Sequences of Frustration and Confusion, and Learning

We use sensor-free affect detection and a discovery with models approach to explore the relationship between affect over varying durations and learning outcomes among students using Cognitive Tutor Algebra. Researchers have suggested that the affective state of confusion can have positive effects on learning as long as students can resolve the confusion, and recent research seems to accord with this hypothesis. However, there is room for concern that some of this earlier work may have conflated frustration and confusion. We replicate these analyses using sensor-free automated detectors trained to distinguish the two affective states. Our analyses suggest that the effect may be stronger for frustration than confusion, but is strongest when these two affective states are taken together

Puzzle-solving activity as an indicator of epistemic confusion

When students perform complex cognitive activities, such as solving a problem, epistemic emotions can occur and influence the completion of the task. Confusion is one of these emotions and it can produce either negative or positive outcomes, according to the situation. For this reason, considering confusion can be an important factor for educators to evaluate students' progression in cognitive activities. However, in digital learning environments, observing students' confusion, as well as other epistemic emotions, can be problematic because of the remoteness of students. The study reported in this article explored new methodologies to assess emotions in a problem-solving task. The experimental task consisted of the resolution of logic puzzles presented on a computer, before, and after watching an instructional video depicting a method to solve the puzzle. In parallel to collecting self-reported confusion ratings, human-computer interaction was captured to serve as non-intrusive measures of emotions. The results revealed that the level of self-reported confusion was negatively correlated with the performance on solving the puzzles. In addition, while comparing the pre- and post-video sequences, the experience of confusion tended to differ. Before watching the instructional video, the number of clicks on the puzzle was positively correlated with the level of confusion whereas the correlation was negatively after the video. Moreover, the main emotions reported before the video (e.g., confusion, frustration, curiosity) tended to differ from the emotions reported after the videos (e.g., engagement, delight, boredom). These results provide insights into the ambivalent impact of confusion in problem-solving task, illustrating the dual effect (i.e., positive or negative) of this emotion on activity and performance, as reported in the literature. Applications of this methodology to real-world settings are discussed

Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

More than a cognitive experience: unfamiliarity, invalidation, and emotion in organizational learning

Literature on organizational learning (OL) lacks an integrative framework that captures the emotions involved as OL proceeds. Drawing on personal construct theory, we suggest that organizations learn where their members reconstrue meaning around questions of strategic significance for the organization. In this 5-year study of an electronics company, we explore the way in which emotions change as members perceive progress or a lack of progress around strategic themes. Our framework also takes into account whether OL involves experiences that are familiar or unfamiliar and the implications for emotions. We detected similar patterns of emotion arising over time for three different themes in our data, thereby adding to OL perspectives that are predominantly cognitive in orientation

Modular knowledge systems accelerate human migration in asymmetric random environments

Migration is a key mechanism for expansion of communities. In spatially heterogeneous environments, rapidly gaining knowledge about the local environment is key to the evolutionary success of a migrating population. For historical human migration, environmental heterogeneity was naturally asymmetric in the north-south (NS) and east-west (EW) directions. We here consider the human migration process in the Americas, modeled as random, asymmetric, modularly correlated environments. Knowledge about the environments determines the fitness of each individual. We present a phase diagram for asymmetry of migration as a function of carrying capacity and fitness threshold. We find that the speed of migration is proportional to the inverse complement of the spatial environmental gradient, and in particular we find that north-south migration rates are lower than east-west migration rates when the environmental gradient is higher in the north-south direction. Communication of knowledge between individuals can help to spread beneficial knowledge within the population. The speed of migration increases when communication transmits pieces of knowledge that contribute in a modular way to the fitness of individuals. The results for the dependence of migration rate on asymmetry and modularity are consistent with existing archaeological observations. The results for asymmetry of genetic divergence are consistent with patterns of human gene flow.Comment: 13 pages, 6 figures, 1 table in Proc. Roy. Soc. Interface 201

E3: Emotions, Engagement, and Educational Digital Games

The use of educational digital games as a method of instruction for science, technology, engineering, and mathematics has increased in the past decade. While these games provide successfully implemented interactive and fun interfaces, they are not designed to respond or remedy students’ negative affect towards the game dynamics or their educational content. Therefore, this exploratory study investigated the frequent patterns of student emotional and behavioral response to educational digital games. To unveil the sequential occurrence of these affective states, students were assigned to play the game for nine class sessions. During these sessions, their affective and behavioral response was recorded to uncover possible underlying patterns of affect (particularly confusion, frustration, and boredom) and behavior (disengagement). In addition, these affect and behavior frequency pattern data were combined with students’ gameplay data in order to identify patterns of emotions that led to a better performance in the game. The results provide information on possible affect and behavior patterns that could be used in further research on affect and behavior detection in such open-ended digital game environments. Particularly, the findings show that students experience a considerable amount of confusion, frustration, and boredom. Another finding highlights the need for remediation via embedded help, as the students referred to peer help often during their gameplay. However, possibly because of the low quality of the received help, students seemed to become frustrated or disengaged with the environment. Finally, the findings suggest the importance of the decay rate of confusion; students’ gameplay performance was associated with the length of time students remained confused or frustrated. Overall, these findings show that there are interesting patterns related to students who experience relatively negative emotions during their gameplay

Affective learning: improving engagement and enhancing learning with affect-aware feedback

This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students’ affective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students’ affective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students’ performance (the non-affect condition). Our results suggest that affect-aware support contributes to reducing boredom and off-task behavior, and may have an effect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that affect-aware modelling plays in the adaptive delivery of formative feedback to support learning

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)