Research and main contributions
| Articles | Themes covered |
|---|---|
| Anoopkumar and Rahman (2016) | It addresses the most commonly used techniques and methods in assessing student performance, which comprises improvements in curricular and pedagogical frameworks. It details users and tools, data mining methods and algorithms applied to the EDM context |
| Schwendimann et al. (2016) | A review on learning dashboards and their wide use among students, educators and administration to perform monitoring and tracking of them. Details types of dashboards and their main functionalities |
| They discuss learning dashboards, their respective objectives, data source and platforms, indicators and technologies used for their construction. Applicability related to their users (educators, students, administrators and researchers) and learning scenarios (formal, nonformal and informal, educational level and pedagogical approach) is discussed | |
| Bodily and Verbert (2017) | The main theme of the review is student-focused learning dashboards, placing the student as the main actor in the process, where he is the analyst and can better manage his learning. Cites types of systems and most commonly used analytics. Proposes that topics such as usability and recommendations be included in future work |
| Dutt et al. (2017) | It deals mainly with the analysis of student behavior, learning style and collaborative learning in e-learning environments |
| Vieira et al. (2018) | It addresses the topic of information visualization in the field of education. The paper points out what visual tools for learning analysis have been used. It discusses existing approaches, the purposes, contexts and data sources used. They point out that there are still many gaps to be filled, especially in classroom education, where data collection is still more difficult |
| Bakhshinategh et al. (2018) | They discuss the use of applications and methods that seek to understand student behavior. Classify these applications based on their goals and end-users |
| Gardner and Brooks (2018) | It covers works that study predictive modeling in MOOCs. Conducts a detailed breakdown of models and points out the importance of creating these for possible personalized support with interventions, adaptive content creation and optimized course grids. They discuss which types of data and structure would be the most appropriate. It defines and differentiates MOOCs and other forms of traditional teaching. Suggests future work on the development of more robust experimental models with larger populations and more realistic contexts |
| Rodrigues et al. (2018) | The author surveys publications on e-learning that propose improvements in teaching and learning, bringing the most relevant themes and areas of research. They point out the perspectives and trends of e-learning |
| Aldowah et al. (2019) | The author compares techniques used in EDM and LA, linking them with their applicability. The objective is to provide educational institutions with the best tools for the continuous improvement of this company, consequently identifying the most appropriate technique to be used |
| Abu Saa et al. (2019) | This work presents a study on the performance of students in higher education. Moreover, it summarizes the factors that most affect this performance and the main methods to predict them |
| Du et al. (2020) | They discuss the main research trends and topics covered in the years between 2007 and 2019 in the EDM. Among the main ones are performance prediction, decision support for educators and students, behavior detection and student modeling, algorithm comparison or optimization |
| Themes covered | |
|---|---|
| It addresses the most commonly used techniques and methods in assessing student performance, which comprises improvements in curricular and pedagogical frameworks. It details users and tools, data mining methods and algorithms applied to the EDM context | |
| A review on learning dashboards and their wide use among students, educators and administration to perform monitoring and tracking of them. Details types of dashboards and their main functionalities | |
| They discuss learning dashboards, their respective objectives, data source and platforms, indicators and technologies used for their construction. Applicability related to their users (educators, students, administrators and researchers) and learning scenarios (formal, nonformal and informal, educational level and pedagogical approach) is discussed | |
| The main theme of the review is student-focused learning dashboards, placing the student as the main actor in the process, where he is the analyst and can better manage his learning. Cites types of systems and most commonly used analytics. Proposes that topics such as usability and recommendations be included in future work | |
| It deals mainly with the analysis of student behavior, learning style and collaborative learning in e-learning environments | |
| It addresses the topic of information visualization in the field of education. The paper points out what visual tools for learning analysis have been used. It discusses existing approaches, the purposes, contexts and data sources used. They point out that there are still many gaps to be filled, especially in classroom education, where data collection is still more difficult | |
| They discuss the use of applications and methods that seek to understand student behavior. Classify these applications based on their goals and end-users | |
| It covers works that study predictive modeling in MOOCs. Conducts a detailed breakdown of models and points out the importance of creating these for possible personalized support with interventions, adaptive content creation and optimized course grids. They discuss which types of data and structure would be the most appropriate. It defines and differentiates MOOCs and other forms of traditional teaching. Suggests future work on the development of more robust experimental models with larger populations and more realistic contexts | |
| The author surveys publications on e-learning that propose improvements in teaching and learning, bringing the most relevant themes and areas of research. They point out the perspectives and trends of e-learning | |
| The author compares techniques used in EDM and LA, linking them with their applicability. The objective is to provide educational institutions with the best tools for the continuous improvement of this company, consequently identifying the most appropriate technique to be used | |
| This work presents a study on the performance of students in higher education. Moreover, it summarizes the factors that most affect this performance and the main methods to predict them | |
| They discuss the main research trends and topics covered in the years between 2007 and 2019 in the EDM. Among the main ones are performance prediction, decision support for educators and students, behavior detection and student modeling, algorithm comparison or optimization |
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