Exploring the Link Between Task Complexity and Students’ Affective States During Engineering Laboratory Activities

Abstract

Assessment and feedback play an instrumental role in an individual’s learning process. Continued assistance is required to help students learn better and faster. This need is especially prominent in engineering laboratories where students must perform a wide range of tasks using different machines. One approach to understanding how students feel towards using certain machines is to assess their affective states while they use these machines. Affective state can be defined as the state of feeling an emotion. The authors of this work hypothesize that there is a correlation between students’ perceived affective states and task complexity. By adopting the Wood’s complexity model, the authors propose to assess how the correlations of perceived affective states of students change while they perform tasks of different complexity. In this study, each student performs a “hard” and an “easy” task on the same machine. Each student is given the same tasks using the same materials. Knowledge gained from testing this hypothesis will provide a fundamental understanding of the tasks that negatively impact students’ affective states and risk them potentially dropping out of STEM tracks, and the tasks that positively impact students’ affective states and encourage them to engage in more STEM-related activities. A case study involving 22 students using a power saw machine is conducted. Perceived affective states and completion time were collected. It was found that task complexity has an effect on subjects’ affective states. In addition, we observed some weak correlation between some of the perceived affective states and laboratory task performance. The distribution of correlation between affective states may change as the tasks change. With the knowledge of the relationship between task complexity and affective states, there is the potential to predict students’ affective states before starting a given engineering task.

Engineering students' epistemic affect and meta‐affect in solving ill‐defined problems

“…Emotion has been described as a “rapidly expanding, but internationally dispersed field” of study in engineering education (p. 232), most often introduced to complement or inform other areas of educational research, which has significant areas remaining to be explored (Lönngren, Bellocchi, et al, 2021). The range of both positive and negative emotions identified in this study through affective expressions is similar to the range of emotions reported in the literature elsewhere: first‐year engineering students completing tool‐based cutting challenges also reported feeling anxiety, frustration, stress, calm, comfort, and interest (Hu et al, 2016). Emotion self‐reports from first‐year engineering students found positive academic emotions that overlap with our findings including happiness, interest, and enjoyment, and negative academic emotions, mirroring our findings of confusion, fearfulness, frustration, and anxiousness (Villanueva et al, 2018).…”

“…Other studies of engineering students have looked at affect when students are engaged in a specific, most likely stressful, activity. Hu and colleagues (Hu et al, 2016) examined students' affective states when completing saw tasks after workshop instruction and found students had both positive and negative affective states. Many other studies have examined specific affective phenomena (e.g., test anxiety [Copp & Headley, 2021] or empathy [Strobel et al, 2013]).…”

Board # 91 :When to Provide Feedback? Exploring Human-Co-Robot Interactions in Engineering Environments

“…The primary objective of the student is to complete this task in a manner that optimizes performance (e.g., time to completion, usefulness of the prototype), while the primary objective of the co-robot is to ensure that the student completes the task in a safe and effective manner that results in i) tangible learning outcomes (e.g., improved performance) and ii) the desire to perform such tasks in the future. Measuring whether a student has the desire to perform similar tasks in the future is related to their affective state (i.e., emotions) during task performance 3 . Ekman has proposed six basic emotions that include happiness, sadness, disgust, surprise, fear, and anger 4 .…”