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We investigated the effects of just-in-time guidance at various stages of inquiry learning by novice learners. Thirteen participants, randomly assigned to an intervention (n = 8) or control (n = 5) group, were observed as they learned about DC electric circuits using a web-based simulation. Just-in-time instructional prompts to observe, predict, explain, systematically test, collect evidence, and generate rules were strongly associated with diagnosing and correcting misconceptions, and constructing correct scientific concepts. Students' repeated use of predictions, systematic testing, and evidence-coordinated reasoning often led to formulating new principles, generalizing from observed patterns, verifying comprehension, and experiencing "Aha!" moments. Justin-time prompts helped learners manage embedded cognitive challenges in inquiry tasks, achieve a comprehensive understanding of the model represented in the simulation, and show significantly higher knowledge gain. Just-in-time prompts also promoted rejection of incorrect models of inquiry and construction of robust scientific mental models. The results suggest ways of customizing guidance to promote scientific learning within simulation environments.

This special issue's editors invited discussion of three broad questions. Slightly rephrased, they are: How well do self-report data represent theoretical constructs? How should analyses of data be conditioned by properties of self report data? In what ways do interpretations of self-report data shape interpretations of a study's findings? To approach these issues, I first recap the kinds of self-report data gathered by researchers reporting in this special issue. With that background, I take up a fundamental question. What are self-report data? I foreshadow later critical analysis by listing facets I observe in operational definitions of self-report data: nature of the datum, topic, property, setting or context, response scale, and assumptions setting a stage for analyzing data. Discussion of these issues leads to a proposal that ameliorates some of them: Help respondents become better at self reporting.

Metacognition is the engine of self-regulated learning. At the object level, learners seek information and choose learning tactics and strategies they forecast will develop knowledge. At the meta level, learners gather and analyze data about learning events to draw conclusions, such as: Is this tactic a good fit to conditions? Was it effective? Was effort required reasonable? Is my ability publicly exposed? As data accumulate, learners shape, re-shape and refine a personal theory about optimal learning. Thus, self-regulating learners are learning scientists. However, without training and tools on which "professional" learning scientists rely, learners' N = me research programs are naïve and scruffy. Merging models of tasks, cognition, metacognition and motivation, I describe software tools, approaches to analyzing data and learning analytics designed to serve three goals: supporting self-regulating learners' metacognition in N = me research, accelerating professional learning scientists' research, and boosting synergy among learners and learning scientists to accelerate progress in learning science.

Background: Guidance during inquiry learning plays an important role in developing conceptual understanding and inquiry skills. This study analysed learner-tutor interactions in a simulation-assisted learning environment to investigate how tutor guidance enabled knowledge construction and fostered epistemic practice. Objectives: This research aimed to illuminate challenges learners encounter in the inquiry process and forms of guidance that support learning in both conceptual and epistemic aspects. Methods: This study uses a mixed methods approach. We analysed video recordings in which nine participants asked 72 questions and the microsequences of interactions immediately surrounding and including each question. We coded properties of each question and whether the tutors' utterances were intended to increase (upregulate) or decrease (downregulate) the complexity of the inquiry processes, and used a two-step cluster analysis to explore groupings emerged from tutors' regulation guidance and learners' questions. Results and Conclusions: The regulatory intent of tutors' utterances depended on various characteristics of student questions. The microsequences clustered in five categories: 1) upregulated investigation and inference, 2) upregulated evidence-based justification, 3) downregulated cognitive load, 4) downregulated procedural uncertainties, and 5) downregulated perceptual dissonance. Our findings suggest tutors offering guiding prompts should consider dual processes in the inquiry and, by strategically prompting them, strike a balance between the goals of guiding learners to discover scientific knowledge and grounding their conceptual understanding in concepts, data, and procedures. Implications: We emphasize conceptual and epistemic learning should be concurrently guided in scientific inquiry. We propose a bidirectional guidance model as a pedagogical approach to guide instructional practice.

Data used in learning analytics rarely provide strong and clear signals about how learners process content. As a result, learning as a process is not clearly described for learners or for learning scientists. Gaševic, Dawson, and Siemens (2015) urged data be sought that more straightforwardly describe processes in terms of events within learning episodes. They recommended building on Winne's (1982) characterization of traces -- ambient data gathered as learners study that more clearly represent which operations learners apply to which information -- and his COPES model of a learning event -- conditions, operations, products, evaluations, standards (Winne, 1997). We designed and describe an open source, open access, scalable software system called nStudy that responds to their challenge. nStudy gathers data that trace cognition, metacognition, and motivation as processes that are operationally captured as learners operate on information using nStudy's tools. nStudy can be configured to support learners' evolving self-regulated learning, a process akin to personally focused, self-directed learning science.

In scientific inquiry learning, within-task guidance tailored to the learner's domain knowledge and inquiry skill may be essential to promote intended learning outcomes. However, due to dynamic complexity across the timeline of inquiry learning, principles for designing tailored guidance are elusive. In this study, experienced tutors provided just-in-time guidance to 11 learners. We analysed tutor-learner interactions to investigate how tutors adapted guidance. We found tutors provided five types of guidance: prompts, support for domain knowledge, assessments, hints, and feedback. Guidance was provided when learners made errors, expressed difficulties, or asked questions; or when the tutor judged a learner successfully demonstrated a skill and was ready to progress to a follow-on skill. Based on these results, we propose a model for tailored, just-in-time guidance in simulation-assisted inquiry learning environments.

Learner modeling systems so far formulated model learning in three main ways: a learner's "position" within a lattice of declarative and procedural knowledge about highly structured disciplines such as geometry or physics, a learner's path through curricular tasks compared to milestones, or profiles of a learner's achievements on a set of tasks relative to mastery criteria or a peer group. Opening these models to learners identifies for them factors and relations among factors. Open learner models tacitly invite learners to regulate learning. However, contemporary learner models omit data about how learners have and should process information to learn, understand, consolidate and transfer new knowledge and skills. What to do with information opened to learners is opaque. I propose incorporating trace data about learning processes in learner models. Trace data allow generating learning analytics that inform self-regulating learners about potentially productive adaptations to processes they have used to learn. In a context of big data, such elaborated learner models are positioned to collaborate with self-regulating learners. Together, they can coordinate symbiotically, creating a platform for the system to improve its models of learners and for learners to more productively self-regulate learning.

Developing knowledge-transforming skills in writing may help students increase learning by actively building knowledge, regardless of the domain. However, many undergraduate students struggle to transform knowledge when drafting essays based on multiple sources. Writing analytics can be used to scaffold knowledge transforming as writers bring evidence to bear in supporting claims. We investigated how to automatically identify sentences representing knowledge transformation in argumentative essays. A synthesis of cognitive theories of writing and Bloom's typology identified 22 linguistic features to model processes of knowledge transforming in a corpus of 38 undergraduates' essays. Findings indicate undergraduates mostly paraphrase or copy information from multiple sources rather than engage deeply with sources' content. Eight linguistic features were important for discriminating evidential sentences as telling versus transforming source knowledge. We trained a machine learning algorithm that accurately classified nearly three of four evidential sentences as knowledge-telling or knowledge-transforming, offering potential for use in future research.

We investigated knowledge and attitudes before and after reading refutation texts augmented by different kinds of persuasive information and how emotions mediated the process of knowledge and attitude change. Undergraduates (N = 424) enrolled in 4 universities from 3 countries read a refutation text on genetically modified foods (GMFs) and were then randomly assigned to receive additional information about advantages of GMFs, disadvantages of GMFs, or both. After studying, students reading about advantages of GMFs had significantly more positive attitudes than students who read about disadvantages. There was also a significant reduction in misconceptions; participants in the positive-oriented text condition showed the largest learning gains, particularly those who held more positive initial attitudes. Epistemic emotions of curiosity, frustration, hope, and enjoyment mediated attitude change while confusion mediated relations between prereading attitudes and postreading knowledge. In addition, the direct relationship between prior attitudes and surprise was moderated by type of text. When reading about both advantages and disadvantages of GMFs, participants reported significantly less surprise when compared with those who read about either advantages or disadvantages of GMFs. To foster conceptual change when learning about complex topics, refutation texts may be paired with persuasive information that is aligned with accurate conceptions.

Self-regulated learning is an ongoing process rather than a single snapshot in time. Naturally, the field of learning analytics, focusing on interactions and learning trajectories, offers exciting opportunities for analyzing and supporting self-regulated learning. This special section highlights the current state of research at the intersection of self-regulated learning and learning analytics, bridging communities, disciplines, and schools of thought. In this opening article, we introduce the papers and identify themes and challenges in understanding and supporting self- regulated learning in interactive learning environments.

This special issue presents a sample of modern work on self-regulated learning (SRL) among high ability and gifted students. It includes diverse views about the construct per se, and gifted students' and their teachers' accounts about SRL and factors they believe moderate it. Zeidner and Stroeger (this issue) set the stage with a sketch of an extensive literature about SRL that has deep roots in North American educational philosophy and practice. The menu of work here is fundamentally well done and, in varying ways and degrees, slightly provocative. A trite observation would be these articles don't fully represent the multiple facets and complex articulation among them comprising SRL, especially given relatively less research with participants identified as academically talented or gifted. In this situation, I would be pedantic to point out such-and-such is omitted or this-or-that is underrepresented. Rather, using admittedly using idiosyncratic standards, I select a few matters for discussion and, hopefully, constructive critique. Other commentators would likely apply different filters.

Self-explanation is a process by which learners generate inferences about causal connections or conceptual relationships. A meta-analysis was conducted on research that investigated learning outcomes for participants who received self-explanation prompts while studying or solving problems. Our systematic search of relevant bibliographic databases identified 69 effect sizes (from 64 research reports) which met certain inclusion criteria. The overall weighted mean effect size using a random effects model was g = 0.55. We coded and analyzed 20 moderator variables including type of learning task (e.g., solving problems, studying worked problems, and studying text), subject area, level of education, type of inducement, and treatment duration. We found that self-explanation prompts are a potentially powerful intervention across a range of instructional conditions. Due to the limitations of relying on instructor-scripted prompts, we recommend that future research explore computer-generation of self-explanation prompts.

Background: Deep versus surface knowledge is widely discussed by educational practitioners. A corresponding construct, levels of processing, has received extensive theoretical and empirical attention in learning science and psychology. In both arenas, lower levels of information and shallower levels of processing are predicted and generally empirically demonstrated to limit knowledge learners gain, curtail what they can do with newly acquired knowledge, and shorten the life span of recently acquired knowledge. Purpose: I recapitulate major accounts of levels or depth of information and information processing to set a stage for conceptualizing, first, self-regulated learning (SRL) from this perspective and, second, how a "levels-sensitive" approach might be implemented in research about SRL. Method: I merge the levels construct into a model of SRL (Winne, 2011, "Handbook of self-regulation of learning and performance" (pp. 15-32), New York: Routledge; Winne, 2017b, "Handbook of self-regulation of learning and performance" (2nd ed.), New York: Routledge; Winne & Hadwin, 1998, "Metacognition in educational theory and practice" (pp. 277-304). Mahwah, NJ: Lawrence Erlbaum) conceptually and with respect to operationally defining the levels construct in the context of SRL in relation to each of the model's four phases--surveying task conditions, setting goals and planning, engaging the task, and composing major adaptations for future tasks. Select illustrations are provided for each phase of SRL. Regarding phase 3, a software system called nStudy is introduced as state-of-the-art instrumentation for gathering fine-grained, time-stamped trace data about information learners select for processing and operations they use to process that information. Conclusions: Self-regulated learning can be viewed through a lens of the levels construct, and operational definitions can be designed to research SRL with respect to levels. While information can be organized arbitrarily deeply, the levels construct may not be particularly useful for distinguishing among processes except in a sense that, because processes in SRL operate on information with depth, they epiphenomenally acquire characteristics of levels. Thus, SRL per se is not a deeper kind of processing. Instead, it is processing more complex--deeper--information about a different topic, namely processes for learning.

A bottleneck in gathering big data about learning is instrumentation designed to record data about processes students use to learn and information on which those processes operate. The software system nStudy fills this gap. nStudy is an extension to the Chrome web browser plus a server side database for logged trace data plus peripheral modules that analyze trace data and assemble web pages as learning analytics. Students can use nStudy anywhere they connect to the internet. Every event related to creating, modifying, reviewing, linking and organizing information artifacts is logged in fine grain with a time stamp. These data fully trace information students operate on and how they operate on it. Ambient big data about studying gathered au naturel can be tailored by configuring several of nStudy's features. Thus the system can be used to gather data across a wide range lab studies and field trials designed to test a range of models and theories.

Learning analytics are often formatted as visualisations developed from traced data collected as students study in online learning environments. Optimal analytics inform and motivate students' decisions about adaptations that improve their learning. We observe that designs for learning often neglect theories and empirical findings in learning science that explain how students learn. We present six learning analytics that reflect what is known in six areas (we call them cases) of theory and research findings in the learning sciences: setting goals and monitoring progress, distributed practice, retrieval practice, prior knowledge for reading, comparative evaluation of writing, and collaborative learning. Our designs demonstrate learning analytics can be grounded in research on self-regulated learning and self-determination. We propose designs for learning analytics in general should guide students toward more effective self-regulated learning and promote motivation through perceptions of autonomy, competence, and relatedness.

Recent research has shown that for some topics, messages to refute and revise misconceptions may backfire. The current research offers one possible account for this backfire effect (i.e., the ironic strengthening of belief in erroneous information after an attempted refutation) from an educational psychology perspective and examines whether emotions mediate the relationship between self-concept and learning from refutation texts. In an experimental design, 120 undergraduate students responded to a questionnaire focused on their dietary self-concept and were randomly assigned to read an expository or refutation text on the topic of genetically modified foods. Immediately after reading, participants self-reported their emotions followed by completing post-test measures of their knowledge and attitudes of the topic. Results showed an interaction between self-concept and text condition on emotions wherein self-concept predicted negative emotions (i.e., confusion, anxiety, frustration) while reading a refutation text specifically. Further, negative emotions significantly mediated relations between self-concept and post-test knowledge and attitudes. Implications for educational design and future research are discussed.

The trajectory of scholarship about self-regulated learning (SRL) originates in mid-19th-century writings about learners' sense of responsibility in self education. Although Descartes's 17th-century writings implied mental activities consistent with metacognition, a central feature of SRL, these were inarticulate until Flavell and colleagues' studies circa 1970. Since then, research on metacognition and its role in SRL has approximately doubled every decade. Foundations for modeling SRL include Skinner's behaviorism, which acknowledged learners' choices about reinforcers for behavior, and Bandura's social learning theory, with its construct of agency. Research in the 1980s gathered data about SRL mainly using interviews, self-report questionnaires, and think-aloud protocols. These methods were quickly supplemented by observations of behavior and traces of learning activities tightly coupled to features of SRL. Today, SRL research is prominent across a broad spectrum of educational topics. Its importance will grow with trends toward lifelong learning and self-directed inquiries that survey vast information on the Internet, where students control what and how they will learn. Implications for future research include reconceptualizing "error variance" as arising partially due to SRL and capitalizing on software technologies that massively increase access to data about how and to what effects learners self-regulate learning.