Incorporating Learner Emotions through Sentiment Analysis in Adaptive E-learning Systems: A Pilot Study

This research delves into the exciting avenue of incorporating learner emotions into adaptive E-learning systems through sentiment analysis techniques. Utilizing a pilot study with 40 undergraduate computer science students, we investigated the ability of an adaptive system to detect boredom and frustration in learner forum posts and subsequently personalize content or offer support based on these emotional states. This approach proved demonstrably successful, as learners in the experimental group who received emotion-based adaptation exhibited both increased engagement (reflected in higher time spent on tasks) and improved learning outcomes (evidenced by higher post-test scores). Furthermore, qualitative feedback revealed positive responses to the personalized interventions, indicating that learners appreciated the tailored support provided by the system. While acknowledging limitations such as the small sample size and single subject area, this study firmly establishes the promising potential of emotion-aware adaptive systems. By addressing the emotional dynamics of the learning process, such systems can pave the way for truly personalized and responsive E-learning environments that cater to individual learner needs and foster deeper engagement, positive learning experiences, and ultimately, success for all students

Scripts and scaffolds In Problem-based computer supported collaborative learning environments: fostering participation and transfer

This study investigates collaborative learning of small groups via text-based com-puter-mediated communication. We analyzed how two approaches to pre-structure communication influence participation, individual knowledge transfer, the conver-gence of participation and the convergence of knowledge among learning partners. We varied the factor "scripted cooperation" and the factor "scaffolding" in a 2x2-design. 105 university students of Pedagogy participated. Results show that scrip-ted cooperation was most and scaffolding least beneficial to individual transfer, knowledge convergence and participation in comparison to open discourseDiese Studie befasst sich mit kooperativem Lernen in Kleingruppen über text-basierte computervermittelte Kommunikation. Es wurden zwei Ansätze der Vor-strukturierung von computervermittelter Kommunikation und ihre Auswirkungen auf Partizipation, individuellen Wissenstransfer, die Konvergenz der Partizipation und die Wissenskonvergenz innerhalb einer Lerngruppe untersucht. Dabei wurden die Faktoren "Kooperationsskript" und "Scaffolding" in einem 2x2-Design variiert. 105 Studierende der Pädagogik nahmen teil. Die Ergebnisse zeigen, dass sich das Ko-operationsskript am günstigsten und das Scaffolding am wenigsten günstig auf individuellen Wissenstransfer, Wissenskonvergenz und Partizipation im Vergleich zu einer Kontrollgruppe des 'Offenen Diskurses' ausgewirkt ha

The second language acquisition of French immersion learners: Investigating the variables influencing motivation and achievement

The purpose of this study was to investigate the relationships between attitudes, motivational variables and achievement in 53 grade six French Immersion (FI) students, from four elementary FI schools. Students completed surveys to measure motivational variables, as well as a French achievement test and final report card grades for French Language Arts. The statistical tests included correlational analysis, discriminant function analysis and regression analysis. Attitudes towards French, desire to learn French, interest in foreign languages and motivational intensity all correlated positively with achievement. High French use anxiety correlated negatively with achievement. Contrary to earlier studies, integrative motivation did not correlate significantly with achievement. Regression analysis indicated that anxiety rather than motivation was the best predictor of achievement. The results of this study supported many of the relationships of the Socio-Educational Model. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .M366. Source: Masters Abstracts International, Volume: 40-06, page: 1345. Adviser: Norm Diffey. Thesis (M.Ed.)--University of Windsor (Canada), 2002

Detecting and Modelling Stress Levels in E-Learning Environment Users

A modern Intelligent Tutoring System (ITS) should be sentient of a learner's cognitive and affective states, as a learner’s performance could be affected by motivational and emotional factors. It is important to design a method that supports low-cost, task-independent and unobtrusive sensing of a learner’s cognitive and affective states, to improve a learner's experience in e-learning, as well as to enable personalized learning. Although tremendous related affective computing research were done in this area, there is a lack of empirical research that can automatically measure a learner's stress using objective methods. This research is set to examine how an objective stress measurement model can be developed, to compute a learner’s cognitive and emotional stress automatically using mouse and keystroke dynamics. To ensure the measurement is not affected even if the user switches between tasks, three preliminary research experiments were carried out based on three common tasks during e-learning − search, assessment and typing. A stress measurement model was then built using the datasets collected from the experiments. Three stress classifiers were tested, namely certainty factors, feedforward back-propagation neural network and adaptive neuro-fuzzy inference system. The best classifier was then integrated into the ITS stress inference engine, which is designed to decide necessary adaptation, and to provide analytical information of learners' performances, which include stress levels and learners’ behaviours when answering questions

THE ROLE OF EMOTIONS IN MUSIC EDUCATION: THEORETICAL INSIGHTS

Emotional expression has been the focus of teachers and educational researchers, as it can result in an improvement in cognitive performance. In specific settings, personal and emotional experiences can provide a steppingstone to developmental and learning processes. Emotions significantly influence learner learning and play a crucial role in quality teaching, educational reform, and learner-teacher interaction. The inherent social and communicative nature of music would make group training an excellent tool for increasing the coordination of behaviour, affect, and mental states among children. This paper aims to explore the literature on various aspects of the concept of emotions in the context of music education with the main focus on opportunities for experiencing and expressing emotion in music education, learners' positive emotion experiences in music education, teaching to generate positive emotion outcomes, and the benefits of a greater emphasis on the emotions in music education. The results of theoretical analysis indicate that music education has a particularly positive effect on identifying emotions, emotion regulation, emotion recognition, improved learning, and self-expression

Five Lenses on Team Tutor Challenges: A Multidisciplinary Approach

This chapter describes five disciplinary domains of research or lenses that contribute to the design of a team tutor. We focus on four significant challenges in developing Intelligent Team Tutoring Systems (ITTSs), and explore how the five lenses can offer guidance for these challenges. The four challenges arise in the design of team member interactions, performance metrics and skill development, feedback, and tutor authoring. The five lenses or research domains that we apply to these four challenges are Tutor Engineering, Learning Sciences, Science of Teams, Data Analyst, and Human–Computer Interaction. This matrix of applications from each perspective offers a framework to guide designers in creating ITTSs

Emotions identification utilizing periodic handwriting on mobile surfaces

The purpose of this study is to between the learners’ emotional characteristics and styles in touch screen environments. We propose a method to identify the variety of learners' boredom in the learning process utilising handwriting data. The novelty of the method is to avoid explicit polling of learners how do they feel. We use the recurring acquiring of personal handwriting data utilising the computational power of both a mobile device and cloud-based resources. Also, we use machine learning-based sentiment detection in the research. We smoothly inject periodic handwriting tests convolving them with learning objects to study the correlation between learners’ emotions dynamic, they demonstrate, and the ability to focus and think critically. With the help of machine-learning methods and new communication protocols, we can step up the student-centric mobile-based education process by taking advantage of the latest achievements in a big data analysis and cloud computing. Also, we clarify the conceptual model for the testbed used in the experiment. The findings may likely impact the future personalized e-learning systems