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The present exploratory study evaluated perceptions of actual support received in relation to stress and well-being among first-year students attending Canadian and U.S. higher education institutions (N = 126). Given that traditional assessments of received support account only for how often support was received, the present research examined unique effects of support quality in addition to frequency with respect to four distinct sources of support (family, friends, faculty/staff, institution). Following from empirical confirmation of received support frequency (RSF) and received support quality (RSQ) as distinguishable constructs, RSQ was found to significantly mediate effects of RSF across varied well-being outcomes (e.g., stress, burnout, quitting intentions) in relation to family, faculty/staff, and institution support. Overall, study findings highlight the importance of evaluating the quality of support received by first-year students during the transition to higher education and show faculty/staff support to be an important contributor to student well-being.

Although the effectiveness and experiences of computer-based examinations is a widely investigated area of research, the question of whether and how computer-based assessment limits or heightens the experience of negative test emotions remains largely unexamined. Drawing from the control-value theory of achievement emotions, we investigated undergraduate students' emotions during an authentic, course-based assessment in a computer-based testing environment, as well as predictors and outcomes associated with their emotions. We found that students (N = 74) in a computer-based testing environment reported lower levels of negative emotions than their typical negative test emotions. Females and males performed equally in the examination, yet females reported higher retrospective negative emotions. Consistently, females reported higher levels of typical test-taking anxiety in prior examinations, but they reported lower anxiety in a computer-based environment. Finally, although typical and retrospective emotions were correlated, only retrospective emotions were associated with examination performance. We discuss the importance of testing environments and time-frames in understanding how to support students' emotions in testing with particular emphasis on implications for online assessment.

Mobile apps take advantage of the ubiquity of mobile phones and can be used to share unique pedagogical experiences with multimedia content not yet available in curriculums. This preliminary study used a quasi-experimental pretest-posttest design to examine changes in self-reported empathy toward sexual orientation and gender identity (SOGI) minority people. We also report on the associations between gender and a physiological measure of emotional activation, skin conductance level (SCL), on self-reported empathy. The main results of this study that examined 57 undergraduate students at a Canadian University whom interacted with a queer history app individually were the following: Preliminary evidence that (1) students' empathy toward SO and GI minorities can be measured using a modified version of the Scale of Ethnocultural Empathy (SEE). (2) Statistically significant increases in empathy toward SO and GI minorities pre to post app interaction. (3) Students' pre- and post-empathy levels were statistically significantly higher toward SO than GI minorities. (4) Female students had statistically significantly higher self-reported empathy toward SO and GI minorities than males. (5) Male students had statistically significantly higher SCL than females. (6) Statistically significant interaction between SCL grouping and questionnaire administration on GI minority empathy. Findings and implications are discussed in lieu of the contributions that mobile apps can play to support social change, in particular, by fostering empathy.

Learning analytics (LA) incorporates analyzing cognitive, social and emotional processes in learning scenarios to make informed decisions regarding instructional design and delivery. Research has highlighted important roles that emotions play in learning. We have extended this field of research by exploring the role of emotions in a relatively uncommon learning scenario: learning about queer history with a multimedia mobile app. Specifically, we used an automatic facial recognition software (FaceReader 7) to measure learners' discrete emotions and a counter-balanced multiple-choice quiz to assess learning. We also used an eye tracker (EyeLink 1000) to identify the emotions learners experienced while they read specific content, as opposed to the emotions they experienced over the course of the entire learning session. A total of 33 out of 57 of the learners' data were eligible to be analyzed. Results revealed that learners expressed more negative-activating emotions (ie, anger, anxiety) and negative-deactivating emotions (ie, sadness) than positive-activating emotions (ie, happiness). Learners with an angry emotion profile had the highest learning gains. The importance of examining typically undesirable emotions in learning, such as anger, is discussed using the control-value theory of achievement emotions. Further, this study describes a multimodal methodology to integrate behavioral trace data into learning analytics research.

Pre-service teachers' conceptions of effective and ineffective instruction stand to inform their personal views of what constitutes effective and ineffective instruction, yet few qualitative studies have examined both conceptions of effective and ineffective instruction. The purpose of this study was to determine whether pre-service teachers described what happens in university courses primarily in terms of teacher characteristics, teaching practices, or instructional context. There were two research questions guiding the study. First, how are the dimensions of effective and ineffective instruction alike and different? Second, how do results correspond to similar qualitative studies? Nine distinct themes were inductively derived through open coding of 34 preservice teachers' essays: (a) motivation, (b) student autonomy, (c) meaningful learning, (d) comfortable learning environment, (e) classroom management, (f) student-teacher relationship, (g) teacher's personal characteristics and manner, (h) lesson organization, and (i) teacher impact/student development. The results of this study support previous findings and add to the small number of studies that have examined pre-service teachers' descriptions of effective and ineffective instruction. Findings have also contributed a new category that has not appeared in previous literature: teacher impact/student development. Pre-service teachers' descriptions in this study confirm that the theoretical conception of what happens in classrooms must include the teacher's characteristics, teaching, and the context of instruction.

The purpose of this study is to experimentally manipulate discourse processes hypothesized to impact the emotions students experience when interacting with handheld augmented reality devices in informal learning settings. Research conducted in the field is often limited by practical constraints, requiring heavy investments in time and resources to collect data from large samples of students. To demonstrate the feasibility of our proposed method, a guided walking tour with 60 students using a location-based augmented reality app was simulated in the context of a controlled laboratory setting. The difference between groups of students clustered into distinct profiles of positive and negative self-reported emotions was attributed to patterns in the mined dialog between students and tour guide. Furthermore, student engagement predicted the ability to recall topics covered in the tour. We discuss the implications and directions for future research in tour simulations conducted in a laboratory setting as a means to evaluate the role of mobile technologies in enhancing learning and desirable emotions.

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. [Manuscript presented at AERA 2017.]

In this paper, we present the results obtained using a clustering algorithm (Expectation-Maximization) on data collected from 106 college students learning about the circulatory system with MetaTutor, an agent-based Intelligent Tutoring System (ITS) designed to foster self-regulated learning (SRL). The three extracted clusters were validated and analyzed using multivariate statistics (MANOVAs) in order to characterize three distinct profiles of students, displaying statistically significant differences over all 12 variables used for the clusters formation (including performance, use of note-taking and number of sub-goals attempted). We show through additional analyses that variations also exist between the clusters regarding prompts they received by the system to perform SRL processes. We conclude with a discussion of implications for designing a more adaptive ITS based on an identification of learners' profiles.

Achievement emotions are critical because of their impact on success and failure in important domains such as learning. These emotions may be modified via emotion regulation (ER). The dominant process model of ER (PMER) proposed by J. Gross, however, provides a domain-general account of ER strategies and has not had substantial contact with theories of achievement emotions such as R. Pekrun's control-value theory (CVT) and the academic achievement literature. Moreover, ER has not been a focal point of major theories related to achievement emotions, such as CVT. We propose an integrated model of ER in achievement situations (ERAS) that integrates propositions about the generation of emotions from CVT with propositions about how emotions are regulated and types of ER strategies from PMER. The ERAS model also offers new propositions regarding how different achievement situations, object foci, and time frames, as well as discrete emotions with different appraisal patterns, impact ER strategies.

Research on collaborative learning between humans and virtual pedagogical agents represents a necessary extension to recent research on the conceptual, theoretical, methodological, analytical, and educational issues behind co- and socially-shared regulated learning between humans. This study presents a novel coding framework that was developed and used to describe collaborations between learners and a pedagogical agent (PA) during a subgoal setting activity with MetaTutor, an intelligent tutoring system. Learner-PA interactions were examined across two scaffolding conditions: prompt and feedback (PF), and control. Learners' compliance to follow the PA's prompts and feedback in the PF condition were also examined. Results demonstrated that learners followed the PA's prompts and feedback to help them set more appropriate subgoals for their learning session the majority of the time. Descriptive statistics revealed that when subgoals were set collaboratively between learners and the PA, they generally lead to higher proportional learning gains when compared to less collaboratively set goals. Taken together, the results provide preliminary evidence that learners are both willing to engage in and benefit from collaborative interactions with PAs when immediate, directional feedback and the opportunity to try again are provided. Implications and future directions for extending co- and socially-shared regulated learning theories to include learner-PA interactions are proposed.

Students experience a variety of emotions following achievement outcomes which stand to influence how they learn and perform in academic settings. However, little is known about the link between student outcome emotions and dimensions of performance feedback in computer-based learning environments (CBLEs). Understanding the dynamics of this relationship is particularly important for high-stakes, competency-based domains such as medical education. In this study, we examined the relationship between medical students' (N = 30) outcome emotion profiles and their performance on a diagnostic reasoning task in the CBLE, BioWorld. We found that participants could be organized into distinct emotion groups using k-means cluster analyses based on their self-reported outcome emotion profiles: an expected positive emotion cluster and negative emotion cluster and an unexpected low intensity emotion cluster. A clear relationship was found between emotion clusters and diagnostic performance such that participants classified to the positive emotion cluster had the highest performance; those classified to the negative emotion cluster had the lowest performance; and those classified to the low intensity emotion cluster had performance outcomes that fell between the other two. Our discussion focuses on the theoretical implications of emotion classification and design recommendations for learning environments and emotional interventions in computer-based contexts.

A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven approaches to enhance learners' experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to date that captures this topic's breadth. We took up this grand challenge by integrating related empirical studies and existing conceptual work and proposing a theoretically-guided taxonomy for the development and improvement of emotion-aware systems. In particular, multiple strategies system developers may use to help learners experience positive emotions are mapped out, including those that require different amounts and types of information about the user, as well as when this information is required. Examples from the literature are provided to illustrate how different emotion-aware system approaches can be combined to take advantage of different types of data, both prior to and during the learner-system interaction. High-level system features that emotion-aware systems can tailor to learners in order to elicit positive emotions are also described and exemplified. Theoretically, the taxonomy is primarily informed by the control-value theory of achievement emotions (Pekrun 2006, 2011) and its assumptions about the relationship between distal and proximal antecedents and the elicitation and regulation of emotion. The taxonomy expands upon a dichotomy of emotion-aware systems proposed by D'Mello and Graesser (2015) and is intended to guide the design of emotion-aware systems that can foster positive emotions during learner-system interactions through the use of varied approaches, data sources, and design features.

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.