Multilevel Analysis of Academic Goals
Bibliographic record
Abstract
Based on a literature research, we found plenty of different goal characteristics. However, these variables on which goals vary, which we call goal dimensions (Austin & Vancouver, 1996), were not measured with comparable items across the literature. Hence, we developed a comprehensive measure of goals by first extracting "common" goal dimensions (being measured in at least 10 publications and having found at least 10 items in the literature review). We then conducted a study asking participants 2-3 items from each common goal dimension and conducted an exploratory factor analysis resulting in 9 factors (which we named: Expectancy, Progress, Enjoyment, Support, Value, External Motivation, Demand, inter-goal Facilitation, & Commitment) with 3 items each. Then, we confirmed the factor structure by another study and confirmatory factor analysis. This resulted in a goal dimension questionnaire. Now, we want to validate this instrument by a field study. In this study we will ask students from the University of Siegen about their performance in an academic goal, i.e. a course exam. Recruitment will be done at the university campus where participants will be introduced to the longitudinal study and the first questionnaire battery. This includes the goal dimension questionnaire as well as instruments asking for the following constructs: Satisfaction with life, Scale of positive and negative experiences, Big-5 XS, Big-5 conscientiousness, trait Self-control, and Metacognition in Self-control. The second measurement time point is 7 days before the exam, the third point one day after the exam and the last time point 6 weeks after the exam. We want to predict the goal dimension "progress" and well-being by the remaining goal dimensions. We want to analyse prevalent variance according to the levels: 1) variance of goals across time, 2) variance between goals and 3) variance in goals between persons and we want to test the reliability of goal dimension variance found by our new year resolution study.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.023 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.017 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.209 | 0.003 |
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; both teacher heads agree on what is shown here.
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".