Bibliographic record
Abstract
As we wrap up summer, this issue of JCBE spans the globe and the process of CBE. The first two articles highlight factors in and indicators of student success. What a great way to welcome the fall issue with a focus on student success! In their manuscript entitled Factors that Contribute to Student Success and Satisfaction at Brandman University, Dr. Gilmer-Echols and her colleagues identify factors that are most influential in student success for Brandman's model of Competency-Based Education (CBE). Defining success as program longevity, the authors explored the results of student satisfaction surveys of various student services, such as academic coaching and the writing/math center. The discussion section offers a number of opportunities for further study that may be of interest to CBE researchers. In their article, Learning and Individual Differences in Skilled Competency-Based Performance: Using a Course Planning and Learning Tool as an Indicator for Student Success, Dr. Sean Gyll and his colleague at Western Governors University used a course planning and learning tool (CPLT) to identify factors in student success, such as the use of resources or the summative assessment. They defined student success as measured by planning and learning (using the course planning tool), knowledge, and confidence/experience (using a course preassessment). Implications for supporting student success include the importance of assessing the learner profile by using the CPLT. The third article, The Teacher Education Curriculum and its Competency-Based Education Attributes, comes to JCBE from Tanzania where Dr. Tarmo and his colleague at the University of Dar Es Salaam provide an analysis of the Diploma of Secondary Education to establish its integration of CBE attributes. Using a qualitative content analysis of documents and arguing that implementation of the CBE approach now mandated in K-12 schools is dependent upon teacher preparation, the author(s) found contradictory attributes in the Diploma that constrain CBE implementation in K-12 classrooms. The fourth article opens up the conversation about how to support widespread adoption of CBE, Implementing Competency-Based Education in Multiple Programs: A Workshop to Structure and Monitor Programs' Priorities Using ADDIE. Dr. LaFleur and his colleagues from Laval University Faculty of Medicine in Canada report on their support of the transition of 29 medical residency programs to CBE. They describe nine workshops they offered and how the ADDIE instructional model was used to assess and monitor levels of CBE implementation. Implications of their work extend to training faculty for CBE adoption. The fifth and final manuscript explores a new approach to developing CBE models, Developing competency frameworks using natural language processing: An exploratory study. Dr. Garman from Rush University and colleagues from Oak Park, Illinois, as well as Idaho State and DePaul universities, generate a set of leadership competency domains using a language processing approach called Latent Dirichlet Allocation (LDA). The LDA-generated competency domains are compared to a set of human-generated domains and assessed for coherence. The findings are intriguing and suggest opportunities for refinement of an LDA approach in establishing and evaluating domains of competency.
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 | 0.003 | 0.002 |
| 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) | 0.001 | 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".