An Analysis of Student Performance at the Intersection of Diversity and Information Literacy
Bibliographic record
Abstract
Abstract Objective – When teaching Information Literacy (IL) concepts, instructors often have no knowledge about the background or previous IL exposure of the students they are teaching. This study aims to create a holistic picture of the students at a large Midwestern United States university in a first year introductory course on the design process for solving engineering problems. Methods – Institutional data and course level data were traced and linked to individual students in an introduction to design thinking first year course. This course is at a major high research activity institution in the Midwestern United States. From a total course size of 650, institutional and course level data of 127 students were selected randomly and analyzed. Some data points are self-reported and some data points are performance-based. Results – Underrepresented minorities (URMs) had a higher increase in IL score from assignment 1 to assignment 3 than non-URM students. However, non-URMs performed higher on both the first and the last assignments. Students in concurrent IL designated courses had a higher increase from assignments 1 to 3 than those not in simultaneous IL designated courses. Black and international students had the highest increases from assignments 1 to 3 of any demographic. Regarding IL, the fact that none of the students had been exposed to much IL instruction justified continued collaboration in the course between the instructor of record and the IL specialist. There were significantly negative correlations between the final grade and first-generation status. Legacy students also performed more poorly from assignments 1 to 3. Conclusion – Students are more diverse in a single classroom setting than presumed prior to research; therefore, our instructional practices should be diverse and inclusive, as well. More preparation work and fact finding should be conducted by library faculty and instructors to facilitate the learning of the students, and not just the act of teaching. Librarians could ask for more information about the course demographics and respond accordingly. Librarians should also be properly trained in instructional practices to be better equipped to meet the expectations and challenges of teaching a diverse class.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.720 |
| 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".