The impact of classroom seating location and computer use on student academic performance
Bibliographic record
Abstract
A student's ability to learn effectively in a classroom setting is subject to many factors. While some factors are difficult to regulate, this study explores two factors that a student, or instructor, has full control over, namely 1) seating position, and 2) computer usage. Both factors have been studied considerably with regard to their effects on student performance, and the results indicate that sitting further from the instructor, or using a computer in the classroom, are related to a decline in grade performance. However, it is unclear if the choice of where to sit and whether or not to use a computer in class are mediated by the same cognitive process. If they are the same, then we would expect to see an interaction between the factors, such that, for example, computer usage would most negatively impact the grades of students who sit near the back of a class. This study aims to answer this question by looking at the individual and combined effects of seating position and computer usage on classroom performance. We sampled 1364 students, collecting nearly 3000 total responses across 5 different introductory psychology courses with 4 different instructors on 3 separate occasions. In agreement with previous research, we found that sitting further from the instructor negatively impacted students' grades (0.75 percentage points/row), and using a computer in class negatively impacted grades (by 3.88 percentage points). Our novel finding is that these deleterious effects combined in an additive manner, such that using a computer had the same harmful effect on grade performance regardless of whether the student sat at the front or back of the classroom.
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 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".