Bibliographic record
Abstract
Editor's Note Elizabeth A. Jones According to Banta, Jones, and Black (2009), outcomes assessment can be sustained over time if there is sufficient planning and implementation that occurs in a culture that values data or information and uses that evidence to make crucial decisions. The authors in this issue of JGE: The Journal of General Education carefully analyze and report key assessments that they have completed in specific types of courses. In the first article, Marie-France Orillion investigates the relationship between interdisciplinary curriculum and the outcomes that students achieve. She conducted a formal study and analyzed student work including take-home essay exams at the middle and end of the quarter as well as online discussions. The results from these assessments revealed some issues that faculty decided should be addressed within the curriculum. This article is instructive because it shows an example of assessments that can be used to make informed changes. Assessing what students learn in the major is very important. Neva Sanders-Dewey and Stephanie Zaleski conducted an assessment study to determine if students who take psychology courses gain significant knowledge as gleaned through pre- and posttests. They found that the number of psychology courses a student completes and grade point averages were significant predictors of factual knowledge demonstrated on the posttest. This type of study indicates a strategy for assessing student learning over time by using locally developed tests to identify significant gains in critical knowledge areas. It is sometimes challenging to assess student participation in classroom discussions. Kristine Bruss designed a project to create and communicate criteria to assess students as well as provide opportunities for guided practice and feedback. A humanities team developed a rubric that identified key areas of student performance important for effective discussion participation and then described student achievement at various levels that aligned with the key performance areas. The faculty members through these assessments discovered [End Page vii] insightful information about student performance particularly regarding students' strengths and limitations. Finally, in "Introducing a Culture of Civility in First-Year College Classes," Robert Connelly reviews key definitions and descriptions of civility. He then builds a case for educating students about civility in the first-year classroom. He proposes a model where students learn what is acceptable behavior and the norms and values that become part of the content that is facilitated by instructors. I hope that you find these articles to be useful and meaningful as instructors and consider implementing or revising assessments and using the results to identify key changes. As you read these articles, you may consider sharing some of your own work with JGE. References Banta, T. W., Jones, E. A., & Black, K. (2009). Designing effective assessment: Principles and profiles of good practice. San Francisco: Jossey-Bass. [End Page viii] Google Scholar Copyright © 2009 The Pennsylvania State University
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.069 | 0.041 |
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 source (direct Gemma or distilled Codex), 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".