Systematic endeavors to take computer science (CS) and computational thinking (CT) to scale in middle and high school classrooms are underway with curricula that emphasize the enactment of authentic CT skills, especially in the context of programming in block-based programming environments. There is, therefore, a growing need to measure students’ learning of CT in the context of programming and also support all learners through this process of learning computational problem solving. The goal of this research is to explore hypothesis-driven approaches that can be combined with data-driven ones to better interpret student actions and processes in log data captured from block-based programming environments with the goal of measuring and assessing students’ CT skills. Informed by past literature and based on our empirical work examining a dataset from the use of the Fairy Assessment in the Alice programming environment in middle schools, we present a framework that formalizes a process where a hypothesis-driven approach informed by Evidence-Centered Design effectively complements data-driven learning analytics in interpreting students’ programming process and assessing CT in block-based programming environments. We apply the framework to the design of Alice tasks for high school CS to be used for measuring CT during programming.
Many introductory programming environments generate a large amount of log data, but making insights from these data accessible to instructors remains a challenge. This research demonstrates that student outcomes can be accurately predicted from student program states at various time points throughout the course, and integrates the resulting predictive models into an instructor dashboard. The effectiveness of the dashboard is evaluated by measuring how well the dashboard analytics correctly suggest that the instructor help students classified as most in need. Finally, we describe a method of matching low-performing students with high-performing peer tutors, and show that the inclusion of peer tutors not only increases the amount of help given, but the consistency of help availability as well.
The generativity and complexity of human thought stem in large part from the ability to represent relations among concepts and form propositions. The current study reveals how a given object such as rabbit is neurally encoded differently and identifiably depending on whether it is an agent ("the rabbit punches the monkey") or a patient ("the monkey punches the rabbit"). Machine-learning classifiers were trained on functional magnetic resonance imaging (fMRI) data evoked by a set of short videos that conveyed agent-verb-patient propositions. When tested on a held-out video, the classifiers were able to reliably identify the thematic role of an object from its associated fMRI activation pattern. Moreover, when trained on one subset of the study participants, classifiers reliably identified the thematic roles in the data of a left-out participant (mean accuracy = .66), indicating that the neural representations of thematic roles were common across individuals.ARTICLE HISTORY
K-12 classrooms use block-based programming environments (BBPEs) for teaching computer science and computational thinking (CT). To support assessment of student learning in BBPEs, we propose a learning analytics framework that combines hypothesis-and data-driven approaches to discern students' programming strategies from BBPE log data. We use a principled approach to design assessment tasks to elicit evidence of specific CT skills. Piloting these tasks in high school classrooms enabled us to analyze student programs and video recordings of students as they built their programs. We discuss a priori patterns derived from this analysis to support data-driven analysis of log data in order to better assess understanding and use of CT in BBPEs.
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