Anti-Procrastination Strategies, Techniques and Tools and Their Interrelation with Self-Regulation and Self-Efficacy
Bibliographic record
Abstract
Academic procrastination generates several problems and negative consequences both individually and collectively: low student performance, increased stress, negative effects on physical and mental health, and general waste of resources. Therefore, it demands to be studied and understood in its different forms, so that mitigating mechanisms can be developed. The current work seeks to analyse the phenomenon of academic procrastination within postgraduate students registered for the Management Postgraduate Program in a Brazilian Federal University, in order to identify strategies, techniques and tools utilised by the students to overcome procrastination as well as its relation with the concepts of self-regulation and self-efficacy. The current research is descriptive with a qualitative nature, adopting a phenomenological approach through thematic analysis in order to bring to light the experiences and perceptions of individuals from their own perspectives, challenging structural or normative assumptions. In total, 24 students, 12 from the master’s degree program and 12 from the doctorate program in Management were interviewed. As a result, it was noticed that there are some overall inferences that can be taken from the narratives, outlining the phenomenon of academic procrastination and its relations with the concepts of self-regulation and self-efficacy, which led to reinforce the notion that the central elements to understand and specifically combat procrastination are related to the concepts of self-efficacy and self-regulation, which have a considerable influence on students’ motivation, behaviour and habits. The research has also pointed to some key strategies, techniques and tools that can be utilised to support further applied research as well as to guide the faculty in supporting the students to overcome or mitigate the procrastination impulse, such as use of goal orientation, visual tools, and academic support starting with undergraduate degrees. The work has an original contribution as no research has been made on that topic utilising qualitative data through a phenomenological approach, which opens a vast and new path for future research.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".