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

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 influence 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

Assessment of student's cognitive-affective states in learning within a computer-based environment: Effects on performance

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

When does disengagement correlate with learning in spoken dialog computer tutoring?

We investigate whether an overall student disengagement label and six different labels of disengagement type are predictive of learning in a spoken dialog computer tutoring corpus. Our results show first that although students' percentage of overall disengaged turns negatively correlates with the amount they learn, the individual types of disengagement correlate differently with learning: some negatively correlate with learning, while others don't correlate with learning at all. Second, we show that these relationships change somewhat depending on student prerequisite knowledge level. Third, we show that using multiple disengagement types to predict learning improves predictive power. Overall, our results suggest that although adapting to disengagement should improve learning, maximizing learning requires different system interventions depending on disengagement type. © 2011 Springer-Verlag Berlin Heidelberg

Automated Gaze-Based Mind Wandering Detection during Computerized Learning in Classrooms

We investigate the use of commercial off-the-shelf (COTS) eye-trackers to automatically detect mind wandering—a phenomenon involving a shift in attention from task-related to task-unrelated thoughts—during computerized learning. Study 1 (N = 135 high-school students) tested the feasibility of COTS eye tracking while students learn biology with an intelligent tutoring system called GuruTutor in their classroom. We could successfully track eye gaze in 75% (both eyes tracked) and 95% (one eye tracked) of the cases for 85% of the sessions where gaze was successfully recorded. In Study 2, we used this data to build automated student-independent detectors of mind wandering, obtaining accuracies (mind wandering F1 = 0.59) substantially better than chance (F1 = 0.24). Study 3 investigated context-generalizability of mind wandering detectors, finding that models trained on data collected in a controlled laboratory more successfully generalized to the classroom than the reverse. Study 4 investigated gaze- and video- based mind wandering detection, finding that gaze-based detection was superior and multimodal detection yielded an improvement in limited circumstances. We tested live mind wandering detection on a new sample of 39 students in Study 5 and found that detection accuracy (mind wandering F1 = 0.40) was considerably above chance (F1 = 0.24), albeit lower than offline detection accuracy from Study 1 (F1 = 0.59), a finding attributable to handling of missing data. We discuss our next steps towards developing gaze-based attention-aware learning technologies to increase engagement and learning by combating mind wandering in classroom contexts