literacy instruction for diverse learners. There are several potential research directions highlighted in the final chapter

Looking At Chinese Students’ Strategy Use for English Learning and Use in the Asian Context: An Ecological and Complexity Perspective

Against the background of educational globalization, this article first points out the substantially increasing number of Chinese students studying in Asian regions which are different socio-cultural-linguistic contexts from the western Anglophone countries, and calls on the second language (L2) education and English language teaching (ELT) research field to pay adequate attention to these Chinese students’ acculturation to the Asian contexts. A research project is then proposed as an exploratory study to investigate the Chinese students’ acculturation process at Assumption University of Thailand, which is a typical case of a multicultural and multilingual Asian context of education, through their use of strategies in English learning and use. Basing his educational philosophy on ecologism, the author suggests the adoption of a complexity perspective on L2 learning as a way of interpreting the issues involved in the research. This suggestion is subsequently supported by an overview of language learning strategy (LLS) research, an overview of the development of complexity theory (C-T) and its applications to SLA and L2 education research, and by a pilot study. The author then introduces R. Oxford’s Strategic Self-Regulation (S²R) Model as a strategy framework that is appropriate for an integration with C-T for research. Lastly the author outlines the research methodology and wraps up the article by calling for more efforts to establish an ecological prespective in the L2 education and ELT research fields

Towards emotion awareness tools to support emotion and appraisal regulation in academic contexts

International audienceThis paper studies learners’ emotion awareness in university level academic contexts as a first step to help learners regulate their emotions. Existing emotion awareness tools offer little information on learners’ emotions and their antecedents. This study created an emotion-reporting grid for university students based on the emotions they experienced daily. Students were interviewed based on their self-reported grid. A quantitative descriptive analysis of these retrospective interviews was conducted based on Pekrun’s control-value theory of achievement emotions. Student transcripts were analyzed based on the focus of their emotions (retrospective, activity, or prospective), the causes they attribute to their emotions (agent or external circumstances) and how they appraised the situation in which they experienced the emotions (value and control). We discuss the results with regard to the types of emotion-oriented and appraisal-oriented regulation strategies used in learning contexts and draw implications for the design of emotion awareness tools to support emotion regulation processes

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers