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Many college students drop STEM majors after struggling in gateway courses, in part because these courses place large demands on students9 time. In three online experiments with two different lessons (measures of central tendency and multiple regression), we identified a promising approach to increase the efficiency of STEM instruction. When we removed lectures and taught participants exclusively with practice and feedback, they learned at least 15% faster. However, our research also showed that this instructional strategy has the potential to undermine interest in course content for less-confident students, who may be discouraged when challenged to solve problems without upfront instruction and learn from their mistakes. If researchers and educators can develop engaging and efficacy-building activities that replace lectures, STEM courses could become better learning environments. 

Research spanning nearly a century has found that math plays an important role in the learning of chemistry. Here, we use a large dataset of student interactions with online courseware to investigate the details of this link between math and chemistry. The activities in the courseware are labeled against a list of knowledge components (KCs) covered by the content, and student interactions are tracked over a full semester of general chemistry at a range of institutions. Logistic regression is used to model student performance as a function of the number of opportunities a student has taken to engage with a particular KC. This regression analysis generates estimates of both the initial knowledge and the learning rate for each student and each KC. Consistent with results from other domains, the initial knowledge varies substantially across students, but the learning rate is nearly the same for all students. The role of math is investigated by labeling each KC with the level of math involved. The overwhelming result from regressions based on these labels is that only the initial knowledge varies strongly across students and across the level of math involved in a particular topic. The student learning rate is nearly independent of both the level of math involved in a KC and the prior mathematical preparation of an individual student. The observation that the primary challenge for students lies in initial knowledge, rather than learning rate, may have implications for course and curriculum design. 

Visual thinking with diagrams is a crucial skill for learning and problem-solving in STEM subjects. To improve in this area, students need a variety of visual problems for deliberate practice. However, in our interviews, educators shared that they struggle to create these practice exercises because of limitations of existing tools. We introduce Edgeworth, a tool designed to help educators easily create visual problems. Edgeworth works in two main ways: firstly, it takes a single diagram from the user and systematically alters it to produce many variations, which the educator can then choose from to create multiple problems. Secondly, it automates the layout of diagrams, ensuring consistent high quality without the need for manual adjustments. To assess Edgeworth, we carried out case studies, a technical evaluation, and expert walkthrough demonstrations. We show that Edgeworth can create problems in three domains: geometry, chemistry, and discrete math. These problems were authored in just 15 lines of Edgeworth code on average. Edgeworth generated usable answer options within the first 10 diagram variations in 87% of authored problems. Finally, educators gave positive feedback on Edgeworth's utility and the real-world applicability of its outputs. 

There is a growing community of researchers at the intersection of data mining, AI, and computing education research. The objective of the CSEDM workshop is to facilitate a discussion among this research community, with a focus on how data mining can be uniquely applied in computing education research. For example, what new techniques are needed to analyze program code and CS log data? How do results from CS education inform our analysis of this data? The workshop is meant to be an interdisciplinary event at the intersection of EDM and Computing Education Research. Researchers, faculty, and students are encouraged to share their AI- and data-driven approaches, methodologies, and experiences where data transforms how students learn Computer Science (CS) skills. This full-day hybrid workshop will feature paper presentations and discussions to promote collaboration. 

Museum exhibits encourage exploration with physical materials typically with minimal signage or guidance. Ideally children get interactive support as they explore, but it is not always feasible to have knowledgeable staff regularly present. Technology-based interactive support can provide guidance to help learners achieve scientific understanding for how and why things work and engineering skills for designing and constructing useful artifacts and for solving important problems. We have developed an innovative AI-based technology, Intelligent Science Exhibits that provide interactive guidance to visitors of an inquiry-based science exhibit. We used this technology to investigate alternative views of appropriate levels of guidance in exhibits. We contrasted visitor engagement and learning from interaction with an Intelligent Science Exhibit to a matched conventional exhibit. We found evidence that the Intelligent Science Exhibit produces substantially better learning for both scientific and engineering outcomes, equivalent levels of self-reported enjoyment, and higher levels of engagement as measured by the length of time voluntarily spent at the exhibit. These findings show potential for transforming hands-on museum exhibits with intelligent science exhibits and more generally indicate how providing children with feedback on their predictions and scientific explanations enhances their learning and engagement. 

Given the demonstrated prevalence of a “doer effect” showing that active practice is related to substantially larger learning gains than passive approaches, an important research goal is to investigate whether and how different active practice features promote students’ learning outcomes. We investigated these questions in the context of an online learning platform that teaches e-learning design principles. In particular, we considered two different practice modes -practice activities inserted in the text (inline practice) and review practice quizzes -and compared their contributions to students' learning outcomes, in terms of module quizzes, periodic exams, and course projects. Our results showed that the different practice modes had distinct impacts on learning outcomes. Doing inline practice activities contributed to students’ quiz performance at the first attempt and project performance while doing review practice quizzes helped students improve their periodic exam performance. We offer some instructional suggestions such as emphasizing practice activities that are more clearly linked with specific learning objectives for projects, and emphasizing review practice quizzes for exam preparation.