Betty's Brain Research Articles

Envisioning the future of learning and teaching engineering in the artificial intelligence era: Opportunities and challenges

Envisioning the future of learning and teaching engineering in the artificial intelligence era: Opportunities and challenges

Analysing adaptive scaffolds that help students develop self‐regulated learning behaviours

AbstractBackgroundProviding adaptive scaffolds to help learners develop effective self‐regulated learning (SRL) behaviours has been an important goal for intelligent learning environments. Adaptive scaffolding is especially important in open‐ended learning environments (OELE), where novice learners often face difficulties in completing their learning tasks.ObjectivesThis paper presents a systematic framework for adaptive scaffolding in Betty's Brain, a learning‐by‐teaching OELE for middle school science, where students construct a causal model to teach a virtual agent, generically named Betty. We evaluate the adaptive scaffolding framework and discuss its implications on the development of more effective scaffolds for SRL in OELEs.MethodsWe detect key cognitive/metacognitive inflection points, that is, moments where students' behaviours and performance change during learning, often suggesting an inability to apply effective learning strategies. At inflection points, Mr. Davis (a mentor agent in Betty's Brain) or Betty (the teachable agent) provides context‐specific conversational feedback, focusing on strategies to help the student become a more productive learner, or encouragement to support positive emotions. We conduct a classroom study with 98 middle schoolers to analyse the impact of adaptive scaffolds on students' learning behaviours and performance. We analyse how students with differential pre‐to‐post learning outcomes receive and use the scaffolds to support their subsequent learning process in Betty's Brain.Results and ConclusionsAdaptive scaffolding produced mixed results, with some scaffolds (viz., strategic hints that supported debugging and assessment of causal models) being generally more useful to students than others (viz., encouragement prompts). Additionally, there were differences in how students with high versus low learning outcomes responded to some hints, as suggested by the differences in their learning behaviours and performance in the intervals after scaffolding. Overall, our findings suggest how adaptive scaffolding in OELEs like Betty's Brain can be further improved to better support SRL behaviours and narrow the learning outcomes gap between high and low performing students.ImplicationsThis paper contributes to our understanding and impact of adaptive scaffolding in OELEs. The results of our study indicate that successful scaffolding has to combine context‐sensitive inflection points with conversational feedback that is tailored to the students' current proficiency levels and needs. Also, our conceptual framework can be used to design adaptive scaffolds that help students develop and apply SRL behaviours in other computer‐based learning environments.

The evolution of metacognitive strategy use in an open-ended learning environment: Do prior domain knowledge and motivation play a role?

There is a growing interest in viewing self-regulated learning as events unfolding over time, especially when students perform learning tasks in computer-based environments. Metacognitive activities are critical events in self-regulated learning. This study investigated the evolution of metacognitive strategy use in an open-ended computer-based learning environment, Betty's Brain. The data were from 93 sixth graders who used Betty's Brain to learn about climate change for four days. We extracted indicators of metacognitive strategy use from action logs. A knowledge test and self-report questionnaire were administrated before students started using Betty's Brain to assess prior domain knowledge and motivation, respectively. Results showed that metacognitive strategy use increased from the first to the second day and remained stable from the second to the fourth day of the study. The evolution of these behaviors varied across students. Task value and prior domain knowledge partially explained the individual differences in this evolution. Task value and prior domain knowledge also predicted the use of metacognitive strategies. Self-efficacy did not influence metacognitive strategy use. These results suggest the need for further investigation into the role of motivation and prior domain knowledge in the temporal evolution of metacognitive events.

Do Students' Learning Behaviors Differ when they Collaborate in Open-Ended Learning Environments?

Researchers have long recognized the importance of using technology to support students' collaboration in learning and problem solving tasks. Recently, there has been a lot of research in capturing and characterizing student discourse and how they regulate each other when they perform learning tasks in pairs or in small groups. In this paper, our goal is to dive a little deeper into how students collaborate, and the learning behaviors they exhibit when working in pairs on a learning by modeling task, while also teaching a virtual agent in the Betty's Brain system. We report the results of a quasi-experimental study, where students were divided into two groups: one group worked in pairs and the other group worked individually. The results illustrate that students in the collaborative group built more correct causal maps than those working individually, and their pre-post test results show significantly higher learning gains in the science content. A differential sequence mining algorithm applied to their action sequences captured in log files showed differences in the learning behaviors between the two groups. The differences imply that the collaborative groups were better at debugging their evolving causal maps than the students who worked individually.

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.

Shallow strategy development in a teachable agent environment designed to support self-regulated learning

To support self-regulated learning (SRL), computer-based learning environments (CBLEs) are often designed to be open-ended and multidimensional. These systems incorporate diverse features that allow students to enact and reveal their SRL strategies via the choices they make. However, research shows that students' use of such features is limited; students often neglect SRL-supportive tools in CBLEs. In this study, we examined middle school students' feature use and strategy development over time using a teachable agent system called Betty's Brain. Students learned about climate change and thermoregulation in two units spanning several weeks. Learning was assessed using a pretest-posttest design, and students' interactions with the system were logged. Results indicated that use of SRL-supportive tools was positively correlated with learning outcomes. However, promising strategy patterns weakened over time due to shallow strategy development, which also negatively impacted the efficacy of the system. Although students seemed to acquire one beneficial strategy, they did so at the cost of other beneficial strategies. Understanding this phenomenon may be a key avenue for future research on SRL-supportive CBLEs. We consider two hypotheses for explaining and perhaps reducing shallow strategy development: a student-centered hypothesis related to ''gaming the system,'' and a design-centered hypothesis regarding how students are scaffolded via the system.

Designing Learning by Teaching Agents: The Betty's Brain System

The idea that teaching others is a powerful way to learn is intuitively compelling and supported in the research literature. We have developed computer-based, domain-independent Teachable Agents th...

LEARNING BY TEACHING: A NEW AGENT PARADIGM FOR EDUCATIONAL SOFTWARE

ABSTRACT This paper discusses Betty's Brain, a teachable agent in the domain of river ecosystems that combines learning by teaching with self-regulation mentoring to promote deep learning and understanding. Two studies demonstrate the effectiveness of this system. The first study focused on components that define student-teacher interactions in the learning by teaching task. The second study examined the value of adding meta-cognitive strategies that governed Betty's behavior and self-regulation hints provided by a mentor agent. The study compared three versions: a system where the student was tutored by a pedagogical agent, a learning by teaching system, where students taught a baseline version of Betty, and received tutoring help from the mentor, and a learning by teaching system, where Betty was enhanced to include self-regulation strategies, and the mentor provided help on domain material on how to become better learners and better teachers. Results indicate that the addition of the self-regulated Betty and the self-regulation mentor better prepared students to learn new concepts later, even when they no longer had access to the SRL environment.