Abstract:

In this paper, we demonstrate our experience and assessment of the impact of integrating affective (emotional) feedback into assessment and interactive activities during class meetings. In typical class meetings without tools and activities to measure affective feedback, the evaluation of students’ state and learning is primarily based on summative and infrequent cognitive assessment methods, such as attendance, in-class participation, assignment grades, etc. By affective feedback, we refer to actively measuring and collecting specific emotions and sentiments of students during class meetings, especially in the service of teaching and learning. Moreover, we can also look for these data in the students’ text-based responses from in-class activities such as reflective posts and forum discussions. In the work reported in this paper, these activities were facilitated by the Discovery Teaching platform; a web application with several tools designed to support interactive and evidence-based teaching and learning in classrooms. In detail, during class sessions, frequent feedback pollings were used where students were asked to indicate their real-time sentiment by choosing between three default options representing Engaged, Confused, or Bored, plus a short optional comment. Another regular activity was the submission of reading reflections, as a forum post, ahead of an upcoming class corresponding to pre-class readings or materials. We also looked at direct student feedback comments after individual classes, and also course feedback at the end of the semester. The experience and impact assessment of affective feedback reported in this paper is based on data collected from three Computer Science courses during the spring 2019 and spring 2020 semesters where affective feedback activities were utilized as a part of a larger interactive computer-supported pedagogy.

In our data analysis, we took into account the correlations between students’ sentiments and emotions and their performance on formative and summative activities. This would inform us whether affective feedback serves as a good indicator of students’ learning and response to class materials and activities. We also looked at the sentiments of students in their reading reflections before every class and how they felt after the class to see if this, often hidden, unused, and subtle affective feedback is informative and useful in understanding and improving the teaching and learning processes. To uncover these measures in our textual data, we used the IBM Watson Tone Analyzer library. Our overall experience and results demonstrate that affective feedback can facilitate instructors’ understanding of the class and individual student’s learning, progress, challenges, nature of engagement with class materials and activities, and serves as a valuable resource in assessing and improving the quality of the teaching and learning processes in pursuit of better learning and teaching outcomes, equitable and inclusive pedagogy, as well as an informed and evidence-based agile pedagogy. In this study, we also assess the emotional evidence and impact of the COVID-19 pandemic as seen in the data analysis.

Keywords:

Emotional feedback, affective feedback, formative assessment, interactive teaching, agile teaching, COVID19.