Satabdi Basu

Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework

Computational thinking (CT) draws on concepts and practices that are fundamental to computing and computer science. It includes epistemic and representational practices, such as problem representation, abstraction, decomposition, simulation, verification, and prediction. However, these practices are also central to the development of expertise in scientific and mathematical disciplines. Recently, arguments have been made in favour of integrating CT and programming into the K-12 STEM curricula. In this paper, we first present a theoretical investigation of key issues that need to be considered for integrating CT into K-12 science topics by identifying the synergies between CT and scientific expertise using a particular genre of computation: agent-based computation. We then present a critical review of the literature in educational computing, and propose a set of guidelines for designing learning environments on science topics that can jointly foster the development of computational thinking with scientific expertise. This is followed by the description of a learning environment that supports CT through modeling and simulation to help middle school students learn physics and biology. We demonstrate the effectiveness of our system by discussing the results of a small study conducted in a middle school science classroom. Finally, we discuss the implications of our work for future research on developing CT-based science learning environments.

Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment

Learner modeling has been used in computer-based learning environments to model learners’ domain knowledge, cognitive skills, and interests, and customize their experiences in the environment based on this information. In this paper, we develop a learner modeling and adaptive scaffolding framework for Computational Thinking using Simulation and Modeling (CTSiM)—an open ended learning environment that supports synergistic learning of science and Computational Thinking (CT) for middle school students. In CTSiM, students have the freedom to choose and coordinate use of the different tools provided in the environment, as they build and test their models. However, the open-ended nature of the environment makes it hard to interpret the intent of students’ actions, and to provide useful feedback and hints that improves student understanding and helps them achieve their learning goals. To address this challenge, we define an extended learner modeling scheme that uses (1) a hierarchical task model for the CTSiM environment, (2) a set of strategies that support effective learning and model building, and (3) effectiveness and coherence measures that help us evaluate student’s proficiency in the different tasks and strategies. We use this scheme to dynamically scaffold learners when they are deficient in performing their tasks, or they demonstrate suboptimal use of strategies. We demonstrate the effectiveness of our approach in a classroom study where one group of 6th grade students received scaffolding and the other did not. We found that students who received scaffolding built more accurate models, used modeling strategies effectively, adopted more useful modeling behaviors, showed a better understanding of important science and CT concepts, and transferred their modeling skills better to new scenarios.

Identifying middle school students’ challenges in computational thinking-based science learning

Computational thinking (CT) parallels the core practices of science, technology, engineering, and mathematics (STEM) education and is believed to effectively support students’ learning of science and math concepts. However, despite the synergies between CT and STEM education, integrating the two to support synergistic learning remains an important challenge. Relatively, little is known about how a student’s conceptual understanding develops in such learning environments and the difficulties they face when learning with such integrated curricula. In this paper, we present a research study with CTSiM (Computational Thinking in Simulation and Modeling)—computational thinking-based learning environment for K-12 science, where students build and simulate computational models to study and gain an understanding of science processes. We investigate a set of core challenges (both computational and science domain related) that middle school students face when working with CTSiM, how these challenges evolve across different modeling activities, and the kinds of support provided by human observers that help students overcome these challenges. We identify four broad categories and 14 subcategories of challenges and show that the human-provided scaffolds help reduce the number of challenges students face over time. Finally, we discuss our plans to modify the CTSiM interfaces and embed scaffolding tools into CTSiM to help students overcome their various programming, modeling, and science-related challenges and thus gain a deeper understanding of the science concepts.

An instructor dashboard for real-time analytics in interactive programming assignments

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.

Cloud-hosted simulation-as-a-service for high school STEM education

Abstract Despite their advanced status, nations such as the United States of America continue to face a STEM (science, technology, engineering and mathematics) crisis in their education system. Lack of effective teaching modalities that can leverage real-world examples to stimulate student interest in STEM concepts are identified as one of the reasons for this crisis. To address these challenges, our research is investigating the use of innovative and attractive modeling and simulation frameworks for concurrent, interactive and collaborative STEM education where vehicular traffic serves as the real-world example to reify STEM concepts. Existing traffic-related tools, such as traffic simulators, however, do not provide: (1) intuitive abstractions to construct, refine, and simulate various traffic models that are commensurate to the level of high school students, (2) concurrent and scalable model execution, and (3) collaborative learning environments. On the other hand, although intuitive abstractions such as Google Maps exist, these abstractions do not support semantics for dynamic behavior, which is representative of real-world traffic scenarios. To overcome both these challenges and address the STEM problem, this paper presents a Cloud-based, Collaborative, and Scaled-up Modeling and Simulation Framework for STEM Education called C2SuMo. The key contribution of this paper lies in the design and implementation of a cloud-based, elastic modeling and simulation framework that provides an intuitive, model-driven, collaborative, and concurrent visual simulation environment for STEM education. The paper also reports on insights we gained conducting a user study involving over sixty high school students.

Assessing Student Performance in a Computational-Thinking Based Science Learning Environment

Computational Thinking (CT) can effectively promote science learning, but K-12 curricula lack efforts to integrate CT with science. In this paper, we present a generic CT assessment scheme and propose metrics for evaluating correctness of computational and domain-specific constructs in computational models that students construct in CTSiM – a learning environment that combines CT with middle school science. We report a teacher-led, multi-domain classroom study using CTSiM and use our metrics to study how students’ model evolution relates to their pre-post learning gains. Our results lay the framework for online evaluation and scaffolding of students in CTSiM.

A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments

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.

Data-driven generation of rubric criteria from an educational programming environment

We demonstrate that, by using a small set of hand-graded student work, we can automatically generate rubric criteria with a high degree of validity, and that a predictive model incorporating these rubric criteria is more accurate than a previously reported model. We present this method as one approach to addressing the often challenging problem of grading assignments in programming environments. A classic solution is creating unit-tests that the student-generated program must pass, but the rigid, structured nature of unit-tests is suboptimal for assessing the more open-ended assignments students encounter in introductory programming environments like Alice. Furthermore, the creation of unit-tests requires predicting the various ways a student might correctly solve a problem - a challenging and time-intensive process. The current study proposes an alternative, semi-automated method for generating rubric criteria using low-level data from the Alice programming environment.

Data-Driven Generation of Rubric Parameters from an Educational Programming Environment

We demonstrate that, by using a small set of hand-graded students, we can automatically generate rubric parameters with a high degree of validity, and that a predictive model incorporating these rubric parameters is more accurate than a previously reported model. We present this method as one approach to addressing the often challenging problem of grading assignments in programming environments. A classic solution is creating unit-tests that the student-generated program must pass, but the rigid, structured nature of unit-tests is suboptimal for assessing more open-ended assignments. Furthermore, the creation of unit-tests requires predicting the various ways a student might correctly solve a problem – a challenging and time-intensive process. The current study proposes an alternative, semi-automated method for generating rubric parameters using low-level data from the Alice programming environment.

A Computational Thinking Approach to Learning Middle School Science

Computational Thinking (CT) defines a domain-general, analytic approach to problem solving, combining computer science concepts with practices central to modeling and reasoning in STEM (Science, Technology, Engineering and Mathematics) domains. In our research, we exploit this synergy to develop CTSiM (Computational Thinking in Simulation and Modeling) - a cross-domain, visual programming and agent based, scaffolded environment for learning CT and science concepts simultaneously. CTSiM allows students to conceptualize and build computational models of scientific phenomena, execute the models as simulations, conduct experiments to verify the simulation behaviors against ‘expert behavior’, and use the models to solve real world problems.