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Study finds that emotion recognition in computer technology could be used to improve learning experiences

​Several challenges exist in computer science education and more needs to be done to cater for the current and upcoming demand for computer science within higher education. Teaching and learning methods exist that directly attempt to address these issues found in computer science education, but certain issues remain open problems. Fundamental stumbling blocks are still present in certain institutions (especially in third world institutions), such as a lack of numeracy skills, but more can be done to cater to students. The observations by a scientist, Prof Dustin van der Haar from the University of Johannesburg (UJ) are published in the Springer’s lecture notes in computer science.

Technology has progressed well, and the near prospects show even more potential in various domains. It has especially opened opportunities within the space of higher education. There is also evidence that shows there is improved access to education and satisfaction through distance learning. More recently we can also see that blended learning has enhanced both the effectiveness and efficiency of more meaningful learning experiences. Thereby showing there is a continuing inquiry into how best to use technology in higher education, says UJ researchers.

The UJ study serves as exploratory research that allows for further insights on deriving emotion within the domain of the physical classroom without being too intrusive. Once sufficient background on the problem domain and methods is explored, a model is formed, along with a basic implementation for a pilot study to derive insights relevant on whether there is value in using computer vision methods to derive emotion within a physical classroom.

The scientist, Prof Dustin van der Haar, Deputy HOD – Information and Cyber Security within the Academy of Computer Science and Software Engineering says: “Changing the education landscape to include more participatory teaching methods to maximize student learning has been a challenge especially in the sciences. It is further complicated by the fact that certain students do not engage with certain participatory methods. An experienced educator can pick up any distance between these teaching methods and their students to facilitate adaptive teaching and learning.”

The observation addresses this distance by proposing a model that derives user sentiment with affective computing methods and leveraging the sentiment outcome to support the educator by providing feedback relevant for teaching. The technology will then allow the educator to adjust teaching and provide a more personalized teaching experience cognizant of classroom concepts with a lower level of understanding or that evoke certain emotions.

UJ researchers introduce such an approach by applying affective computing methods where the student’s emotion is derived using a sensor, some processing and machine learning to achieve adaptive teaching and learning in a physical classroom while limiting the cost to the educator’s side.

“Overall one can ask is there value in pursuing emotion recognition for computer science education or education as a whole. Although one has to be cognizant of the constraints experienced within this context, it still achieves the use case and it opens up a further avenue of research that may assist educational psychologists and educators alike in determining conducive conditions for student learning,” concludes Prof van der Haar.

Dustin van der Haar is an Associate Professor in the Academy of Computer Science and Software Engineering at the University of Johannesburg. He has published articles both nationally and internationally in pattern recognition. His research interests include biometrics, computer vision and machine learning and have collaborated with multiple universities. The vision of his research intends on empowering users by bridging the gap of interaction between people and technology in their daily lifestyle using pattern recognition.

Prof Dustin van der Haar
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