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’ cognitiveaffective 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 infl uence learning in a computer-based environment. This paper investigates the types of cognitive-affective state that students experience when they learn through a specifi c instance of CBL (i.e., a content sequencing system). Further, research was carried to understand whether the cognitive-affective states would infl uence 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’ cognitiveaffective states. Students were classifi ed 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 lowprior knowledge students.