Enhancing Understanding and Retention in Undergraduate ECE Courses through Concept Mapping
Bibliographic record
Abstract
Abstract Concept mapping is well recognized for its effectiveness in promoting deep learning and aiding students in understanding knowledge acquisition in complex subjects. In undergraduate ECE courses, instructors usually present topics one by one, followed by examples and applications. Instructors can easily navigate all course information due to their well-established understanding of the entire course and the connections between its topics. However, students face the challenge of establishing the connection between their existing knowledge and the new concepts and reinforcing those connections through repeated practice. In this work, we introduced concept mapping as an assessment tool to help students build these links and enhance their learning experience. The goal is to improve students' comprehension, retention, and interconnectivity of complex course topics. We have systematically integrated concept mapping into four distinct courses: a freshman course about electronics (ECE 110), a sophomore course about signal processing (ECE 210), and two junior-level courses about electromagnetics (ECE 329) and green energy (ECE 333). In each course, students were asked to create their own concept maps before midterm exams. The maps were scored qualitatively by the instructor based on the number of concepts and their structures. This exercise was designed to encourage students to consolidate their knowledge and foster a deeper understanding of the course material by visualizing and summarizing the relationships between key topics. This type of active learning also empowers students to take ownership of their learning by creating and revising their concept maps. A fundamental aspect of our course improvement work involved gathering feedback from students regarding their perceptions of the effectiveness of concept mapping in these courses. In each course, a survey was administered at the end of the semester to gauge students' experiences, opinions, and reflections. Our findings from the surveys indicate that concept mapping is perceived positively by a significant proportion of the students, especially if it's actively used as an instruction tool during the semester. Students reported that concept mapping enhanced their understanding of the course material.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".