There to date exists limited research on how emotion regulation shapes students’ emotional experiences and academic development in higher education. The purpose of this study was to address this gap by examining how students in STEM (Science, Technology, Engineering, Math) degree programs ( N = 174) use emotion regulation strategies related to their achievement emotions, approaches to learning, and exam performance. Data was collected across four phases pertaining to a required STEM course at the beginning of the semester, while studying for a midterm exam, and within 48 h following the exam. Results suggested that while emotion regulation while studying did not predict students’ emotions more than control and value appraisals, their emotion regulation specific to the exam predicted their emotions above and beyond control and value appraisals. Findings also showed cognitive reappraisal to correspond with more positive emotions, less negative emotions, more complex approaches to learning, and better exam performance. Conversely, suppression was associated with poorer exam performance. Results additionally showed that associations between cognitive reappraisal and emotions were stronger during exams than while studying. Overall, these findings indicate that how post-secondary students choose to regulate their emotions in STEM degree programs has important implications for how they feel, learn, and perform.

Test anxiety is a widespread and mostly detrimental emotion in learning and achievement settings. Thus, it is a construct of high interest for researchers and its measurement is an important issue. So far, test anxiety has typically been assessed using self-report measures. However, physiological measures (e.g., heart rate or skin conductance level) have gained increasing attention in educational research, as they allow for an objective and often continuous assessment of students' physiological arousal (i.e., the physiological component of test anxiety) in real-life situations, such as a test. Although theoretically one would assume self-report measures of test anxiety and objective physiological measures would converge, empirical evidence is scarce and findings have been mixed. To achieve a more coherent picture of the relationship between these measures, this systematic review and meta-analysis investigated whether higher self-reported test anxiety is associated with expected increases in objectively measured physiological arousal. A systematic literature search yielded an initial 231 articles, and a structured selection process identified 29 eligible articles, comprising 31 studies, which met the specified inclusion criteria and provided sufficient information about the relationship under investigation. In line with theoretical models, in 21 out of the 31 included studies, there was a significant positive relationship between self-reported test anxiety and physiological arousal. The strengths of these correlations were of medium size. Moderators influencing the relation between these two measures are discussed, along with implications for the assessment of physiological data in future classroom-based research on test anxiety.

BACKGROUND AND OBJECTIVES:Although anxiety consists of multiple components, including cognitive, affective, motivational, and physiological, and some findings suggest that there might be differences regarding their control antecedents and effects on performance, previous studies have largely neglected to examine these components separately and for reasons of convenience often assessed test anxiety as a unified construct using a single-item. Therefore, this study investigated the different test anxiety components with the goal to: (1) examine the relative impact of the anxiety components in the mediating mechanism that connects control and performance - as proposed by Pekrun's control-value theory, and (2) determine which specific anxiety component is underlying common single-item anxiety measures. METHODS:The research questions were investigated using an intra-individual approach in a sample of N = 137 German 8th graders during a mathematics exam. RESULTS:As expected, control was negatively related to all anxiety components, but associations varied in strength. Additionally, the components differed in their relative impact on performance, with the cognitive component being central for this outcome. Furthermore, common single-item measures seem to specifically assess the affective component, and thus not the component most relevant for test performance. CONCLUSION:Consequently, our study strongly recommends to distinguish between the anxiety components depending on the research question at hand.

There is a lack of theory-driven empirical research that evaluates outcomes of location-based augmented reality (AR) applications with the purpose of improving instructional design and use guidelines. The primary aim of this study was to compare the effectiveness of two historical reasoning guide protocols, one based on prior research by Harley and colleagues (2016a; the other an extension) while learners used a mobile AR app to learn about history. Learners reported significantly higher levels of enjoyment and curiosity from learning about history than using the app itself, though mean levels were high for both-in contrast to negative emotions. Results suggest that the new and extended historical reasoning guide protocol succeeded in fostering higher levels of knowledge than the former. Findings also revealed that learners reported significantly higher levels of task value after the guided tour compared to their pre-guided-tour responses. Implications and future directions are discussed.

Despite the importance of emotion regulation in education there is a paucity of research examining it in authentic educational contexts. Moreover, emotion measurement continues to be dominated by self-report measures. We address these gaps in the literature by measuring emotion regulation and activation in 37 medical students’ who were solving medical cases using BioWorld, a computer based learning environment. Specifically, we examined students’ habitual use of emotion regulation strategies as well as electrodermal activation (emotional arousal) from skin conductance level (SCL) or skin conductance response (SCR), as well as appraisals of control and value and self-reported emotional responses during a diagnostic reasoning task in Bioworld. Our results revealed that medical students reported significantly higher habitual levels of reappraisal than suppression ER strategies. Higher habitual levels of reappraisal significantly and positively predicted learners’ self-reported pride. On the other hand, higher habitual levels of suppression significantly and positively predicted learners’ self-reported anxiety, shame, and hopelessness. Results also revealed that medical students experienced relatively low SCLs and few SCRs while interacting with Bioworld. Habitual suppression strategies significantly and positively predicted medical students’ SCLs, while SCRs significantly and positively predicted their diagnostic efficiency. Findings also revealed a significant, positive predictive relationship between SCL and shame and anxiety and the inverse relationship between SCL and task value. Implications and future directions are discussed.

This report served as background paper for the 13th Session of the Joint ILO–UNESCO Committee of Experts on the Application of the Recommendations concerning Teaching Personnel(CEART). Assessing teaching to identify opportunities for professional development and for evaluative purposes are essential for ensuring quality learning and teaching in higher education. The purpose of this paper is to discuss these challenges and issues and to propose recommendations for supporting quality assessments of teaching.

There is an active strand of research on how affect and self-regulatory activities influence performance and learning outcomes, but the mechanisms through which they interact during learning remain poorly understood. Additionally, these constructs have been under-researched in medical education. Using multimodal data in the context of a clinical reasoning task for medical students learning case diagnosis, we explored the temporal nature of cognition, affect, motivation, and self-regulation. With a sample of n = 10 medical students, we collected data on self-regulated learning (SRL) processes through think-aloud analyses; emotion data through facial expressions; and achievement goal orientations and habitual emotion regulation strategies through self-report questionnaires. Results from our sequential data mining techniques and quantitative analyses suggested that highperforming medical students (who arrived at an accurate diagnosis) differed from low-performing students (who did not arrive at correct diagnosis): Low performers reported higher performance goal orientation, and expressed more emotions overall, than high performers. High performers exhibited marginally more monitoring SRL behaviours, while low performers tended to orient and reorient throughout the task. High and low performers also differed on the co-occurrences of, and sequential transitions between, emotions and SRL behaviours. The implications of these findings with respect to research and medical education are discussed. ABSTRACT There is an active strand of research on how affect and self-regulatory activities influence performance and learning outcomes, but the mechanisms through which they interact during learning remain poorly understood. Additionally, these constructs have been under-researched in medical education. Using multimodal data in the context of a clinical reasoning task for medical students learning case diagnosis, we explored the temporal nature of cognition, affect, motivation, and self-regulation. With a sample of n = 10 medical students, we collected data on self-regulated learning (SRL) processes through think-aloud analyses; emotion data through facial expressions; and achievement goal orientations and habitual emotion regulation strategies through self-report questionnaires. Results from our sequential data mining techniques and quantitative analyses suggested that highperforming medical students (who arrived at an accurate diagnosis) differed from low-performing students (who did not arrive at correct diagnosis): Low performers reported higher performance goal orientation, and expressed more emotions overall, than high performers. High performers exhibited marginally more monitoring SRL behaviours, while low performers tended to orient and reorient throughout the task. High and low performers also differed on the co-occurrences of, and sequential transitions between, emotions and SRL behaviours. The implications of these findings with respect to research and medical education are discussed.

This article offers a critical review of several influential emotion theories and emotion regulation models in terms of their utility for explaining how, when, and why students regulate their achievement emotions. Based on this review, we propose a novel framework for the regulation of achievement emotions. This framework is based on the premise that student learning and achievement is influenced by both achievement emotions and efforts to regulate these emotions. The framework further proposes that emotion regulation decisions, namely, the identification, selection, and implementation of regulatory strategies, are shaped by 5 antecedent factors: emotion-outcome expectancies, motives for emotion regulation, implicit beliefs about emotions, emotion regulation self-efficacy, and emotion regulation aptitude. The theoretical and practical implications of this framework are discussed.

Achievement emotions have a powerful influence on how students interact with current and future learning and performance tasks. As such, pedagogical practices that support adaptive student emotions are critical for teaching and learning in computer-based learning environments (CBLEs). This research investigates the relationship between during-task achievement emotions and participants' appraisals of task control, value, perceived performance, and actual performance outcomes on a diagnostic reasoning task with a CBLE, BioWorld. Based on the emotions participants reported experiencing during the task, we found that participants could be organized into three groups using a k-means cluster analysis: a positive, negative, and low emotion group. Participants assigned to the positive emotion group had the highest subjective appraisals of task value, task control, and the highest perceived performance; however, these participants had lower levels of actual performance when compared to learners assigned to the low emotion cluster and had actual performance levels comparable to learners in the negative emotion cluster. These results provide preliminary evidence for fostering low emotionality rather than positive emotionality with pedagogical interventions in order to support better performance outcomes, while learners engage in academic achievement tasks in CBLEs.

Research on the effectiveness of augmented reality (AR) on learning exists, but there is a paucity of empirical work that explores the role that positive emotions play in supporting learning in such settings. To address this gap, this study compared undergraduate students' emotions and learning outcomes during a guided historical tour using mobile AR applications. Data was collected in a laboratory (Study 1; N = 13) and outdoors (Study 2; N = 18) from thirty-one undergraduate students at a large North American university. Our findings demonstrated that learners were able to effectively and enjoyably learn about historical differences between past and present historical locations by contextualizing their visual representations, and that the two mobile AR apps were effective both in and outside of the laboratory. Learners were virtually situated in the historical location in Study 1 and physically visited the location in Study 2. In comparing results between studies, findings revealed that learners were able to identify more differences outdoors and required less scaffolding to identify differences. Learners reported high levels of enjoyment throughout both studies, but more enjoyment and less boredom in the outdoor study. Eye tracking results from Study 1 indicated that learners frequently compared historical information by switching their gaze between mobile devices and a Smart Board, which virtually situated them at the historical location. Results enhance our understanding of AR applications' effectiveness in different contexts (virtual and location-based). Design recommendations for mobile AR apps are discussed.

Introduction Learner modeling is a critical component in the process of adapting instruction to the specific needs of different learners with computerized systems. Adaptive instructional systems may be defined as a systematic process which consists of four steps: (1) capturing information about the learner; (2) analyzing learner interactions through a model of learner characteristics in relation to the domain; (3) selecting the appropriate instructional content and resources; and (4) delivering the content to the learner (Shute & Zapata-Rivera, 2012). The analytical function of the learner model can be further classified in terms of processes conducted at both the macro and micro levels (VanLehn, 2006). At the macro-level, a representation of the path towards competency within the domain is updated for each task with the aim of selecting the next task that is the most appropriate for the learner. At the micro-level, instructional materials such as hints and feedback are delivered to the learner on the basis of a representation that is repeatedly updated over the duration of task performance. Intelligent tutoring systems have been shown to improve upon typical classroom instruction in several disciplines, including mathematics (e.g., Cognitive Tutors; Koedinger & Corbett, 2006), computer science (e.g., Constraint-Based Tutors; Mitrovic, 2003), microbiology (e.g., Narrative-Based Tutors; Rowe, Shores, Mott, & Lester, 2011), as well as physics and computer literacy (e.g., Dialogue-Based Tutors; Graesser, VanLehn, Rose, Jordan, & Harter, 2001). The challenge in modeling learning in the context of solving ill-structured problems is that there are multiple paths towards attaining the correct solution (Lajoie, 2003, 2009). These paths should be documented in order to represent common misconceptions or impasses in moving along the trajectory towards competency. In the medical domain, experts have been shown to diagnose patient diseases in different ways, while at the same time, justifying plausible hypotheses on the basis of common evidence items, including patient symptoms and lab test information (Gauthier & Lajoie, 2014; Lajoie, Gauthier, & Lu, 2009). An intelligent tutoring system such as BioWorld can represent these evidence items in terms of a novice-expert overlay system, which compares novice solution paths to those of the experts in order to individualize feedback. A similar modeling technique has been applied in other systems, such as SlideTutor in diagnosing dermatopathology (Feyzi-Behnagh, Azevedo, Legowski, Reitmeyer, Tseytlin, & Crowley, 2014) as well as the MedU virtual patient cases (Berman, Fall, Chessman, Dell, Lang, Leong, Nixon, & Smith, 2011). On the basis of the user interactions that are recorded by BioWorld, the system will highlight areas of similarities and differences with the expert solution path, allowing novices to self-reflect on their own approach to resolving the problem (Lajoie & Poitras, 2014). Several studies investigating the novice-expert overlay model have been carried out with the aim of capturing linguistic features from written case summaries to tailor feedback content (Poitras, Doleck, & Lajoie, 2014; Lajoie, Poitras, Doleck, & Jarrell, 2015) as well as the impact of goal-orientations and affective reactions towards attention given to feedback (Lajoie, Naismith, Poitras, Hong, Panesso-Cruz, Ranelluci, & Wiseman, 2013). In this study, we examine the use of subgroup discovery for the induction of rules that characterize the relationship between impasses in problem-solving and lab-tests ordered in BioWorld. Whereas our previous research characterized how experts converged in their paths to solving a problem, the present study captures how novices diverged from an expert solution path. In doing so, we claim that subgroup discovery algorithms are particularly well suited towards describing the multiple paths that characterize problem-solving in ill-structured domains. …

Clinical reasoning is a central skill in diagnosing cases. However, diagnosing a clinical case poses several challenges that are inherent to solving multifaceted ill-structured problems. In particular, when solving such problems, the complexity stems from the existence of multiple paths to arriving at the correct solution (Anonymous, 2003). Moreover, the approach one employs in diagnosing a clinical case is in some measure dependent upon the complexity of the case. This leads us to the question: Are there differences in the manner in which novices solve cases with varying levels of complexity in a computer based learning environment? More specifically, we are interested in understanding and elucidating if there are clinical reasoning differences in regards to accuracy, efficiency, and process across three virtual patient cases of varying difficulty levels. Examining such differences may have implications from both a learner modeling and system enhancement perspective. We close by discussing the implications for practice, limitations of the study, and future research directions.

This paper is motivated by two observations on computer-supported education: First, there has been growing availability, rapid proliferation, and increased diversity of learner-system educational data. Second, advances in learning analytics and data mining have facilitated and spawned a variety of novel investigations using such data. Driven by these complementary trends, the present work is geared towards exploring knowledge-based discovery approaches in understanding learner-system usage data. More specifically, with an eye toward tracing and comprehending learner behaviors in a medical intelligent tutoring system, we explore the utility of Process Mining, in understanding the problem solving trajectories of students in a medical computer-based learning environment.

Although cognitive errors (i.e., premature closure, faulty data gathering, and faulty knowledge) are the main reasons for making diagnostic mistakes, the mechanisms by which they occur are difficult to isolate in clinical settings. Computer-based learning environments (CBLE) offer the opportunity to train medical students to avoid cognitive errors by tracking the onset of these errors. The purpose of this study is to explore cognitive errors in a CBLE called Bio-World. A logistic regression was fitted to learner behaviors that characterize premature closure in order to predict diagnostic performance. An ANOVA was used to assess if participants who were highly confident in their wrong diagnosis engaged in more faulty data gathering via confirmation bias. Findings suggest that diagnostic mistakes can be predicted from faulty knowledge and faulty data gathering and indicate poor metacognitive awareness. This study supports the notion that to improve diagnostic performance medical education programs should promote metacognitive skills.

Technology Rich Learning Environments (TREs) are increasingly used to support scholastic activities. BioWorld is an example of a TRE designed to support the metacognitive activities of learners tasked with solving virtual patient cases. The present paper aims to examine the performance differences of novice physicians in diagnosing cases in BioWorld. We present an empirically guided line of research concerning the performance differences: (1) across three endocrinology cases, (2) between genders, (3) between goal orientations, and (4) in diagnosis correctness.

The purpose of this research is to understand achievement emotions resulting from performance feedback in a medical education context where 30 first and second year medical students learned to diagnose virtual patients in an intelligent tutoring system (ITS), BioWorld. We found that students could be organized into groups using cluster analyses based on the emotions they reported after receiving performance feedback: a positive emotion cluster, negative emotion cluster, and low overall emotion cluster. Medical students in the positive achievement emotion cluster had the highest performance on the diagnostic reasoning cases; those in the negative achievement emotion cluster had the lowest performance; and students categorized as belonging to the low overall achievement emotion cluster had mean performance levels that fell between the two. From the results we propose critical performance thresholds that can be used to predict emotions following performance feedback.

Medical diagnostic reasoning is ill-defined and complex, requiring novice physicians to monitor and control their problem-solving efforts. Self-regulation is critical for effective medical problem-solving, helping individuals progress towards a correct diagnosis through a series of actions that informs subsequent ones. BioWorld is a computer-based learning environment designed to support novices in developing medical diagnostic reasoning as they receive feedback in the context of solving virtual cases. The system provides tools that scaffold learners in their requisite cognitive and metacognitive activities. Novices attain higher levels of competence as the system dynamically assesses their performance against expert solution paths. Dynamic assessment in this system relies on a novice-expert overlay and it is used to develop feedback when novices request help. When help-seeking occurs, help is provided by the tutoring module which applies a set of pre-defined rules based on the context of the learner's activity. The system also provides cumulative feedback by comparing the novice solution with an expert solution following completion of the case. This chapter covers the essential design guidelines of this scaffolding approach to metacognitive activities in problem-solving within the domain of medical education. Specifically, we review recent advances in modeling metacognition through online measures, including concurrent think-aloud protocols, video-screen captures, and log-file entries. Educational data mining techniques are outlined with the goals of capturing metacognitive activities as they unfold throughout problem solving, and guiding the design of scaffolding tools in order to promote higher levels of competence in novices.