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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.

Understanding scientific phenomena requires learners to construct mental models of causal systems. Simulation-based discovery learning offers learners the opportunity to construct mental models and test them against the behavior of a simulation. The purpose of this study was to investigate sequential patterns of learner actions and utterances associated with outcomes of simulation-based guided discovery learning. We conducted a "sequence analysis" of data gathered from 11 undergraduate students engaged in discovery learning. Three related methods were used for the sequence analysis: Levenshtein edit distance, k-means clustering of the Levenshtein distance, and the Kohonen generalized median sequence. The median sequences of high-gaining and low-gaining participants showed qualitative differences in how they gathered evidence, stated claims, and drew explanatory inferences. Differences between the sequences of actions and utterances of high-gaining and low-gaining participants suggested ways that students might be guided to enhance discovery learning. By tracking the learning patterns of learners, researchers can determine the conditions under which prompts should be provided and offer recommendations for transforming less effective learning strategies to more effective ones.

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.

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.

Learning management systems (LMS) offer quiz tools that help students prepare for examinations. The purpose of this study is to investigate quiz tracking variables typically reported by LMS in relation to student achievement, motivation and learning strategies. The data from 143 undergraduate students comprised quiz tracking variables (number of attempts, completion time, and score), exam scores and responses to the Need for Cognition Scale (NfC), and selected components from the Motivated Strategies for Learning Questionnaire and the Achievement Goal Questionnaire. We hypothesized students retrieving information from memory while taking a quiz would complete the quiz in less time than students who searched for answers in the textbook, and consequently, quiz completion time would correlate with exam performance and key motivational and self-regulatory factors. We found quiz completion time correlated positively with performance-avoidance goal orientation. It correlated negatively with exam performance, NfC, self-efficacy, and effort regulation. The results indicated completion time of low stakes quizzes is associated with achievement-related motivations and reliably predicts achievement on summative exams. We attribute these links to the use of retrieval practice by students who successfully regulate their effort and learning strategies.

Instructional support is fundamental to successful implementation of Inquiry-Based Learning (IBL) in various educational settings. Although research indicates that instructional support can lead to learning gain and achievement of inquiry strategies, it does not explain why the same level of guidance for students with relatively the same level of domain knowledge can lead to major differences in their performance and learning gain. To investigate this issue, we performed a descriptive observational study on a group of undergraduate students (N = 10) during their inquiry learning process in a physics simulation. Our results indicated that employing strategies such as setting goals, confronting misconceptions, testing prior knowledge, collecting evidence, reasoning, and inferring by examining various conditions contributed to high learning gain in students. Additionally, instructional support was found beneficial by all individuals -- with both high and low learning gains. However, low gainers tended to benefit more from explicit explanations and direct modeling. The results imply that some specific cognitive and metacognitive strategies are required as prerequisite inquiry competency for successful use of simulation tools and tutor guidance in a simulation based inquiry learning. These strategies can probably be developed through training workshops, instruction, and appropriate scaffolding during various phases of inquiry learning.