Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments

We study the incidence (rate of occurrence), persistence (rate of reoccurrence immediately after occurrence), and impact (effect on behavior) of students’ cognitive–affective states during their use of three different computer-based learning environments. Students’ cognitive–affective states are studied using different populations (Philippines, USA), different methods (quantitative field observation, self-report), and different types of learning environments (dialogue tutor, problem-solving game, and problem-solving-based Intelligent Tutoring System). By varying the studies along these multiple factors, we can have greater confidence that findings which generalize across studies are robust. The incidence, persistence, and impact of boredom, frustration, confusion, engaged concentration, delight, and surprise were compared. We found that boredom was very persistent across learning environments and was associated with poorer learning and problem behaviors, such as gaming the system. Despite prior hypothesis to the contrary, frustration was less persistent, less associated with poorer learning, and did not appear to be an antecedent to gaming the system. Confusion and engaged concentration were the most common states within all three learning environments. Experiences of delight and surprise were rare. These findings suggest that significant effort should be put into detecting and responding to boredom and confusion, with a particular emphasis on developing pedagogical interventions to disrupt the “vicious cycles” which occur when a student becomes bored and remains bored for long periods of time

Put your thinking cap on: Detecting cognitive load using EEG during learning

Current learning technologies have no direct way to assess students\u27 mental effort: are they in deep thought, struggling to overcome an impasse, or are they zoned out? To address this challenge, we propose the use of EEG-based cognitive load detectors during learning. Despite its potential, EEG has not yet been utilized as a way to optimize instructional strategies. We take an initial step towards this goal by assessing how experimentally manipulated (easy and difficult) sections of an intelligent tutoring system (ITS) influenced EEG-based estimates of students\u27 cognitive load. We found a main effect of task difficulty on EEG-based cognitive load estimates, which were also correlated with learning performance. Our results show that EEG can be a viable source of data to model learners\u27 mental states across a 90-minute session

Multimodal capture of teacher-student interactions for automated dialogic analysis in live classrooms

We focus on data collection designs for the automated analysis of teacher-student interactions in live classrooms with the goal of identifying instructional activities (e.g., lecturing, discussion) and assessing the quality of dialogic instruction (e.g., analysis of questions). Our designs were motivated by multiple technical requirements and constraints. Most importantly, teachers could be individually mic\u27ed but their audio needed to be of excellent quality for automatic speech recognition (ASR) and spoken utterance segmentation. Individual students could not be mic\u27ed but classroom audio quality only needed to be sufficient to detect student spoken utterances. Visual information could only be recorded if students could not be identified. Design 1 used an omnidirectional laptop microphone to record both teacher and classroom audio and was quickly deemed unsuitable. In Designs 2 and 3, teachers wore a wireless Samson AirLine 77 vocal headset system, which is a unidirectional microphone with a cardioid pickup pattern. In Design 2, classroom audio was recorded with dual firstgeneration Microsoft Kinects placed at the front corners of the class. Design 3 used a Crown PZM-30D pressure zone microphone mounted on the blackboard to record classroom audio. Designs 2 and 3 were tested by recording audio in 38 live middle school classrooms from six U.S. schools while trained human coders simultaneously performed live coding of classroom discourse. Qualitative and quantitative analyses revealed that Design 3 was suitable for three of our core tasks: (1) ASR on teacher speech (word recognition rate of 66% and word overlap rate of 69% using Google Speech ASR engine); (2) teacher utterance segmentation (F-measure of 97%); and (3) student utterance segmentation (F-measure of 66%). Ideas to incorporate video and skeletal tracking with dual second-generation Kinects to produce Design 4 are discussed

Comparing Learners’ Affect While Using an Intelligent Tutoring System and a Simulation Problem Solving Game

We compare the affect associated with an intelligent tutoring environment, Aplusix, and a simulations problem solving game, The Incredible Machine, to determine whether students experience significantly better affect in an educational game than in an ITS. We find that affect was, on the whole, better in Aplusix than it was in The Incredible Machine. Students experienced significantly less boredom and frustration and more flow while using Aplusix. This implies that, while aspects unique to games (e.g. fantasy and competition) may make games more fun, the interactivity and challenge common to both games and ITSs may play a larger role in making both types of systems affectively positive learning environment