TY - GEN
T1 - Student Emotion Recognition in Computer Science Education
T2 - 6th International Conference on Learning and Collaboration Technologies, LCT 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019
AU - van der Haar, Dustin Terence
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - One of the key skills in the fourth industrial revolution is the ability to program. To attain this skill, many prospective students study for a degree in computer science or a related field. An important skill in computer science is the ability to solve for a particular problem by programming an application. However, some challenges exist that make teaching this skill difficult, which leads to student frustration and a decrease in grades. These challenges can be attributed to a lack of access to appropriate skill-building or disjoint teaching methods that are not applicable to the student, which is especially prevalent with some inexperienced educators. Using teaching methods, which a student cannot relate to can lead to distance between the taught skill and the student. The article aims to address 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. It can also provide an informal assessment of content delivery by using student sentiment to infer whether concepts are well received. The preliminary prototype shows there is value in using assistive technologies in the physical classroom to achieve adaptive student learning. However, the onus is still on the educator to be able to react correctly to compensate for the lack of understanding for it to be an effective tool.
AB - One of the key skills in the fourth industrial revolution is the ability to program. To attain this skill, many prospective students study for a degree in computer science or a related field. An important skill in computer science is the ability to solve for a particular problem by programming an application. However, some challenges exist that make teaching this skill difficult, which leads to student frustration and a decrease in grades. These challenges can be attributed to a lack of access to appropriate skill-building or disjoint teaching methods that are not applicable to the student, which is especially prevalent with some inexperienced educators. Using teaching methods, which a student cannot relate to can lead to distance between the taught skill and the student. The article aims to address 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. It can also provide an informal assessment of content delivery by using student sentiment to infer whether concepts are well received. The preliminary prototype shows there is value in using assistive technologies in the physical classroom to achieve adaptive student learning. However, the onus is still on the educator to be able to react correctly to compensate for the lack of understanding for it to be an effective tool.
KW - Affective computing
KW - Computer science education
KW - Computer vision
UR - http://www.scopus.com/inward/record.url?scp=85069833055&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-21814-0_23
DO - 10.1007/978-3-030-21814-0_23
M3 - Conference contribution
AN - SCOPUS:85069833055
SN - 9783030218133
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 301
EP - 311
BT - Learning and Collaboration Technologies. Designing Learning Experiences - 6th International Conference, LCT 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings
A2 - Zaphiris, Panayiotis
A2 - Ioannou, Andri
PB - Springer Verlag
Y2 - 26 July 2019 through 31 July 2019
ER -
