To my librarianship readers: This is the third of a series of reflections on readings from a text for a course that I am taking on learning analytics. While it may not be directly related to librarianship or library assessment, I am hoping to learn of the opportunities where library analytics and learning analytics overlap.
2022. D’Mello, Sidney K. and Emily Jensen. Chapter 12: Emotional Learning Analytics. In, Handbook of Learning Analytics, C. Lang, G. Siemans, A. F. Wise, D. Gasevic and A. Merceron, eds. Society of Learning Analytics Research (SoLAR). DOI: 10.18608/hla22.012
This chapter discusses the ubiquity and importance of emotion to learning. It argues substantial progress can be made by coupling discovery-oriented, data-driven, analytic methods of learning analytics and educational data mining with theoretical advances and methodologies from the affective and learning sciences. Core, emerging, and future themes of research at the intersection of these areas are discussed. Keywords: Affect, affective science, affective computing, educational data mining, learning Analytics
Human learning (versus machine learning) does not take place in a vaccuum; it is influenced heavily by environmental and internal factors, including emotions of the learner. This chapter highlighted a myriad of experiments and systems designed to identify characteristics of physical features or written or spoken text which are most likely associated with specific emotions. Traditional methods of detection have relied heavily on human judgments, following standard protocols such as the Baker-Rodrigo Observation Method Protocol (BROMP). More recently, machine learning methods of supervised learning, trained on data sets derived from such methods, have proven to be more efficient and nearly as accurate. Parallel development of methods of detection based on textual (aural or written) traces as well as bodily or physical traces (notably eye-gaze detection and movement of the body) have resulted in systems that can detect emotions of boredom, confusion, and achievement or success or satisfaction.
Such methods have been applied in classroom settings, notably as intelligent online tutorial systems, collaborative learning systems, and online classroom management dashboards. The intelligent tutorial systems can detect occurrence of a limited set of emotions and behaviors associated with being on- or off-task. The systems then can react to these detections, providing responses meant to reassure and re-connect the learner who may be getting less engaged due to the negative emotions. Often these responses are programmed to be empathic, even mirroring the emotion(s) detected, with the expectation that the learner will re-engage with the system.
While these systems appear to be improving on the detection of these emotions, there are many other emotions that impact learning. Affective states of envy, jealousy, pride, guilt, shame, and others may be more difficult to detect as they often are negatively perceived in many cultures, and thus may be more likely to be hidden by the learner. Experiments of recording the occurrences of these emotions combined with physiological detections could help with identifying facial characteristics that could lead to applications in which these emotions are empathically addressed to assist with the emotional growth of the learner.
I was concerned about the idea of a dashboard for instructor’s use that detects and highlights emotions of specific students. My initial concern was that this could lead to the erosion or lack of development of this skill in the teachers themselves. I understand that it would be most useful to have little flags above each child’s physical head denoting the engagement level or lack of understanding, but I wonder if we may be becoming too dependent on such notifications.
Coming from a field outside of education or learning sciences, much of the information presented in this chapter was new to me. It was useful to learn of the general research and understanding of the impact of emotion on learning, particularly its signaling, evaluative, and modulating functions. This idea is not unlike lessons I am learning about the psychology behind mindfulness and communication, notably how emotions are indicators of needs being met or not being met (signaling).
This chapter was a whirlwind of the basic research behind, and the progressive development of affective computing in a learning environment. I was disappointed in the lack of research or applications from my literature searching of writings in librarianship. There are many opportunities for librarians to integrate affective assessment of communications in training sessions, reference interviews, automated “virtual reference” using chatbots, and formal discussion forums.