[No abstract available]

Learning disabilities (LDs) encompass disorders of those who have difficulty learning and using academic skills, exhibiting performance below expectations for their chronological age in the areas of reading, writing, and/or mathematics. Each of the disorders making up the LDs involve different deficits; however, some commonalities can be found within that heterogeneity, such in terms of learning self-regulation and metacognition. Unlike in early ages and later educational levels, there are hardly any evidence-based evaluation protocols for adults with LDs. LDs influence academic performance but also have serious consequences in professional, social, and family contexts. In response to this, the current work proposes a multimodal evaluation protocol focused on metacognitive, self-regulation of learning, and emotional processes, which make up the basis of the difficulties in adults with LDs. The assessment is carried out through analysis of the on-line learning process using a variety methods, techniques, and sensors (e.g., eye tracking, facial expressions of emotion, physiological responses, concurrent verbalizations, log files, screen recordings of human-machine interactions) and off-line methods (e.g., questionnaires, interviews, and self-report measures). This theoretically-driven and empirically-based guideline aims to provide an accurate assessment of LDs in adulthood in order to design effective prevention and intervention proposals.

This chapter focuses on the challenges of measuring self-regulatory processes and learning outcomes while learning from multiple representations with advanced learning technologies (ALTs). More specifically, we present challenges associated with measuring learners' processes and outcomes while using multiple representations with ALTs. ALTs are technology-based learning and training systems designed to teach learners of all ages to learn, reason, problem solve, and understand different topics and domains such as physics, math, history, biology, medicine and can include different systems such as multimedia, hypermedia, serious games, simulations, intelligent tutoring systems, immersive virtual environments and so forth. The structure of our chapter is as follows: (1) a brief overview of the major issues related to using multiple representations for learning with ALTs; (2) a description of the analytical techniques for understanding multiple representations and their goals for successful learning with ALTs; (3) a discussion of the issues associated with using multiple representations to measure and assess learning outcomes and how we can use ALTs as research tools to capture self-regulated learning; and (4) a presentation of challenges and future directions that need to be addressed by researchers.

65 undergraduate students from a North American university interacted with MetaTutorIVH, an agent-based multimedia learning environment that fosters self-regulated learning (SRL) strategy use (e.g., metacognition) while presenting information on several human body systems. Participants completed a self-paced task to study the influence of relevant content and an agents' expressed emotions on metacognitive judgments, gaze behaviors, and science learning. The goal of this study was to examine eye gaze behavior and metacognitive process use relative to their perceived relevancy of content based on discrepancies between multimedia materials and artificial agent's facial expressions. Results indicate an increase in overall fixation duration on multimedia content (e.g., text, diagram) when the text was perceived as less relevant, specifically revealing longer time spent fixating on text when the text was perceived as less relevant compared text and diagrams that were perceived as relevant. Further analyses reveal an increase in diagram fixation duration when the text was judged as being less relevant, but when compared to instances where the text and diagrams were both seen as only somewhat relevant. Across trials, there was no indication of the actual relevancy of content influencing the gaze behaviors of participants.

The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students' (N = 194, with a subset of n = 30 for eye tracking and sequence mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the sequences of engaging in cognitive and metacognitive self-regulated learning processes during learning with MetaTutor, an Intelligent Tutoring System that teaches students about the human circulatory system. Results revealed that there were no significant differences in fixations on single areas of interest by the prior knowledge group students were assigned to; however there were significant differences in fixations on pairs of areas of interest, as evidenced by eye-tracking data. Furthermore, there were significant differences in sequential patterns of engaging in cognitive and metacognitive self-regulated learning processes by students' prior knowledge group, as evidenced from log-file data. Specifically, students with high prior knowledge engaged in processes containing cognitive strategies and metacognitive strategies whereas students with low prior knowledge did not. These results have implications for designing adaptive intelligent tutoring systems that provide individualized scaffolding and feedback based on individual differences, such as levels of prior knowledge.

Analyzing multimodal multichannel data about self-regulated learning (SRL) obtained during the use of advanced learning technologies such as intelligent tutoring systems, serious games, hypermedia, and immersive virtual learning environments is key to understanding the interplay among cognitive, affective, metacognitive, and social processes and their impact on learning, problem solving, reasoning, and conceptual understanding in learners of all ages and contexts. In this special issue of Computers in Human Behavior, we report six studies conducted by interdisciplinary teams' use of various trace methodologies such as eye tracking, log-files, physiological data, facial expressions of emotions, screen recordings, concurrent think-alouds, and linguistic analyses of discourse. The research studies focus on how these data were analyzed using a combination of traditional statistical techniques as well as educational data-mining procedures to detect, measure, and infer cognitive, metacognitive, and social processes related to regulating the self and others across several tasks, domains, ages, and contexts. The results of these studies point to future work necessitating interdisciplinary researchers' collaboration to use theoretically based and empirically derived approaches to collecting, measuring, and modeling multimodal multichannel SRL data to extend our current models, frameworks, and theories by making them more predictive by elucidating the nature, complexity, and temporality of underlying processes. Lastly, analyses of multimodal multichannel SRL process data can significantly augment advanced learning technologies by providing real-time, intelligent, adaptive, individualized scaffolding and feedback to address learners' self-regulatory needs.

Emotions are a core factor of learning. Studies have shown that multiple emotions are co-experienced during learning and have a significant impact on learning outcomes. The present study investigated the importance of multiple, co-occurring emotions during learning about human biology with MetaTutor, a hypermedia-based tutoring system. Person-centered as well as variable-centered approaches of cluster analyses were used to identify emotion clusters. The person-centered clustering analyses indicated three emotion profiles: a positive, negative and neutral profile. Students with a negative profile learned less than those with other profiles and also reported less usage of emotion regulation strategies. Emotion patterns identified through spectral co-clustering confirmed these results. Throughout the learning activity, emotions built a stable correlational structure of a positive, a negative, a neutral and a boredom emotion pattern. Positive emotion pattern scores before the learning activity and negative emotion pattern scores during the learning activity predicted learning, but not consistently. These results reveal the importance of negative emotions during learning with MetaTutor. Potential moderating factors and implications for the design and development of educational interventions that target emotions and emotion regulation with digital learning environments are discussed.

Cognitive, affective, metacognitive, and motivational (CAMM) processes are critical components of self-regulated learning (SRL) essential for learning and problem solving. Currently, ITSs are designed to foster cognitive, affective, and metacognitive (CAM) strategies and processes, presenting major gaps in the research since motivation is a key component of SRL and influences the remaining CAM processes. In our study, students interacted with MetaTutor, a hypermedia-based ITS, to investigate how 190 undergraduate students' proportional learning gain (PLG) related to sub-goals set, cognitive strategy use and metacognitive processes differed based on self-reported achievement goal orientation. Results indicated differences between approach, avoidance, and students who adopted both approach and avoidance goal orientations, but no differences between mastery, performance and students who adopted both mastery and performance goal orientations on PLG for content related to sub-goal 1. Conversely, no differences were found between goal orientation groups on PLG for sub-goal 2, revealing possible changes in goal orientation following sub-goal 1. Analyses indicated no differences between goal orientation groups on metacognitive processes and cognitive strategy use. Thus, we suggest turning away from self-report data, where future studies aim to incorporate multi-channel data over durations of tasks as students interact with ITSs to measure motivation and its tendency to fluctuate in real-time. Implications for using multiple data channels to measure motivation could contribute to adaptive ITS design based on all CAMM processes.

The goal of this study was to examine 38 undergraduate and graduate students' note taking and summarizing, and the relationship between emotions, the accuracy of those notes and summaries, and proportional learning gain, during learning with MetaTutor, an ITS that fosters self-regulated learning while learning complex science topics. Results revealed that students expressed both positive (i.e., joy, surprise) and negative (i.e., confusion, frustration, anger, and contempt) emotions during note taking and summarizing, and that these emotions correlated with each other, as well as with proportional learning gain and accuracy of their notes and summaries. Specifically, contempt during note taking was positively correlated with proportional learning gain; note taking accuracy was negatively correlated with proportional learning gain; and confusion during summarizing was positively correlated with summary accuracy. These results reveal the importance of investigating specific self-regulated learning processes, such as taking notes or making summaries, with future research aimed at examining the differences and similarities between different cognitive and metacognitive processes and how they interact with different emotions similarly or differently during learning. Implications of these findings move us toward developing adaptive ITSs that foster self-regulated science learning, with specific scaffolding based on each individual student's learning needs.

We investigated how college students' (n = 40) different levels of action unit 4 (AU4: brow lowerer), metacognitive monitoring process use and pre-test score were associated with metacognitive monitoring accuracy during learning with a hypermedia-based ITS. Results revealed that participants with high pre-test scores had the highest accuracy scores with low levels of AU4 and use of more metacognitive monitoring processes, whereas participants with low pre-test scores had higher accuracy scores with high levels of AU4 and use of more metacognitive monitoring processes. Implications include designing adaptive ITSs that provide different types of scaffolding based on levels of prior knowledge, use of metacognitive monitoring processes, and emotional expressivity keeping in mind that levels of emotions change over time, and therefore must be monitored to provide effective scaffolding during learning.