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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.

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.

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.

Our article introduces the "Journal of Educational Data Mining's" Special Issue on Educational Data Mining on Motivation, Metacognition, and Self-Regulated Learning. We outline general research challenges for data mining researchers who conduct investigations in these areas, the potential of EDM to advance research in this area, and issues in validating findings generated by EDM.

A program of research is necessary to examine the psychometric properties of instruments designed to measure individuals' achievement goal orientations. Recently, research on achievement goal orientation has examined the stability of achievement goals to assess how context might influence individuals' achievement goals. Accordingly, studies are necessary to establish factorial invariance across contexts. We examined the psychometric properties of the Achievement Goal Questionnaire (AGQ; Elliot & McGregor, 2001) across task contexts within a single classroom environment. We tested the factor structure by comparing five competing models and evaluated the invariance of the factor structure across four task contexts. Results revealed that the hypothesized four-factor structure was replicated, construct- and discriminant-related evidence of validity were supported, and both internal consistency and test-retest reliability estimates were satisfactory. Moreover, invariance held at all levels across the various contexts. (Contains 6 tables.)

In the context of continued growth in the accessibility of information through the internet, recent advances in theories of self-regulated learning present an opportunity to reexamine how learners work with networked resources in constructivist approaches such as problem-based learning, project-­based learning, and collaborative problem solving. We present a Resource Inquiry model consisting of five stages: (1) Set resource inquiry goals, (2) Plan for resource study, (3) Search and select resources, (4) Study and assess new knowledge, and (5) Critique and recommend resources. Our model informs designers of online tools about how to support learners' cognitive and metacognitive strategies when learning activities involve interacting with networked resources.

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.

Researching self-regulated learning (SRL) as a process that evolves across multiple episodes of studying poses large methodological challenges. While self-report data provide useful information about learners' perceptions of learning, these data are not reliable indicators of studying tactics learners actually use while studying, especially when learners are young children. We argue that self-reports about SRL need to be augmented by fine-grained traces that are records of learners' actual activities as they study. We describe how gStudy software unobtrusively collects detailed trace data about learners' use of study tactics as they engage with content presented in learning kits--collections of documents (e.g., texts, graphics, video clips) and tasks (e.g., notes, concept maps) on which learners operate to study. We suggest that trace data can advance research about how learners select, monitor, assemble, rehearse, and translate information to learn it, and provide raw materials for mapping SRL and its effects. Examples from the "Life Cycles Learning Kit" that supports grade 1 students learning about the life cycles of humans and frogs are given.

Examines the effect of feedback on helping poor readers improve their comprehension. Compares the effects of instruction about text-based inferencing using explicit, process-explaining feedback to effects of using feedback that did not describe how to draw inferences. Finds that both instructional conditions boosted overall comprehension, but inference-making was better when students received explicit feedback. (RS)