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1. Using Thinkalouds to Understand Rule Learning and Cognitive Control Mechanisms within an Intelligent Tutoring System

   Unal, Deniz Sonmez; Arrington, Catherine M.; Solovey, Erin; Walker, Erin. (June 2020, Artificial Intelligence in Education 2020)

   Cognitive control and rule learning are two important mechanisms that explain how goals influence behavior and how knowledge is acquired. These mechanisms are studied heavily in cognitive science literature within highly controlled tasks to understand human cognition. Although they are closely linked to the student behaviors that are often studied within intelligent tutoring systems (ITS), their direct effects on learning have not been explored. Understanding these underlying cognitive mechanisms of beneficial and harmful student behaviors can provide deeper insight into detecting such behaviors and improve predictive models of student learning. In this paper, we present a thinkaloud study where we asked students to narrate their thought processes while solving probability problems in ASSISTments. Students are randomly assigned to one of two conditions that are designed to induce the two modes of cognitive control based on the Dual Mechanisms of Control framework. We also observe how the students go through the phases of rule learning as defined in a rule learning paradigm. We discuss the effects of these different mechanisms on learning, and how the information they provide can be used in student modeling. 

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2. Modeling the phases of rule learning during problem solving with an interactive learning environment

   https://doi.org/10.1007/s11257-025-09426-4  

   Sonmez Unal, Deniz; Solovey, Erin; Arrington, Catherine M; Walker, Erin (March 2025, User Modeling and User-Adapted Interaction)

   While existing student modeling methods focus on predicting students’ knowledge states, they often overlook the underlying cognitive processes contributing to learning. In this work, we integrate cognitive processes, specifically phases of rule learning, into student modeling, drawing inspiration from cognitive science. Rule learning involves rule search, discovery, and following, providing a systematic framework for understanding how individuals acquire and apply knowledge. We conduct two studies to explore rule learning phases in a real-world learning context. Moreover, we present a two-step approach to first predict the phases of rule learning students experience during problem solving with an intelligent tutoring system and then estimate the time spent on each predicted phase. Furthermore, we identify the relationships between the time spent on specific phases of rule learning and student performance. Our findings underscore the importance of integrating cognitive processes into student modeling for more targeted interventions and personalized support. 

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3. Connecting Activity Implementation Characteristics to Student Buy-In Toward and Utilization of Formative Assessments Within Undergraduate Biology Courses

   https://doi.org/10.1007/s41979-021-00054-2  

   Brazeal, Kathleen R.; Brown, Tanya L.; Couch, Brian A. (December 2021, Journal for STEM Education Research)

   Abstract While formative assessments (FAs) can facilitate learning within undergraduate STEM courses, their impact likely depends on many factors, including how instructors implement them, whether students buy-in to them, and how students utilize them. FAs have many different implementation characteristics, including what kinds of questions are asked, whether questions are asked before or after covering the material in class, how feedback is provided, how students are graded, and other logistical considerations. We conducted 38 semi-structured interviews with students from eight undergraduate biology courses to explore how various implementation characteristics of in-class and out-of-class FAs can influence student perceptions and behaviors. We also interviewed course instructors to provide context for understanding student experiences. Using thematic analysis, we outlined various FA implementation characteristics, characterized the range of FA utilization behaviors reported by students, and identified emergent themes regarding the impact of certain implementation characteristics on student buy-in and utilization. Furthermore, we found that implementation characteristics have combined effects on student engagement and that students will tolerate a degree of “acceptable discomfort” with implementation features that contradict their learning preferences. These results can aid instructor reflection and guide future research on the complex connections between activity implementation and student engagement within STEM disciplines. 

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4. Impact of Non-Cognitive Interventions on Student Learning Behaviors and Outcomes: An analysis of seven large-scale experimental inventions

   https://doi.org/10.1145/3576050.3576073  

   Vanacore, Kirk; Gurung, Ashish; Mcreynolds, Andrew; Liu, Allison; Shaw, Stacy; Heffernan, Neil (March 2023, LAK23: 13th International Learning Analytics and Knowledge Conference)

   As evidence grows supporting the importance of non-cognitive factors in learning, computer-assisted learning platforms increasingly incorporate non-academic interventions to influence student learning and learning related-behaviors. Non-cognitive interventions often attempt to influence students’ mindset, motivation, or metacognitive reflection to impact learning behaviors and outcomes. In the current paper, we analyze data from five experiments, involving seven treatment conditions embedded in mastery-based learning activities hosted on a computer-assisted learning platform focused on middle school mathematics. Each treatment condition embodied a specific non-cognitive theoretical perspective. Over seven school years, 20,472 students participated in the experiments. We estimated the effects of each treatment condition on students’ response time, hint usage, likelihood of mastering knowledge components, learning efficiency, and post-tests performance. Our analyses reveal a mix of both positive and negative treatment effects on student learning behaviors and performance. Few interventions impacted learning as assessed by the post-tests. These findings highlight the difficulty in positively influencing student learning behaviors and outcomes using non-cognitive interventions. 

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5. Eliciting Proactive and Reactive Control During Use of an Interactive Learning Environment

   Unal, D. S.; Arrington, C. M.; Solovey, E.; Walker, E. (January 2023, Artificial Intelligence in Education (AIED 2023))

   Mendez, G.; Matsuda, N.; Santos, O. C.; Dimitrova, V. (Ed.)

   The dual mechanisms of control framework describes two modes of goal-directed behavior: proactive control (goal maintenance) and reactive control (goal activation on task demands). Although these mechanisms are relevant to learner behaviors during interaction with intelligent tutoring systems (ITS), their relation to ITSs is under-researched. We propose a manipulation to induce proactive or reactive control during interaction with an online tutoring system. We present two experiments where students solved problems using either proactive or reactive control. Study 1 validates the manipulation by investigating behavioral measures that reflect usage of the intended strategy and assesses whether either mode impacted learning. Study 2 investigates if alternating between control modes during problem solving affects student performance. 

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