Modelo de integración de la competencia digital del docente universitario para su desarrollo profesional en la enseñanza de la matemática – Universidad Tecnológica Equinoccial de Ecuador
Bibliographic record
Abstract
Resumen:Esta investigación se centra en el desarrollo de un modelo de integración de competencias digitales del docente universitario para su desarrollo profesional en la enseñanza de la matemática, como escenario para el crecimiento y fortalecimiento del ejercicio profesional docente. Metodológicamente se analizaron los aspectos más relevantes de las diferentes normas y modelos propuestos por varios ministerios de educación, entre ellos Australia, Bélgica, Canadá, Chile, España, Estados Unidos, Francia, Noruega y otros, así como organizaciones como la UNESCO O ISTE, y las investigaciones de prestigiosas universidades (Pozos Pérez, 2009; Carrera y Coiduras, 2012; Prendes y Gutiérrez, 2013). Este estudio determina un perfil preliminar del profesorado del área de matemáticas, en el que se describen las dimensiones, competencias e indicadores que desarrolla el profesorado de una universidad de Ecuador y que fue validado por un grupo de expertos internacionales en competencias digitales. Los resultados muestran que el perfil está compuesto por 44 indicadores que corresponden a 21 habilidades digitales que responden a los niveles: básico, intermedio y avanzado de las cuestiones de dominio, uso e innovación en cinco áreas: información y alfabetización, comunicación y colaboración, creación de contenidos digitales, seguridad y resolución de problemas. Abstract:This research focuses on developing a model of digital competence integration of the university teacher for their professional development in the teaching of mathematic, as a scenario for the growth and strengthening of the professional teaching practice. Methodologically, the most relevant aspects of the different standards and models proposed by several ministries of education were analyzed, among them Australia, Belgium, Canada, Chile, Spain, the United States, France, Norway and others, as well as organizations such as UNESCO or ISTE, and the researches of prestigious universities (Pozos Pérez, 2009; Carrera y Coiduras, 2012; Prendes y Gutiérrez, 2013). This study determines a preliminary profile of teachers in the area of mathematics, which describes the dimensions, competencies and indicators developed by teachers of a university in Ecuador and validated by a group of international experts in digitals competence teacher. The results show that the profile is composed by 44 indicators corresponding to 21 digital competences that respond to the basic, intermediate and advanced levels of domain, use and innovation in five areas: information and information literacy, communication and collaboration, creation of digital content, security and problem solvingKeywords: ICT, Mathematics, Digital competences, Higher education
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".