Semi-Automatic Generation of Competency Maps Based on Educational Data Mining

Alfonso, David; Manjarrés, Ángeles; Pickin, Simon

doi:10.2991/ijcis.d.190627.001

Cited by 3 publications

(3 citation statements)

References 40 publications

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“… Association rules as rule-based statements, that aim to find interesting relationships between data items in large datasets, were used in [108] to discover the relationships between answers to different test questions in order to be able to infer from them the relationships between competences in an educational area. In [28] a subtype of these, called Causal Data Mining, was used to find causal relationships between different events.…”

Section: Datasets and Toolsmentioning

confidence: 99%

“… Evaluation and validation: Rigorous evaluation of models and methods is essential to ensure that the results obtained are valid and reliable. However, there are cases where model validation is still subjective, with no metrics being used to assess the results obtained [108]. www.ijacsa.thesai.org  Prediction vs. interpretation: Another issue is the balance between creating accurate predictive models and understanding the reasons behind these predictions.…”

Section: Datasets and Toolsmentioning

confidence: 99%

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Educational Data Mining in European Union – Achievements and Challenges: A Systematic Literature Review

Simionescu,

Danubianu,

Gradinaru

et al. 2024

IJACSA

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The quality of education is one of the pillars of sustainable development, as set out in "The 2030 Agenda for Sustainable Development", adopted by all United Nations Member States in 2015. Recent social and technological developments, as well as events such as the COVID-19 pandemic or conflicts in many parts of the world, have led to essential changes in the way education processes are carried out. In addition, they have made it possible to generate, collect and store large amounts of data related to these processes, data that can hide useful information for decisions that, in the medium or long term, can lead to a significant increase in the quality of education. Uncovering this information is the subject of Educational Data Mining. To understand the state-of-the-art reflected by recent developments, trends, theories, methodologies, and applications in this field, in the European Union, we considered it appropriate to conduct a systematic and critical literature review. Our paper aims to identify, analyze, and synthesize relevant information from these articles, both to build a foundation for further studies and to identify gaps or unexplored issues that can be addressed in future research. The analysis is based on research identified in three international databases recognized for content quality: Scopus, Science direct, and IEEEXplore.

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Section: Datasets and Toolsmentioning

confidence: 99%

Section: Datasets and Toolsmentioning

confidence: 99%

Educational Data Mining in European Union – Achievements and Challenges: A Systematic Literature Review

Simionescu,

Danubianu,

Gradinaru

et al. 2024

IJACSA

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“…Lytvynenko et al (2019a) 2019) learn a BN to analyze the variables that affect entrepreneurial intention and discover that the most important ones are self-efficacy, convenience, attitude, and social norm. Alfonso, Manjarrés and Pickin (2019) propose a semi-automatic method of educational competency maps from a repository of multiple-choice questionnaire answers. In Li and Liu (2018), a Dynamic Bayesian Network is used in conjunction with Wavelet decomposition to predict the path of a storm.…”

Section: Bayesian Network Structural Learning (Bnsl)mentioning

confidence: 99%

Bayesian network quantization method and structural learning

Ribeiro

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To my colleagues at the Signal Processing Laboratory for all the help and companionship. My thesis would not be the same without your help and suggestions during the whole process. A special thanks to Henrique and Matheus who were always at the lab and brought a lot of joy. Finally, I would like to thank Professor Maciel for helping me start my research path, from when I was an undergraduate student until now. Your help was essential for me to reach this point.

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