Assessing the Impact of Reference Assistance and Library Instruction on Retention and Grades Using Student Tracking Technology
Bibliographic record
Abstract
Abstract Objective – To assess the impact of community college academic librarians upon student retention and grades through reference desk visits and attendance in library instruction classes. Methods – Student ID data used for this research was collected from students that visited the reference desk to consult about a course-related question or attended a library instruction class for a specific course. After consenting to share their student ID number, the students’ IDs were scanned and uploaded to a Blackboard Analytics data warehouse. A Pyramid Analytics reporting tool was used to query and extract student-level retention and grade data based upon whether the student had visited the reference desk or attended a library instruction class. Chi-square and Fisher’s exact tests were used to discern any statistical difference in retention rates and grades between students that engaged a librarian through reference or instruction and the general student population. Results – When comparing fall-to-fall retention for all degree-seeking students, students that visited the reference desk or attended a library instruction class had a statistically higher rate of retention. When comparing fall-to-fall retention within low-retention student cohorts, students that visited the reference desk or attended a library instruction class had higher rates of retention among all low-retention cohorts. Eight of 10 cohorts were statistically higher for library instruction and 6 of 10 cohorts were statistically higher for reference visits. With respect to course grades, only 1 of 5 high enrollment courses showed a higher grade average for students that attended a library instruction class. None of the differences in average grades between students that attended a library instruction class and all students in the five courses were statistically significant. For the impact of a reference visit upon a course grade, all five courses showed a higher average grade average for students that visited the reference desk for a question related to their course than all students in the course. Four of the 5 differences were statistically significant. Conclusions – The data collected by systematically tracking students that interact with community college librarians suggests that reference desk visits and attendance of library instruction classes both have a positive, statistically significant impact upon student retention. When looking at course grades, the data does not indicate a statistically significant positive or negative impact for library instruction. The impact of visiting the reference desk upon course grades does suggest a strong, statistically significant positive correlation.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".