Pre-procesamiento de datos educativos desde un enfoque de dominio específico.

The data analysis processes to discover knowledge require pre-processing before applying techniques or algorithms to increase the quality of the data and adapt them to the formats that are best suited for processing, especially when the data comes from different sources. This article presents the experience in designing and constructing a strategy with a specific domain approach for the educational data preparation process. The study methodology included three stages: (1) design and construction of the strategy, (2) recognition and data selection, and (3) application of the strategy and review of results. The study was made up of data from the primary and secondary education system in the Norte de Santander department (Colombia). In addition, there was data referring to the enrollment process, which includes socioeconomic and family variables and data from evaluations of students' academic performance from three public educational institutions. For the two sources, data were processed from 2014 to 2018, with more than eight hundred thousand records. This work adds value in three main aspects: the scope concerning the educational level where the case study data comes from, the inclusion of the domain-specific approach in the solution, and the centralization of the data from multiple sources, resulting in data available in subsequent analysis processes. In conclusion, this work contributed both in the research field and applying knowledge in an existing case. Furthermore, it opened the possibility of carrying out subsequent tests with other data types from the educational context.Los procesos de análisis de datos requieren preprocesamiento antes de la aplicación de técnicas o algoritmos, con el fin de incrementar la calidad de estos y adecuarlos a los formatos necesarios para su procesamiento, principalmente cuando los datos provienen de diferentes fuentes. El presente artículo expone la experiencia en el diseño y construcción de una estrategia con enfoque de dominio específico para el proceso de preparación de datos educativos. La metodología del estudio incluyó tres etapas: (1) diseño y construcción de la estrategia para preparación de los datos educativos, (2) reconocimiento y selección de datos y (3) aplicación de la estrategia y revisión de resultados. El caso de estudio estuvo conformado por datos provenientes del sistema de educación básica y media del departamento Norte de Santander (Colombia). Se contó con datos referentes al proceso de matrícula, los cuales incluyen variables de tipo socioeconómico y familiar y con datos de valoraciones de desempeño académico de estudiantes provenientes de tres instituciones educativas de carácter público. Para las dos fuentes se procesaron datos de los años 2014 a 2018, con un total de más de ochocientos mil registros. Este trabajo aporta valor en tres aspectos principalmente: el alcance respecto al nivel educativo de dónde provienen los datos del caso de estudio, la inclusión del enfoque de dominio específico en la solución y la centralización de los datos de las múltiples fuentes, resultando datos disponibles para posteriores procesos de análisis. En conclusión, este trabajo contribuyó tanto en el ámbito investigativo como en la aplicación del conocimiento en un caso existente y abrió la posibilidad de realizar pruebas posteriores con otro tipo de datos del contexto educativo

Mobile-assisted language learning through learning analytics for self-regulated learning (MALLAS): A conceptual framework

Many adult second and foreign language learners have insufficient opportunities to engage in language learning. However, their successful acquisition of a target language is critical for various reasons, including their fast integration in a host country and their smooth adaptation to new work or educational settings. This suggests that they need additional support to succeed in their second language acquisition. We argue that such support would benefit from recent advances in the fields of mobile-assisted language learning, self-regulated language learning, and learning analytics. In particular, this paper offers a conceptual framework, mobile-assisted language learning through learning analytics for self-regulated learning (MALLAS), to help learning designers support second language learners through the use of learning analytics to enable self-regulated learning. Although the MALLAS framework is presented here as an analytical tool that can be used to operationalise the support of mobile-assisted language learning in a specific exemplary learning context, it would be of interest to researchers who wish to better understand and support self-regulated language learning in mobile contexts

Essays on the Design of Inclusive Learning in Massive Open Online Courses, and Implications for Educational Futures

This thesis examines the tensions and contradictions of Massive Open Online Courses (MOOCs) as a force for more inclusive tertiary education, particularly for adults without a college degree in the United States. Through a multimethodological research approach yielding three discrete papers, presented as chapters, this work seeks to augment and clarify the existing MOOCs literature across conceptual, quantitative, and qualitative domains. The first paper develops a conceptual framework, ‘hegemonic design bias,’ that describes the socio-technical development ecosystem in which MOOCs are embedded. This framework helps account for why MOOCs have yet to serve as a democratising force in education by highlighting the processes and constraints that bias MOOC production toward the already well-educated. The potential economic implications of these developments are also considered. The second paper provides insight into how underrepresented learners are engaging with entry-level MOOCs. The exploration of learning analytic data from an initial sample of more than 260,000 enrolees through cluster analysis and multinomial logistic regression indicates that students without a college degree are more likely to be high-performing learners compared to college-educated students in these courses. Additionally, students from approximated lower socioeconomic backgrounds are no less likely to be successful than students from approximated middle and higher socioeconomic backgrounds in these courses. The third paper provides insight into the opportunities and challenges producers face in building inclusive MOOCs through a qualitative analysis of six semi-structured interviews. The interviews unearthed diverse conceptions of inclusion among producers that reflect a sincere normative commitment to make inclusive MOOCs, though the conceptions were quite distinct and fragmented. Producers were intentional about utilising best-practice pedagogy, as well as innovative program design, to include diverse learners. Innovative technology partners helped create interactive, unique experiences, but this also led to challenges in harmonising the design process and required the considerable influence of intermediary actors. To conclude, I briefly consider the implications of these findings for research, practice, and policy, with particular attention to how the public and social sectors can incentivise improved design of MOOCs with the specific intent of helping adults without college degrees develop human capital in order to remain economically resilient amidst the disruptions of skills-biased technological change.Gates Cambridge Trus

Inclusive learning with assistant chatbot in massive open online courses : examining students’ perceptions, utilizations, and expectations

This study examines students’ learning experiences with an assistant chatbot in professional development MOOCs designed for journalists. Utilizing a mixed-methods approach, it focuses on the students’ learning experience’s sub-domains, such as social presence, teaching and cognitive presence, self-regulation, ease of use, and behavioral intention. Employing the Community of Inquiry (CoI) framework and the Technology Acceptance Model (TAM), the study first assesses the impact of demographics like age, gender, region, and native language on these learning experiences. The study revealed that age and gender had no significant influence on learning experiences, while geo-cultural regions showed variations, particularly in social presence and, teaching and cognitive presence. Socioeconomic regions demonstrated more notable differences, especially between lower-middle and high-income areas. However, the native language did not significantly influence learning experiences. Second, structural equation modeling (SEM) validated several hypothesized relationships, highlighting the positive impact of self-regulation on various other learning domains. Interestingly, teaching and cognitive presence did not significantly influence behavioral intention, nor was there a significant relationship between behavioral intention and use time. Age and socioeconomic region factors were identified as full moderators, while gender was a partial moderator from multigroup SEM results. Third, an extensive analysis of student interactions with the chatbot was conducted using various data sources. This analysis revealed eight key topics of chatbot interactions and showed predominantly neutral sentiments in the chatbot text logs. However, survey and interview data indicated a generally positive perception of the chatbot, especially noting its operational effectiveness and ease of use. Sentiments varied across socioeconomic regions, with more positive feedback from lower-income regions, while those from higher-income regions had higher expectations. The study also observed differences in navigational patterns between chatbot users and non-users in the course. Chatbot users exhibited more diverse navigations, indicating deeper engagement with course materials and a higher completion rate. In contrast, non-users followed a more structured progression, mainly relying on the predefined course path. Finally, the study highlighted students’ expectations for the chatbot, emphasizing the need for improvements in response accuracy, diversity, and additional capabilities like multi-language support. The findings emphasize the role of demographic variables in shaping student interactions with chatbots in MOOCs and suggest that modifying chatbot responses for inclusiveness could be key in meeting diverse student needs. The implications include that adhering to Universal Design for Learning principles, empowered by current advancements in AI-based chatbot technology, and informed by the CoI and TAM, could better address the diverse needs in MOOCs, especially in chatbot-enhanced learning environments.Curriculum and Instructio

Integrating multiple data sources for learning analytics—review of literature

Learning analytics (LA) promises understanding and optimization of learning and learning environments. To enable richer insights regarding questions related to learning and education, LA solutions should be able to integrate data coming from many different data sources, which may be stored in different formats and have varying levels of structure. Data integration also plays a role for the scalability of LA, an important challenge in itself. The objective of this review is to assess the current state of LA in terms of data integration in the context of higher education. The initial search of six academic databases and common venues for publishing LA research resulted in 115 publications, out of which 20 were included in the final analysis. The results show that a few data sources (e.g., LMS) appear repeatedly in the research studies; the number of data sources used in LA studies in higher education tends to be limited; when data are integrated, similar data formats are often combined (a low-hanging fruit in terms of technical challenges); the research literature tends to lack details about data integration in the implemented systems; and, despite being a good starting point for data integration, educational data specifications (e.g., xAPI) seem to seldom be used. In addition, the results indicate a lack of stakeholder (e.g., teachers/instructors, technology vendors) involvement in the research studies. The review concludes by offering recommendations to address limitations and gaps in the research reported in the literature

Integrating multiple data sources for learning analytics—review of literature

Learning analytics (LA) promises understanding and optimization of learning and learning environments. To enable richer insights regarding questions related to learning and education, LA solutions should be able to integrate data coming from many different data sources, which may be stored in different formats and have varying levels of structure. Data integration also plays a role for the scalability of LA, an important challenge in itself. The objective of this review is to assess the current state of LA in terms of data integration in the context of higher education. The initial search of six academic databases and common venues for publishing LA research resulted in 115 publications, out of which 20 were included in the final analysis. The results show that a few data sources (e.g., LMS) appear repeatedly in the research studies; the number of data sources used in LA studies in higher education tends to be limited; when data are integrated, similar data formats are often combined (a low-hanging fruit in terms of technical challenges); the research literature tends to lack details about data integration in the implemented systems; and, despite being a good starting point for data integration, educational data specifications (e.g., xAPI) seem to seldom be used. In addition, the results indicate a lack of stakeholder (e.g., teachers/instructors, technology vendors) involvement in the research studies. The review concludes by offering recommendations to address limitations and gaps in the research reported in the literature

Integrating multiple data sources for learning analytics—review of literature

Learning analytics (LA) promises understanding and optimization of learning and learning environments. To enable richer insights regarding questions related to learning and education, LA solutions should be able to integrate data coming from many different data sources, which may be stored in different formats and have varying levels of structure. Data integration also plays a role for the scalability of LA, an important challenge in itself. The objective of this review is to assess the current state of LA in terms of data integration in the context of higher education. The initial search of six academic databases and common venues for publishing LA research resulted in 115 publications, out of which 20 were included in the final analysis. The results show that a few data sources (e.g., LMS) appear repeatedly in the research studies; the number of data sources used in LA studies in higher education tends to be limited; when data are integrated, similar data formats are often combined (a low-hanging fruit in terms of technical challenges); the research literature tends to lack details about data integration in the implemented systems; and, despite being a good starting point for data integration, educational data specifications (e.g., xAPI) seem to seldom be used. In addition, the results indicate a lack of stakeholder (e.g., teachers/instructors, technology vendors) involvement in the research studies. The review concludes by offering recommendations to address limitations and gaps in the research reported in the literature