Blair Lehman

Bio: Blair Lehman is an academic researcher from University of Memphis. The author has contributed to research in topics: TUTOR & Intelligent tutoring system. The author has an hindex of 15, co-authored 29 publications receiving 1318 citations. Previous affiliations of Blair Lehman include Rhodes College & Educational Testing Service.

Confusion and complex learning during interactions with computer learning environments

Abstract: Folk wisdom holds that being confused is detrimental to learning. However, research on emotions and learning suggest a somewhat more complex relationship between confusion and learning outcomes. In fact, it has been proposed that impasses that trigger states of cognitive disequilibrium and confusion can create opportunities for deep learning of conceptually difficult content. This paper discusses four computer learning environments that either naturally or artificially induce confusion in learners in order to create learning opportunities. First, an Intelligent Tutoring System called AutoTutor that engenders confusion through challenging problems and vague hints is described. The remaining three environments were specifically designed to induce confusion through a number of different interventions. These interventions include device breakdowns, contradictory information, and false feedback. The success and limitations of confusion induction and the impact of confusion resolution on learning are discussed. Potential methods to help learners productively manage their confusion instead of being hopelessly confused are also discussed.

A time for emoting: when affect-sensitivity is and isn't effective at promoting deep learning

Abstract: We have developed and evaluated an affect-sensitive version of AutoTutor, a dialogue based ITS that simulates human tutors. While the original AutoTutor is sensitive to learners' cognitive states, the affect-sensitive tutor is responsive to their affective states as well. This affective tutor automatically detects learners' boredom, confusion, and frustration by monitoring conversational cues, gross body language, and facial features. The sensed affective states guide the tutor's responses in a manner that helps students regulate their negative emotions. The tutor also synthesizes affect via the verbal content of its responses and the facial expressions and speech of an embodied pedagogical agent. An experiment comparing the affect-sensitive and non-affective tutors indicated that the affective tutor improved learning for low-domain knowledge students, particularly at deeper levels of comprehension. We conclude by discussing the conditions upon which affect-sensitivity is effective, and the conditions when it is not.

Monitoring affect states during effortful problem solving activities

Abstract: We explored the affective states that students experienced during effortful problem solving activities. We conducted a study where 41 students solved difficult analytical reasoning problems from the Law School Admission Test. Students viewed videos of their faces and screen captures and judged their emotions from a set of 14 states (basic emotions, learning-centered emotions, and neutral) at relevant points in the problem solving process (after new problem is displayed, in the midst of problem solving, after feedback is received). The results indicated that curiosity, frustration, boredom, confusion, happiness, and anxiety were the major emotions that students experienced, while contempt, anger, sadness, fear, disgust, eureka, and surprise were rare. Follow-up analyses on the temporal dynamics of the emotions, their contextual underpinnings, and relationships to problem solving outcomes supported a general characterization of the affective dimension of problem solving. Affective states differ in: (a) their probability of occurrence as regular, routine, or sporadic emotions, (b) their temporal dynamics as persistent or random emotions, (c) their characterizations as product or process related emotions, and (d) whether they were positively or negatively related to problem solving outcomes. A synthesis of our major findings, limitations, resolutions, and implications for affect-sensitive artificial learning environments are discussed.

What Are You Feeling? Investigating Student Affective States During Expert Human Tutoring Sessions

Abstract: One-to-one tutoring is an extremely effective method for producing learning gains in students and for contributing to greater understanding and positive attitudes towards learning. However, learning inevitably involves failure and a host of positive and negative affective states. In an attempt to explore the link between emotions and learning this research has collected data on student affective states and engagement levels during high stakes learning in one-to-one expert tutoring sessions. Our results indicate that only the affective states of confusion, happiness, anxious, and frustration occurred at significant levels. We also investigated the extent to which expert tutors adapt their pedagogical and motivational strategies in response to learners' affective and cognitive states.

Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications

Abstract: This survey describes recent progress in the field of Affective Computing (AC), with a focus on affect detection. Although many AC researchers have traditionally attempted to remain agnostic to the different emotion theories proposed by psychologists, the affective technologies being developed are rife with theoretical assumptions that impact their effectiveness. Hence, an informed and integrated examination of emotion theories from multiple areas will need to become part of computing practice if truly effective real-world systems are to be achieved. This survey discusses theoretical perspectives that view emotions as expressions, embodiments, outcomes of cognitive appraisal, social constructs, products of neural circuitry, and psychological interpretations of basic feelings. It provides meta-analyses on existing reviews of affect detection systems that focus on traditional affect detection modalities like physiology, face, and voice, and also reviews emerging research on more novel channels such as text, body language, and complex multimodal systems. This survey explicitly explores the multidisciplinary foundation that underlies all AC applications by describing how AC researchers have incorporated psychological theories of emotion and how these theories affect research questions, methods, results, and their interpretations. In this way, models and methods can be compared, and emerging insights from various disciplines can be more expertly integrated.

Finding Deceptive Opinion Spam by Any Stretch of the Imagination

Abstract: Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam---fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.

Handbook of Self-Regulation of Learning and Performance

Abstract: Contents Historical, Contemporary, and Future Perspectives on Self-Regulated Learning and Performance Dale H. Schunk and Jeffrey A. Greene Section I. Basic Domains of Self-Regulation of Learning and Performance Social Cognitive Theoretical Perspective of Self-Regulation Ellen L. Usher and Dale H. Schunk Cognition and Metacognition Within Self-Regulated Learning Philip H. Winne Developmental Trajectories of Skills and Abilities Relevant for Self-Regulation of Learning and Performance Rick H. Hoyle and Amy L. Dent Motivation and Affect in Self-Regulated Learning: Does Metacognition Play a Role? Anastasia Efklides, Bennett L. Schwartz, and Victoria Brown Self-Regulation, Co-Regulation and Shared Regulation in Collaborative Learning Environments Allyson Hadwin, Sanna Jarvela, and Mariel Miller Section II. Self-Regulation of Learning and Performance in Context Metacognitive Pedagogies in Mathematics Classrooms: From Kindergarten to College and Beyond Zemira R. Mevarech, Lieven Verschaffel, and Erik De Corte Self-Regulated Learning in Reading Keith W. Thiede and Anique B. H. de Bruin Self-Regulation and Writing Steve Graham, Karen R. Harris, Charles MacArthur, and Tanya Santangelo The Self-Regulation of Learning and Conceptual Change in Science: Research, Theory, and Educational Applications Gale M. Sinatra and Gita Taasoobshirazi Using Technology-Rich Environments to Foster Self-Regulated Learning in the Social Studies Eric G. Poitras and Susanne P. Lajoie Self-Regulated Learning in Music Practice and Performance Gary E. McPherson, Peter Miksza, and Paul Evans Self-Regulation in Athletes: A Social Cognitive Perspective Anastasia Kitsantas, Maria Kavussanu, Deborah B. Corbatto, and Pepijn K. C. van de Pol Self-Regulation: An Integral Part of Standards-Based Education Marie C. White and Maria K. DiBenedetto Teachers as Agents in Promoting Students' SRL and Performance: Applications for Teachers' Dual-Role Training Program Bracha Kramarski Section III. Technology and Self-Regulation of Learning and Performance Emerging Classroom Technology: Using Self-Regulation Principles as a Guide for Effective Implementation Daniel C. Moos Understanding and Reasoning About Real-Time Cognitive, Affective, and Metacognitive Processes to Foster Self-Regulation With Advanced Learning Technologies Roger Azevedo, Michelle Taub, and Nicholas V. Mudrick The Role of Self-Regulated Learning in Digital Games John L. Nietfeld Self-Regulation of Learning and Performance in Computer-Supported Collaborative Learning Environments Peter Reimann and Maria Bannert Section IV. Methodology and Assessment of Self-Regulation of Learning and Performance Validity and the Use of Self-Report Questionnaires to Assess Self-Regulated Learning Christopher A. Wolters and Sungjun Won Capturing and Modeling Self-Regulated Learning Using Think-Aloud Protocols Jeffrey A. Greene, Victor M. Deekens, Dana Z. Copeland, and Seung Yu Assessing Self-Regulated Learning Using Microanalytic Methods Timothy J. Cleary and Gregory L. Callan Advancing Research and Practice About Self-Regulated Learning: The Promise of In-Depth Case Study Methodologies Deborah L. Butler and Sylvie C. Cartier Examining the Cyclical, Loosely Sequenced, and Contingent Features of Self-Regulated Learning: Trace Data and Their Analysis Matthew L. Bernacki Data Mining Methods for Assessing Self-Regulated Learning Gautam Biswas, Ryan S. Baker, and Luc Paquette Section V. Individual and Group Differences in Self-Regulation of Learning and Performance 26. Calibration of Performance and Academic Delay of Gratification: Individual and Group Differences in Self-Regulation of Learning Peggy P. Chen and Hefer Bembenutty 27. Academic Help Seeking as a Self-Regulated Learning Strategy: Current Issues, Future Directions Stuart A. Karabenick and Eleftheria N. Gonida 28. The Three Faces of Epistemic Thinking in Self-Regulated Learning Krista R. Muis and Cara Singh 29. Advances in Understanding Young Children's Self-Regulation of Learning Nancy E. Perry, Lynda R. Hutchinson, Nikki Yee, and Elina Maatta 30. Self-Regulation: Implications for Individuals With Special Needs Linda H. Mason and Robert Reid 31. Culture and Self-Regulation in Educational Contexts Dennis M. McInerney and Ronnel B. King

The Influences of Emotion on Learning and Memory.

Abstract: Emotion has a substantial influence on the cognitive processes in humans, including perception, attention, learning, memory, reasoning, and problem solving. Emotion has a particularly strong influence on attention, especially modulating the selectivity of attention as well as motivating action and behavior. This attentional and executive control is intimately linked to learning processes, as intrinsically limited attentional capacities are better focused on relevant information. Emotion also facilitates encoding and helps retrieval of information efficiently. However, the effects of emotion on learning and memory are not always univalent, as studies have reported that emotion either enhances or impairs learning and long-term memory (LTM) retention, depending on a range of factors. Recent neuroimaging findings have indicated that the amygdala and prefrontal cortex cooperate with the medial temporal lobe in an integrated manner that affords (i) the amygdala modulating memory consolidation; (ii) the prefrontal cortex mediating memory encoding and formation; and (iii) the hippocampus for successful learning and LTM retention. We also review the nested hierarchies of circular emotional control and cognitive regulation (bottom-up and top-down influences) within the brain to achieve optimal integration of emotional and cognitive processing. This review highlights a basic evolutionary approach to emotion to understand the effects of emotion on learning and memory and the functional roles played by various brain regions and their mutual interactions in relation to emotional processing. We also summarize the current state of knowledge on the impact of emotion on memory and map implications for educational settings. In addition to elucidating the memory-enhancing effects of emotion, neuroimaging findings extend our understanding of emotional influences on learning and memory processes; this knowledge may be useful for the design of effective educational curricula to provide a conducive learning environment for both traditional "live" learning in classrooms and "virtual" learning through online-based educational technologies.