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This WIP paper presents new research on exploratory learning, an educational technique that reverses the order of standard lecture-based instruction techniques. In exploratory learning, students are presented with a novel activity first, followed by instruction. Exploratory learning has been observed to benefit student learning in foundational math and science courses such as calculus, physics, and statistics; however, it has yet to be applied to engineering topics such as programming. In two studies, we tested the effectiveness of exploratory learning in the programming unit of a first-year undergraduate engineering course. We designed a new activity to help students learn about different python error types, ensuring that it would be suitable for exploration. Then we implemented two different orders (the traditional instruct-first versus exploratory learning’s explore-first) across the six sections of the course. In Study 1 (N=406), we did not detect a difference between the instruct-first and explore-first conditions. In Study 2 (N=411), we added more scaffolding to the activity. Students who received the traditional order of instruction followed by the activity scored significantly higher on the assessment. These findings contradict the exploratory learning benefits typically shown, shedding light on potential boundary conditions to this effect. 

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. 

Understanding models is important for engineering students, but not often taught explicitly in first-year courses. Although there are many types of models in engineering, studies have shown that engineering students most commonly identify prototyping or physical models when asked about modeling. In order to evaluate students’ understanding of different types of models used in engineering and the effectiveness of interventions designed to teach modeling, a survey was developed. This paper describes development of a framework to categorize the types of engineering models that first-year engineering students discuss based on both previous literature and students’ responses to survey questions about models. In Fall 2019, the survey was administered to first-year engineering students to investigate their awareness of types of models and understanding of how to apply different types of models in solving engineering problems. Students’ responses to three questions from the survey were analyzed in this study: 1. What is a model in science, technology, engineering, and mathematics (STEM) fields?, 2. List different types of models that you can think of., and 3. Describe each different type of model you listed. Responses were categorized by model type and the framework was updated through an iterative coding process. After four rounds of analysis of 30 different students’ responses, an acceptable percentage agreement was reached between independent researchers coding the data. Resulting frequencies of the various model types identified by students are presented along with representative student responses to provide insight into students’ understanding of models in STEM. This study is part of a larger project to understand the impact of modeling interventions on students’ awareness of models and their ability to build and apply models. 

To succeed in engineering careers, students must be able to create and apply models to certain problems. The different types of modeling skills include physical, mathematical, computational, graphing, and financial. However, many students struggle to define and form relevant models in their engineering courses. We are hoping that the students are able to better define and apply models in their engineering courses after they have completed the MATLAB and/or CATIA courses. We also are hoping to see a difference in model identification between the MATLAB and CATIA courses. All students in the MATLAB and CATIA courses must be able to understand and create models in order to solve problems and think critically in engineering. Students need foundational knowledge about basic modeling skills that will be effective in their course. The goal is for students to create an approach to help them solve problems logically and apply different modeling skills.