Assessment of students’ cognitive–affective states in learning within a computer-based environment: Effects on performance

Abstract

Students’ cognitive-affective states are human-elements that are crucial in the design of computer-based learning (CBL) systems. This paper presents an investigation of students’ cognitive-affective states (i.e., engaged concentration, anxiety, and boredom) when they learn a particular course within CBL systems. The results of past studies by other researchers suggested that certain cognitive-affective states; particularly boredom and anxiety could negatively influence learning in a computer-based environment. This paper investigates the types of cognitive-affective state that students experience when they learn through a specific instance of CBL (i.e., a content sequencing system). Further, research was carried to understand whether the cognitive-affective states would influence students’ performance within the environment. A one-way between-subject-design experiment was conducted utilizing four instruments (i) CBL systems known as IT-Tutor for learning computer network, (ii) a pre-test, (iii) a post-test, and (iv) self-report inventory to capture the students’ cognitive-affective states. A cluster analysis and discriminant function analysis were employed to identify and classify the students’ cognitive-affective states. Students were classified according to their prior knowledge to element the effects of it on performance. Then, non-parametric statistical tests were conducted on different pairs of cluster of the cognitive-affective states and prior knowledge to determine differences on students’ performance. The results of this study suggested that all the three cognitive-affective states were experienced by the students. The cognitive-affective states were found to have positive effects on the students’ performance. This study revealed that disengaged cognitive-affective states, particularly boredom can improve learning performance for low-prior knowledge students.