Paulo Blikstein

Modeling how students learn to program

Despite the potential wealth of educational indicators expressed in a students approach to homework assignments, how students arrive at their final solution is largely overlooked in university courses. In this paper we present a methodology which uses machine learning techniques to autonomously create a graphical model of how students in an introductory programming course progress through a homework assignment. We subsequently show that this model is predictive of which students will struggle with material presented later in the class.

Educational Data Mining and Learning Analytics: Applications to Constructionist Research

AbstractConstructionism can be a powerful framework for teaching complex content to novices. At the core of constructionism is the suggestion that by enabling learners to build creative artifacts that require complex content to function, those learners will have opportunities to learn this content in contextualized, personally meaningful ways. In this paper, we investigate the relevance of a set of approaches broadly called “educational data mining” or “learning analytics” (henceforth, EDM) to help provide a basis for quantitative research on constructionist learning which does not abandon the richness seen as essential by many researchers in that paradigm. We suggest that EDM may have the potential to support research that is meaningful and useful both to researchers working actively in the constructionist tradition but also to wider communities. Finally, we explore potential collaborations between researchers in the EDM and constructionist traditions; such collaborations have the potential to enhance the ability of constructionist researchers to make rich inferences about learning and learners, while providing EDM researchers with many interesting new research questions and challenges.

Gears of our childhood: constructionist toolkits, robotics, and physical computing, past and future

Microcontroller-based toolkits and physical computing devices have been used in educational settings for many years for robotics, environmental sensing, scientific experimentation, and interactive art. Based on a historical analysis of the development of these devices, this study examines the design principles underlying the several available platforms for physical computing and presents a framework to analyze various platforms and their use in education. Given the now widespread use of these devices among children and their long history in the field, a historical review and analysis of this technology would be useful for interaction designers.

Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming

New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and student-centered learning is growing considerably. In this article, we present studies focused on how students learn computer programming, based on data drawn from 154,000 code snapshots of computer programs under development by approximately 370 students enrolled in an introductory undergraduate programming course. We use methods from machine learning to discover patterns in the data and try to predict final exam grades. We begin with a set of exploratory experiments that use fully automated techniques to investigate how much students change their programming behavior throughout all assignments in the course. The results show that students’ change in programming patterns is only weakly predictive of course performance. We subsequently hone in on 1 single assignment, trying to map students’ learning process and trajectories and automatically identify productive and unproductive (sink) states within these trajectories. Results show that our process-based metric has better predictive power for final exams than the midterm grades. We conclude with recommendations about the use of such methods for assessment, real-time feedback, and course improvement.

Towards the development of multimodal action based assessment

In this paper, we describe multimodal learning analytics techniques for understanding and identifying expertise as students engage in a hands-on building activity. Our techniques leverage process-oriented data, and demonstrate how this temporal data can be used to study student learning. The proposed techniques introduce useful insights in how to segment and analyze gesture- and action-based generally, and may also be useful for other sources of process rich data. Using this approach we uncover new ideas about how experts engage in building activities. Finally, a primary objective of this work is to motivate additional research and development in the area of authentic, automated, process-oriented assessments.

Trap it!: A Playful Human-Biology Interaction for a Museum Installation

We developed Trap it!, a human-biology interaction (HBI) medium encompassing a touchscreen interface, microscopy, and light projection. Users can interact with living cells by drawing on a touchscreen displaying the microscope view of the cells. These drawings are projected onto the microscopy field as light patterns, prompting observable movement in phototactic responses. The system design enables stable and robust HBI and a wide variety of programmed activities (art, games, and experiments). We investigated its affordances as an exhibit in a science museum in both facilitated and unfacilitated contexts. Overall, it had a low barrier of entry and fostered rich communication among visitors. Visitors were particularly excited upon realizing that the interaction involved real organisms, an understanding that was facilitated by the eyepiece on the physical system. With the results from user study, we provide our observations, insights and guidelines for designing HBI as a permanent museum exhibit.

MaterialSim: A Constructionist Agent-Based Modeling Approach to Engineering Education

This chapter reports on a model-based, constructionist learning environment for engineering education in the field of materials sciences. “MaterialSim” is a set of activities, computer models, and support materials in which students’ main task is to conduct a scientific investigation by programming and testing their own agent-based models of a materials science phenomenon. In this paper, we investigate: (a) the cognition of students engaging in scientific inquiry through interacting with computer models; (b) the cognitive gains of students programming their own computer models of scientific phenomena; (c) the characteristics, advantages, and trajectories of scientific content knowledge that is articulated in epistemic forms and representational infrastructures unique to agent-based modeling, in comparison with aggregate, equational representations, and (d) the principles which govern the design of agent-based, constructionist learning environments in general and for materials science in particular.

Leveraging multimodal learning analytics to differentiate student learning strategies

Multimodal analysis has had demonstrated effectiveness in studying and modeling several human-human and human-computer interactions. In this paper, we explore the role of multimodal analysis in the service of studying complex learning environments. We compare uni-modal and multimodal; manual and semi-automated methods for examining how students learn in a hands-on, engineering design context. Specifically, we compare human annotations, speech, gesture and electro-dermal activation data from a study (N=20) where student participating in two different experimental conditions. The experimental conditions have already been shown to be associated with differences in learning gains and design quality. Hence, one objective of this paper is to identify the behavioral practices that differed between the two experimental conditions, as this may help us better understand how the learning interventions work. An additional objective is to provide examples of how to conduct learning analytics research in complex environments and compare how the same algorithm, when used with different forms of data can provide complementary results.

Bloctopus: A Novice Modular Sensor System for Playful Prototyping

Tangible prototyping enables designers to rapidly iterate design concepts, gather feedback, and learn quickly from mistakes. However, when a higher level of functionality is needed with sensors, novices struggle with technical implementation. Existing novice electronics toolkits, such as Arduino, have lowered the threshold to electronic experimentation, but still require manual creation of circuits and software programming ability. We present Bloctopus, a modular electronic prototyping toolkit that allows direct electrical interfacing over USB, and physical interfacing with LEGO blocks. We present the stand-alone sensor model, where each module can directly interface with either a computer or microcontroller, using musical message passing over MIDI. We show that the modules can be programmed with a simplified data flow model in a web-based visual programming interface. Finally, we present a prototyping case study that demonstrates the expressivity of devices that can be created using LEGO pieces, combined with functional electronic modules.

Interactive Cloud Experimentation for Biology: An Online Education Case Study

Interacting with biological systems via experiments is important for academia, industry, and education, but access barriers exist due to training, costs, safety, logistics, and spatial separation. High-throughput equipment combined with web streaming could enable interactive biology experiments online, but no such platform currently exists. We present a cloud experimentation architecture (paralleling cloud computation), which is optimized for a class of domain-specific equipments (biotic processing units - BPU) to share and execute many experiments in parallel remotely and interactively at all time. We implemented an instance of this architecture that enables chemotactic experiments with a slime mold Physarum Polycephelum. A user study in the blended teaching and research setting of a graduate-level biophysics class demonstrated that this platform lowers the access barrier for non-biologists, enables discovery, and facilitates learning analytics. This architecture is flexible for integration with various biological specimens and equipments to facilitate scalable interactive online education, collaborations, research, and citizen science.