James R. Segedy

Guided Skill Practice as an Adaptive Scaffolding Strategy in Open-Ended Learning Environments

While open-ended learning environments (OELEs) offer powerful learning opportunities, many students struggle to learn in them. Without proper support, these learners use system tools incorrectly and adopt suboptimal learning strategies. Typically, OELEs support students by providing hints: suggestions for how to proceed combined with information relevant to the learner’s situation. However, students often ignore or fail to understand such hints. To address this problem, we present an alternative approach to supporting students in OELEs that combines suggestions and assertions with guided skill practice. We demonstrate the feasibility of our approach through an experimental study that compares students who receive suggestions, assertions, and guided skill practice to students who receive no such support. Findings indicate that learners who received the scaffolds approached their tasks more systematically.

Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and the choice to adopt a variety of strategies and approaches to solving these problems. To help students overcome their difficulties and become effective learners and problem solvers, we have to develop methods that can track and interpret students’ open-ended learning and problem-solving behaviors. The complexity of the problems and the open-ended nature of the solution processes pose considerable challenges to accurately interpret and evaluate student behaviors and performance as they work on the system. In this paper, we develop a framework that combines model-driven strategy detection with data-driven pattern discovery for analyzing students’ learning activity data in open-ended environments. We present results from an in-depth case study of multiple activity patterns identified in data from the Betty’s Brain learning environment. The results illustrate the benefits of combining model- and data-driven techniques to precisely characterize the learning behavior of students in an open-ended environment.

Studying Student Use of Self-Regulated Learning Tools in an Open-Ended Learning Environment

This paper discusses a design-based research study that we conducted in a middle school science classroom to test the effectiveness of SimSelf, an open-ended learning environment for science learning. In particular, we evaluated two tools intended to help students develop and practice the important regulatory processes of planning and monitoring. Findings showed that students who used the supporting tools as intended demonstrated effective learning of the science topic. Conversely, students who did not use the tools effectively generally achieved minimal success at their learning tasks. Analysis of these results provides a framework for redesigning the environment and highlights areas for additional scaffolding and guidance.

Using a Cognitive/Metacognitive Task Model to Analyze Students Learning Behaviors

Adapting to learners’ needs and providing useful, individualized feedback to help them succeed has been a hallmark of most intelligent tutoring systems. More recently, to promote deep learning and critical thinking skills in STEM disciplines, researchers have begun developing open-ended learning environments that present learners with complex problems and a set of tools for learning and problem solving. To be successful in such environments, learners must employ a variety of cognitive skills and metacognitive strategies. This paper discusses a framework that combines a theory-driven, top-down approach with a bottom-up, pattern-discovery approach for analyzing learning activity data in these environments. Combining these approaches allows for more complex qualitative and quantitative interpretation of a student’s cognitive and metacognitive abilities. The results of this analysis provide a foundation for developing performance- and behavior-based learner models in conjunction with adaptive scaffolding mechanisms to promote effective, personalized learning experiences.

Investigating the relationship between dialogue responsiveness and learning in a teachable agent environment

Using the Bettys Brain Teachable Agents learning environment, we explored a potential relationship between a students responsiveness to pedagogical agent feedback and the students learning and performance in the system. We found that both dialogue and action responsiveness metrics were significantly correlated with learning gains in pre- to post-tests, but only action responsiveness was significantly correlated with task performance scores. Dialogue responsiveness was also a better predictor of learning gain than were standardized test scores.

Bringing CBLEs into Classrooms: Experiences with the Betty's Brain System

This paper discusses the Bettypsilas Brain system and our ongoing work on developing a suite of tools that assist students and teachers in classroom learning in science domains. We describe the design and implementation of the system using a client/server architecture, and the initial responses of the teachers to the tools we have developed. Future enhancements and additions to the suite of tools are discussed.

Coherence Over Time: Understanding Day-to-Day Changes in Students’ Open-Ended Problem Solving Behaviors

Understanding students’ self-regulated learning (SRL) behaviors in open-ended learning environments (OELEs) is an on-going area of research. Whereas OELEs facilitate use of SRL processes, measuring them reliably is difficult. In this paper, we employ coherence analysis, a recently-developed approach to analyzing students’ problem solving behaviors in OELEs, to study how student behaviors change over time as they use an OELE called Betty’s Brain. Results show interesting patterns in students’ day-to-day transitions, and these results can be used to better understand the individual student’s characteristics and the challenges they face when learning in OELEs.

Smart Open-Ended Learning Environments That Support Learners Cognitive and Metacognitive Processes

Metacognition and self-regulation are important for effective learning; but novices often lack these skills. Betty’s Brain, a Smart Open-Ended Learning Environment, helps students develop metacognitive strategies through adaptive scaffolding as they work on challenging tasks related to building causal models of science processes. In this paper, we combine our previous work on sequence mining methods to discover students’ frequently-used behavior patterns with context-driven assessments of the effectiveness of these patterns. Post Hoc analysis provides the framework for systematic analysis of students’ behaviors online to provide the adaptive scaffolding they need to develop appropriate learning strategies and become independent learners.

Designing Representations and Support for Metacognition in the Generalized Intelligent Framework for Tutoring

An important component of metacognition relates to the understanding and use of strategies. Thus, measuring and supporting students’ strategy understanding in complex open-ended learning environments is an important challenge. However, measuring students’ strategy use and understanding is a difficult undertaking. In this paper, we present our design for representing and supporting students in their understanding of strategies while working in complex, open-ended learning environments using the Generalized Intelligent Framework for Tutoring (GIFT). Our approach utilizes a wealth of previous research and relies on three primary instructional interventions: contextualized conversational assessments and feedback; reviewing knowledge and strategies; and teaching through analogies. We believe that incorporating these approaches into GIFT will allow for powerful instruction of complex tasks and topics.

Adaptive Scaffolds in Open-Ended Learning Environments

Open-ended learning environments (OELEs) are learner-centered, and they offer students opportunities to take part in authentic and complex problem-solving tasks. However, learners typically struggle to learn with OELEs without proper adaptive scaffolds. This paper describes research and development related to designing real-time algorithms for diagnosing students’ needs in OELEs and responding with appropriate adaptive scaffolds.