Students bring a multitude of ideas and experiences to the classroom while they are reasoning about scientific phenomena. They often need timely guidance to refine build upon their initial ideas. In this study we explore the development of guidance prompts to provide students with personalized, real-time feedback in the context of a pedagogically grounded chatbot. In the current version of the tool, guidance prompts are authored by learning scientists who are experts in the content of the items and in Knowledge Integration pedagogy. When students engage with the chatbot, an idea detection model is used to determine the ideas that are present in a student explanation and then the expert-authored guidance prompts are assigned based on rules about which ideas are or are not present in the student explanation. While this approach allows for close attention to and control of the pedagogical intent of each prompt, it is time consuming and not easily generalizable. Further this rule-based approach limits the ways in which students can interact with the chatbot. The work in progress study presented in this paper explores the potential of using generative AI to create similarly pedagogically grounded guidance prompts as a first step towards increasing the generalizability and scalability of this approach. Specifically, we ask: using criteria from the Knowledge Integration Pedagogical Framework, how do ChatGPT 3.5-authored guidance prompts compare to human expert-authored guidance prompts? We find that while prompt engineering can enhance the alignment of ChatGPT-authored guidance prompts with pedagogical criteria, the human expert-authored guidance prompts more consistently meet the pedagogical criteria.

Students come to science classrooms with ideas informed by their prior instruction and everyday observations. Following constructivist pedagogy, assessments that encourage students to elaborate their ideas, distinguish among them, and link the most promising ones can capture students' potential and help teachers plan their lessons. In this investigation, we study an assessment that engages students in a dialog to refine their response to a Knowledge Integration (KI) question. Our Research Practice Partnership (RPP) initially trained a Natural Language Processing (NLP) idea detection model on 1218 student responses from 5 schools and identified 13 student ideas. The original model had an overall micro-averaged F-score of 0.7634. After classroom testing, three RPP expert teachers with 10+ years of experience reviewed the classroom data and expanded the model, adding six additional ideas including two that they described as precursor ideas because they foreshadowed more sophisticated reasoning. We trained the idea detection model on these 19 ideas using a dataset from 13 teachers and 1206 students across 8 public schools. The updated model had a somewhat lower overall micro-averaged F-score of 0.7297. The two precursor ideas were among the top four detected ideas. The assessment, using the updated model, guided students to express significantly more ideas. A regression model showed that the updated model was associated with greater KI score gains. Expanding the model, thus, created an assessment that motivated students to express more ideas and to achieve higher KI scores. It also provides teachers with deeper insights into their students' understanding of science.

When middle school students encounter computer models of science phenomenon in science class, how do they think those computer models work? Computer models operationalize real-world behaviors of selected variables, and can simulate interactions between the modeled elements through programmed instructions. This study explores how middle school students think about the high-level semantic meaning of those instructions, which we term rules . To investigate this aspect of students’ computational thinking, we developed the Computational Modeling Inventory and administered it to 253 7 th grade students. The Inventory included three computer models that students interacted with during the assessment. In our sample, 99% of students identified at least one key rule underlying a model, but only 14% identified all key rules; 65% believed that model rules can contradict; and 98% could not distinguish between emergent patterns and behaviors that directly resulted from model rules. Despite these misconceptions, compared to the “typical” questions about the science content alone, questions about model rules elicited deeper science thinking, with 2--10 times more responses including reasoning about scientific mechanisms. These results suggest that incorporating computational thinking instruction into middle school science courses might yield deeper learning and more precise assessments around scientific models.

The effectiveness of using learning analytics for learning design primarily depends upon two concepts: grounding and alignment. This is the primary conjecture for the study described in this paper. In our design-based research study, we design, test, and evaluate teacher-facing learning analytics for an online inquiry science unit on global climate change. We design our learning analytics in accordance with a socioconstructivism-based pedagogical framework, called Knowledge Integration, and the principles of learning analytics Implementation Design. Our methodology for the design process draws upon the principle of the Orchestrating for Learning Analytics framework to engage stakeholders (i.e. teachers, researchers, and developers). The resulting learning analytics were aligned to unit activities that engaged students in key aspects of the knowledge integration process. They provided teachers with actionable insight into their students' understanding at critical junctures in the learning process. We demonstrate the efficacy of the learning analytics in supporting the optimization of the unit's learning design. We conclude by synthesizing the principles that guided our design process into a framework for developing and evaluating learning analytics for learning design.

Interactive simulations are commonly used tools in technology enhanced education. Simulations can be a powerful tool for allowing students to engage in inquiry, especially in science disciplines. They can help students develop an understanding of complex science phenomena in which multiple variables are at play. Developing models for complex domains, like climate science, is important for learning. Equally important, though, is understanding how students use these simulations. Finding use patterns that lead to learning will allow us to develop better guidance for students who struggle to extract the useful information from the simulation. In this study, we generate features from action log data collected while students interacted with simulations on climate change. We seek to understand what types of features are important for student learning by using regression models to map features onto learning outcomes.

What does recent research in the learning sciences have to offer computing education researchers and course designers? Over the past 50 years, course designers have learned a great deal about how to teach computer programming. Programming courses have motivated imaginative instructional innovations including microworlds, case studies, programming patterns, peer critiques, and lab-based courses. Early courses often employed collaborative learning by default--due to limited access to personal computers--and invented solutions to common dilemmas such as imbalances in the contributions to a group product. In addition, many of the innovators in computer programming instruction were themselves self-taught, bringing intriguing insights to the design of new courses.Recent work in the learning sciences offers some fresh ideas while also drawing on this promising tradition of innovation. In this talk I will integrate the research of the Technology-Enhanced Learning in Science (TELS) center with my experiences in design of computer programming courses to identify some fruitful directions for the field.

This paper describes our efforts to develop a new lab-based course format for computer science instruction. Building on learning science research, we created a flexible new technology platform to support students and their instructor as they participated in this new form of instruction. Students work collaboratively on Web-based activities while the instructor interacts with students in a tutorial role. The paper describes our system in detail, outlines the organization of the course that used it, and reviews and evaluates the pilot results. We then discuss the implications for computer science instruction and research in higher education.

In this paper, we review research results relating to the use of software design patterns and advocate knowledge integration perspectives on programming instruction. We also describe a number of components of instruction that would lead to more effective use of patterns and better integration of programming knowledge in general.

Can distance learning transform higher education, saving money and improving student learning? Modern technologies allow instructors to design distance learning environments with all the features of traditional courses and more. What findings from research on instruction can help course designers make effective choices? I argue that students who take an autonomous stance towards instruction tend to learn from most courses, and that course designers who take a scaffolded knowledge integration approach to course design can enable autonomous learning. To help designers create courses that transform passive students into autonomous learners, this article draws on recent research on instruction. I describe the scaffolded knowledge integration framework and use this framework to interpret current approaches to distance learning. © 1996 John Wiley & Sons, Inc.

We and others have created case studies to engage students in the process of designing solutions to complex programming problems. Case studies involve learners in activities of expert programmers such as identifying decisions, justifying choices among alternatives, and evaluating the consequences of these choices. Case studies improve computer programming courses by emphasizing the process rather than the product of problem solving. This paper describes specific uses of case studies in freshman and sophomore-level courses.

Various arguments have been made for the use of functional programming, This paper presents some additional arguments for teaching the discipline of functional programming in an introductory programming course.In particular, functional programming .focuses students on the goal of solving the problem at hand rather than distracting them with a multitude of extraneous details; .encourages the formation of templates, i.e. stereotypical chunks of programming knowledge; .provides computer experiences that reinforce and deepen students' understanding of the abstract concept of function; .leads to code that is concise and elegant.We illustrate these points and others with some of our favorite functional programming examples.Examples are given in LISP and Pascal.