Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Affects <s> 1. Introduction The study of emotion Types of evidence for theories of emotion Some goals for a cognitive theory of emotion 2. Structure of the theory The organisation of emotion types Basic emotions Some implications of the emotions-as-valenced-reactions claim 3. The cognitive psychology of appraisal The appraisal structure Central intensity variables 4. The intensity of emotions Global variables Local variables Variable-values, variable-weights, and emotion thresholds 5. Reactions to events: I. The well-being emotions Loss emotions and fine-grained analyses The fortunes-of-others emotions Self-pity and related states 6. Reactions to events: II. The prospect-based emotions Shock and pleasant surprise Some interrelationships between prospect-based emotions Suspense, resignation, hopelessness, and other related states 7. Reactions to agents The attribution emotions Gratitude, anger, and some other compound emotions 8. Reactions to objects The attraction emotions Fine-grained analyses and emotion sequences 9. The boundaries of the theory Emotion words and cross-cultural issues Emotion experiences and unconscious emotions Coping and the function of emotions Computational tractability. <s> BIB001 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Affects <s> We present a model of the generic learning process and associated metacognitive processes that aid efficient learning. These models can be used as internal representations of a learner’s cognitive-emotive state while engaged in learning. They can also be used to present to the learner a representation of their progress in learning much like a coach o r mentor might use to assist a student. These models and associated representations might be displayed alongside the primary external representation of the subject being learned o r they might be integrated into it. The assessment of the metacognitive processes which shadow the primary learning process needs to be sensitive to the affective state of the learner. As the student proceeds through the learning journey, their affective state may cycle through a wide spectrum of emotions, which influence how efficiently, effectively, and enjoyably they succeed in the learning task. Based upon an understanding and application o f our proposed model, the structure a nd function o f external representations would reflect the learner’s cognitive-emotive state. For example, some representations would provide copious hints that would accelerate the learning at the expense of the playfulness of the e xercise, while other r epresentations might t ransform the learning experience into an enthralling g ame. In p articular these models enable the system designer to provide alternative intervention strategies for the learner who is laboring under a misconception, ranging from a no-nonsense remedial i ntervention to allowing the learner to play out t heir misconceptions in a free-wheeling simulation model that ultimately reveals the folly of their thinking. <s> BIB002 </s> Context-Aware Recommender Systems for Learning: A Survey and Future Challenges <s> Affects <s> Using emotion detection technologies from biophysical signals, this study explored how emotion evolves during learning process and how emotion feedback could be used to improve learning experiences. This article also described a cutting-edge pervasive e-Learning platform used in a Shanghai online college and proposed an affective e-Learning model, which combined learners’ emotions with the Shanghai e-Learning platform. The study was guided by Russell’s circumplex model of affect and Kort’s learning spiral model. The results about emotion recognition from physiological signals achieved a best-case accuracy (86.3%) for four types of learning emotions. And results from emotion revolution study showed that engagement and confusion were the most important and frequently occurred emotions in learning, which is consistent with the findings from AutoTutor project. No evidence from this study validated Kort’s learning spiral model. An experimental prototype of the affective e-Learning model was built to help improve students’ learning experience by customizing learning material delivery based on students’ emotional state. Experiments indicated the superiority of emotion aware over non-emotion-aware with a performance increase of 91%. <s> BIB003
The modeling and use of affective information is a popular research topic in various research areas. Researchers widely refer to Russell's  two-dimension "circumplex model of affect," where emotions are seen as combinations of arousal and valence. The OCC BIB001 model is also referenced widely. This model specifies 22 emotion categories. In TEL, research on the influence of emotions on learning has also gained major interest in recent years. Some researchers have used Russell's model BIB003 as a basis to use emotional data to support learning. The work of the Affective Computing Group at MIT Media Lab BIB002 is also very prominent in this research area.