Producción Científica

La evaluación de la investigación es actualmente uno de los temas de mayor relevancia y controversia en la ciencia y la academia, especialmente en el ámbito universitario. Esta evaluación se utiliza comúnmente para clasificar instituciones, grupos, productos académicos como las revistas científicas, y al personal docente investigador. Los indicadores de calidad e impacto de la investigación influyen decisivamente en el ascenso en carreras académicas, selección de beneficiarios de becas e incentivos, culminación de programas de alto nivel (como maestrías y doctorados), asignación de recursos en instituciones y centros de investigación, y en la definición de políticas públicas a nivel institucional y gubernamental.

Scientometric assessments of Open Educational Resources (OER) offer a way to quantitatively represent teaching in higher education through openly available and accessible artefacts. They could serve science policy monitoring and lead to greater visibility of higher education teaching in a recognition and reward system. In this context, we discuss possible statistics for OER. In a pre-study, a first version of OER indicators was discussed in three focus groups. The findings of these discussions were incorporated into the creation of a more comprehensive second version of a framework for OER statistics, which was evaluated in detail in six expert interviews. After incorporating changes as a result of the evaluation, a third version of the framework for OER statistics emerged that enables scientometric measurements of OER, while considering the common criticisms of scientometric measurements. The framework comprises an individual level, which recognizes all OER created by an individual, and an institutional level, which serves to quantify OER created by an institution. At the individual level, productivity, cooperation, resonance, openness, altmetric and transfer indicators are available. In addition, we record dichotomously whether an OER certification exists. At the institutional level, additional support indicators are proposed to recognize achievements in the development and maintenance of OER-promoting structures at institutional level.

Machine learning, a subset of artificial intelligence, has experienced rapid advancements and applications across various domains. In education, its integration holds great potential to revolutionize teaching, learning, and educational outcomes. Despite the growing interest, there needs to be more comprehensive bibliometric analyses that track the trajectory of machine learning’s integration into educational research. This study addresses this gap by providing a nuanced perspective derived from bibliometric insights. Using a dataset from 1986 to 2022, consisting of 449 documents from 145 sources retrieved from the Web of Science (WoS), the research employs network analysis to unveil collaborative clusters and identify influential authors. A temporal analysis of annual research output sheds light on evolving trends, while a thematic content analysis explores prevalent research themes through keyword frequency. The findings reveal that co-authorship network analysis exposes distinct clusters and influential figures shaping the landscape of machine learning in educational research. Scientific production over time reveals a significant surge in research output, indicating the field’s maturation. The co-occurrence analysis emphasizes a collective focus on student-centric outcomes and technology integration, with terms like “online” and “analytics” prevailing. This study provides a nuanced understanding of the collaborative and thematic fabric characterizing machine learning in educational research. The implications derived from the findings guide strategic collaborations, emphasizing the importance of cross-disciplinary engagement. Recommendations include investing in technological infrastructure and prioritizing student-centric research. The study contributes foundational insights to inform future endeavors in this ever-evolving field.

This study was conducted to determine EFL students’ skills in writing a thesis introduction. Five student dipilih berdasarkan level keterampilan menulis di mana kelima mahasiswa ini merupakan mahasiswa terbaik. The analysis method used is thematic progression. The student texts were analyzed for each paragraph, then counted how many sentences applied the Constant Theme Pattern, Linear Theme Pattern, Split Theme Pattern, and Derived Theme Pattern. The findings indicated that from the total 194 sentences being analysed, seventy-six employed this pattern to connect ideas. Another aspect of connecting ideas is the cohesive devices used. The higher application of definite articles, pronouns and word repetition is evident. Students majorly drew definite articles and demonstrative pronouns (e.g. this, that) which then makes it adaptable for readers to follow their idea development in a paragraph. Regarding the coherence, students in this study significantly relied on the theme of sentences to be the source of their idea development. Despite the clarity and coherence being well managed in students’ writing, the link between ideas can still be diversed by employing other patterns. Moreover, some rhemes contain new information that need further elaboration as the text grows. Students have applied the rules of thematic progression, although there are several sentences that do not comply with the principles of cohesion and coherence. Guidance and feedback from the lecturer is really needed so that writing skills are maintained, especially in writing the introduction to the thesis.