More Like this

1. WIP: Exploratory Learning Before Instruction on Python Error Messages: Looking Beyond the Learning Outcomes

   https://doi.org/10.1109/FIE61694.2024.10893012  

   DeCaro, Marci S; Thompson, Angela; Velić, Lianda; Crockett, Cenetria; Bego, Campbell (October 2024, IEEE)

   This research work in progress research paper examines student perceptions after completing an exploratory learning lesson before instruction on an introductory programming concept. During exploratory learning activities, students explore a novel concept prior to instruction—the reverse of typical instruct-then-practice methods. Exploratory learning before instruction can help students activate prior knowledge, become aware of their knowledge gaps, and discern important problem features to improve conceptual understanding. Students in a first-year engineering course (N=402) learned about Python error messages in one of two conditions. In the explore-first condition, students completed a collaborative activity prior to instruction. In the instruct-first condition, students received instruction prior to the activity. Following the activity and instruction, students completed a survey to assess their perceptions of the activities. Survey items (e.g. cognitive load, self-efficacy, belonging, knowledge gaps) were chosen as potential factors that could explain learning outcomes between the two conditions. In prior work, we found higher posttest scores in the instruct-first compared to explore-first condition, contrary to the majority of previous studies. Cognitive load and knowledge gaps were higher in the explore-first condition than the instruct-first condition. Self-efficacy and competence were lower in the explore-first condition. No other significant differences were found. Exploring before instruction might disrupt learning and perceived efficacy and competence if the activity is too challenging, or if the instruction does not fully resolve gaps in students’ knowledge. 

   more » « less
2. Measuring Mental Demand Using Pupil Dilation in an Augmented Reality Environment

   https://doi.org/10.1177/10711813251358793  

   Mohanty, Siddarth; Kim, Jung Hyup; Mostowfi, Sara; Wang, Fang; Oprean, Danielle; Seo, Kangwon; Wang, Yi (July 2025, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   This study investigates the method for measuring cognitive workload in augmented reality-based biomechanics lectures by analyzing pupil dilation. Using Dikablis Glasses 3 and Microsoft HoloLens, we recorded physiological and subjective data across learning and problem-solving phases. Pupil dilation was normalized and segmented, enabling a comparison of cognitive demands between phases. The results indicated significant correlations between pupil dilation and NASA TLX cognitive demand, particularly in lectures that primarily involved procedural knowledge. These findings suggest that instructional design and content complexity have a significant impact on cognitive load, providing valuable insights for optimizing AR-based learning environments to support cognitive efficiency and student engagement. 

   more » « less
3. Developing Deliberate Practice for Learning Engineering Dynamics by Analyzing Students’ Mental Models

   Tang, Yan; Bai, Haiyan; Catrambone, Richard (June 2022, ASEE annual conference exposition)

   Practice plays a critical role in learning engineering dynamics. Typical practice in a dynamics course involves solving textbook problems. These problems can impose great cognitive load on underprepared students because they have not mastered constituent knowledge and skills required for solving whole problems. For these students, learning can be improved by being engaged in deliberate practice. Deliberate practice refers to a type of practice aimed at improving specific constituent knowledge or skills. Compared to solving whole problems requiring the simultaneous use of multiple constituent skills, deliberate practice is usually focused on one component skill at a time, which results in less cognitive load and more specificity. Contemporary theories of expertise development have highlighted the influence of deliberate practice (DP) on achieving exceptional performance in sports, music, and various professional fields. Concurrently, there is an emerging method for improving learning efficiency of novices by combining deliberate practice with cognitive load theory (CLT), a cognitive-architecture-based theory for instructional design. Mechanics is a foundation for most branches of engineering. It serves to develop problem-solving skills and consolidate understanding of other subjects, such as applied mathematics and physics. Mechanics has been a challenging subject. Students need to understand governing principles to gain conceptual knowledge and acquire procedural knowledge to apply these principles to solve problems. Due to the difficulty in developing conceptual and procedural knowledge, mechanics courses are among those that receive high DFW rates (percentage of students receiving a grade of D or F or Withdrawing from a course), and students are more likely to leave engineering after taking mechanics courses. Deliberate practice can help novices develop good representations of the knowledge needed to produce superior problem solving performance. The goal of the present study is to develop deliberate practice techniques to improve learning effectiveness and to reduce cognitive load. Our pilot study results revealed that the student mental effort scores were negatively correlated with their knowledge test scores with r = -.29 (p < .05) after using deliberate practice strategies. This supports the claim that deliberate practice can improve student learning while reducing cognitive load. In addition, the higher the students’ knowledge test scores, the lower their mental effort was when taking the tests. In other words, the students who used deliberate practice strategies had better learning results with less cognitive load. To design deliberate practice, we often need to analyze students’ persistent problems caused by faulty mental models, also referred to as an intuitive mental model, and misconceptions. In this study, we continue to conduct an in-depth diagnostic process to identify students’ common mistakes and associated intuitive mental models. We then use the results to develop deliberate practice problems aimed at changing students’ cognitive strategies and mental models. 

   more » « less
4. Developing Deliberate Practice for Learning Engineering Dynamics by Analyzing Students’ Mental Models

   Tang, Yan; Bai, Haiyan; Catrambone, Richard (June 2022, ASEE annual conference exposition)

   Practice plays a critical role in learning engineering dynamics. Typical practice in a dynamics course involves solving textbook problems. These problems can impose great cognitive load on underprepared students because they have not mastered constituent knowledge and skills required for solving whole problems. For these students, learning can be improved by being engaged in deliberate practice. Deliberate practice refers to a type of practice aimed at improving specific constituent knowledge or skills. Compared to solving whole problems requiring the simultaneous use of multiple constituent skills, deliberate practice is usually focused on one component skill at a time, which results in less cognitive load and more specificity. Contemporary theories of expertise development have highlighted the influence of deliberate practice (DP) on achieving exceptional performance in sports, music, and various professional fields. Concurrently, there is an emerging method for improving learning efficiency of novices by combining deliberate practice with cognitive load theory (CLT), a cognitive-architecture-based theory for instructional design. Mechanics is a foundation for most branches of engineering. It serves to develop problem-solving skills and consolidate understanding of other subjects, such as applied mathematics and physics. Mechanics has been a challenging subject. Students need to understand governing principles to gain conceptual knowledge and acquire procedural knowledge to apply these principles to solve problems. Due to the difficulty in developing conceptual and procedural knowledge, mechanics courses are among those that receive high DFW rates (percentage of students receiving a grade of D or F or Withdrawing from a course), and students are more likely to leave engineering after taking mechanics courses. Deliberate practice can help novices develop good representations of the knowledge needed to produce superior problem solving performance. The goal of the present study is to develop deliberate practice techniques to improve learning effectiveness and to reduce cognitive load. Our pilot study results revealed that the student mental effort scores were negatively correlated with their knowledge test scores with r = -.29 (p < .05) after using deliberate practice strategies. This supports the claim that deliberate practice can improve student learning while reducing cognitive load. In addition, the higher the students’ knowledge test scores, the lower their mental effort was when taking the tests. In other words, the students who used deliberate practice strategies had better learning results with less cognitive load. To design deliberate practice, we often need to analyze students’ persistent problems caused by faulty mental models, also referred to as an intuitive mental model, and misconceptions. In this study, we continue to conduct an in-depth diagnostic process to identify students’ common mistakes and associated intuitive mental models. We then use the results to develop deliberate practice problems aimed at changing students’ cognitive strategies and mental models. 

   more » « less
5. Applying Deliberate Practice to Facilitate Schema Acquisition in Learning Introductory Mechanics

   Tang, Yan; Bai, Haiyan; Catrambone, Richard (July 2021, ASEE annual conference proceedings)

   null (Ed.)

   Learning is usually conceptualized as a process during which new information is processed in working memory to form knowledge structures called schemas, which are stored in long-term memory. Practice plays a critical role in developing schemas through learning-by-doing. Contemporary expertise development theories have highlighted the influence of deliberate practice (DP) on achieving exceptional performance in sports, music, and different professional fields. Concurrently, there is an emerging method for improving learning efficiency by combining deliberate practice with cognitive load theory (CLT), a cognition-architecture-based theory for instructional design. Mechanics is a foundation for most branches of engineering. It serves to develop problem-solving skills and consolidate understanding of other subjects, such as applied mathematics and physics. Mechanics has been a challenging subject. Students need to understand governing principles to gain conceptual knowledge and acquire procedural knowledge to apply these principles to solve problems. Due to the difficulty in developing conceptual and procedural knowledge, mechanics courses are among those which receive high DFW rates (percentage of students receiving a grade of D or F or Withdrawing from a course) and students are more likely to leave engineering after taking mechanics courses. Since deliberate practice can help novices develop good representations of the knowledge needed to produce superior problem solving performance, this study is to evaluate how deliberate practice helps students learn mechanics during the process of schema acquisition and consolidation. Considering cognitive capacity limitations, we will apply cognitive load theory to develop deliberate practice to help students build declarative and procedural knowledge without exceeding their working memory limitations. We will evaluate the effects of three practice strategies based on CLT on the schema acquisition and consolidation in two mechanics courses (i.e., Statics and Dynamics). Examples and assessment results will be provided to evaluate the effectiveness of the practice strategies as well as the challenges. 

   more » « less