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Technology-based, open-ended learning environments (OELEs) can capture detailed information of students' interactions as they work through a task or solve a problem embedded in the environment. This information, in the form of log data, has the potential to provide important insights about the practices adopted by students for scientific inquiry and problem solving. How to parse and analyse the log data to reveal evidence of multifaceted constructs like inquiry and problem solving holds the key to making interactive learning environments useful for assessing students' higher-order competencies. In this paper, we present a systematic review of studies that used log data generated in OELEs to describe, model and assess scientific inquiry and problem solving. We identify and analyse 70 conference proceedings and journal papers published between 2012 and 2021. Our results reveal large variations in OELE and task characteristics, approaches used to extract features from log data and interpretation models used to link features to target constructs. While the educational data mining and learning analytics communities have made progress in leveraging log data to model inquiry and problem solving, multiple barriers still exist to hamper the production of representative, reproducible and generalizable results. Based on the trends identified, we lay out a set of recommendations pertaining to key aspects of the workflow that we believe will help the field develop more systematic approaches to designing and using OELEs for studying how students engage in inquiry and problem-solving practices.

Constructivist learning theories consider deep understanding of the content to be the result of engagement in relevant learning activities with appropriate scaffolding that provides the learner with timely and substantive feedback. However, any group of students has a variety of levels of knowledge and cognitive development, which makes providing appropriate individual-level scaffolding and feedback challenging in the classroom. Computer simulations can help meet this challenge by providing technology-enhanced embedded scaffolding and feedback via specific simulation design. The use of computer simulations does not, however, guarantee development of deep science understanding. Careful research-driven design of the simulation and the accompanying teaching structure both play critical roles in achieving the desired learning outcomes. In this paper, we discuss the capabilities of computer simulations and the issues that can impact the learning outcomes when combining technology-enhanced scaffolding and feedback with external teaching structures. We conclude with suggestions of promising research avenues on simulation design and their use in the classroom to help students achieve deep science understanding. [ABSTRACT FROM AUTHOR]

Neurodivergent students exhibit an inclination towards Science, Technology, Engineering and Mathematics (STEM) fields, yet their learning experiences in STEM courses remain underexamined. Utilizing an online survey of neurodivergent (n = 60) and neurotypical (n = 83) US college students, this study identified various factors influencing their self‐perceived learning experiences, including interest in the course content, instruction quality and performance outcomes. Compared to their neurotypical peers, neurodivergent students attributed negative experiences in STEM courses less frequently to performance‐related factors and more often to a mismatch between their interests and the course content. Both groups also articulated a variety of strengths and challenges encountered in their STEM studies. Neurodivergent students were more likely to report having interest and passion for STEM and less likely to report having peer support and effective study skills and habits as their primary strength for studying STEM. Conversely, while neurotypical students cited difficult content as their central challenge, neurodivergent students more commonly faced challenges with focus and attention. Despite the study's limited sample size, it revealed emerging patterns that emphasize the importance of developing inclusive teaching methods and specific support mechanisms to cater to the unique strengths and challenges of neurodivergent students in higher education. [ABSTRACT FROM AUTHOR]

While learning effective problem-solving is an important goal of engineering education, "how should we teach problemsolving to engineering students?" is an ongoing challenge. In our previous works, we identified the main practices involved in solving a novel technical problem involving electrical circuits. Among these practices were reflective practices that regulate the problem-solving process by making more intentional and informed decisions. Expanding on that work, we examine in this study whether we can improve students' problem-solving by prompting their use of reflective practices. The study presented here consists of two experiments. The first experiment was conducted with 16 undergraduate students in a mechanical engineering course. Students were introduced to problem-solving reflective practices and then received prompts to engage in these practice as they worked on their weekly projects. The quality of their problem-solving was evaluated pre-and post-course using interactive electrical circuit problems embedded in an educational simulation. The improved performance in problem solving in the mechanical engineering context was observed to transfer and to improve problem-solving in the context of the electrical circuit problems. We conducted the second follow-up experiment to confirm that this improvement was the result of the prompted reflection, not simply repeated practice on the test atid/or learning about electrical circuits in between the pre- and post-course evaluation. For the second study, 70 undergraduate students were randomly assigned to one of two conditions: practice solving electric circuit problems with prompted-reflection (PR) or receiving extra repeated-practice (RP) solving such problems, but without reflective prompts. Student problem-solving in the PR condition improved nearly twice as much as in the RP condition. Overall, the results of the study show that prompting students to reflect on their problem-solving produces problem-solving benefits greater than repeated practice and these benefits transfer across disciplines. [ABSTRACT FROM AUTHOR]