Promoting Self-Learning and Autonomy with the Use of ICT in Higher Education Through Projects Close to Professional Practice
Bibliographic record
Abstract
This paper presents an academic experience about the incorporation and appropriation of various ICT in four courses of the undergraduate program in Information Technologies and Systems of the Metropolitan Autonomous University in Mexico. These courses present students’ situations close to professional practice by performing a specific role to solve problems in a particular area or project. They aim to develop the capabilities of integration and use of knowledge and skills that students have acquired in the curriculum. The paper presents the organization and structure of the courses, their content, ways of conducting and evaluation, as well as some examples of projects that have been supervised by the authors. It also describes in detail the process of conducting a project carried out with students, the ICTs that were incorporated and the possibility of innovation that the teacher has by managing these types of projects. This paper also shows the potential of the courses in the training of students, by promoting autonomy with the use of ICT, involving them in the improvement of their skills for the analysis and use of information, and fostering self-learning through situations and experiences close to their professional practice. Additionally, this experience reinforced students’ critical thinking by promoting the ability to judge different software products, as it allowed them to identify problems and find their solutions, become familiar with programming languages and develop their oral and written communication skills.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| 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".