Journal ArticleDOI
Data mining in education
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Key milestones and the current state of affairs in the field of EDM are reviewed, together with specific applications, tools, and future insights.Abstract:
Applying data mining DM in education is an emerging interdisciplinary research field also known as educational data mining EDM. It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights. © 2012 Wiley Periodicals, Inc.read more
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Journal ArticleDOI
Towards teaching analytics: a contextual model for analysis of students' evaluation of teaching through text mining and machine learning classification.
Kingsley Okoye,Arturo Arrona-Palacios,Claudia Camacho-Zuñiga,Joaquín Alejandro Guerra Achem,Jose Escamilla,Samira Hosseini +5 more
Abstract: Recent trends in educational technology have led to emergence of methods such as teaching analytics (TA) in understanding and management of the teaching-learning processes. Didactically, teaching analytics is one of the promising and emerging methods within the Education domain that have proved to be useful, towards scholastic ways to make use of substantial pieces of evidence drawn from educational data to improve the teaching-learning processes and quality of performance. For this purpose, this study proposed an educational process and data mining plus machine learning (EPDM + ML) model applied to contextually analyze the teachers' performances and recommendations based on data derived from students' evaluation of teaching (SET). The EPDM + ML model was designed and implemented based on amalgamation of the Text mining and Machine learning technologies that builds on the descriptive decision theory, which studies the rationality behind decisions the learners are disposed to make based on the textual data quantification and statistical analysis. To this effect, the study determines pedagogical factors that influences the students' recommendations for their teachers, what role the sentiment and emotions expressed by the students in the SET play in the way they evaluate the teachers by taking into account the gender of the teachers. This includes how to automatically predict what a student's recommendation for the teachers may be based on information about the students' gender, average sentiment, and emotional valence they have shown in the SET. Practically, we applied the Text mining technique to extract the different sentiments and emotions (intensities of the comments) expressed by the students in the SET, and then utilized the quantified data (average sentiment and emotional valence) to conduct an analysis of covariance and Kruskal Wallis Test to determine the influential factors, as well as, how the students' recommendation for the teachers differ by considering the gender constructs, respectively. While a large proportion of the comments that we analyzed (n = 85,378) was classified to be neutral and predominantly interpreted to be positive in nature considering the sentiments (76.4%), and emotional valence (88.2%) expressed by the students. The results of our analysis shows that for the students' comments which contain some kind of positive or negative sentiment (23.6%) and emotional valence (11.8%); that females students recommended the teachers taking into account the sentiments (p = .000). While the males appear to be slightly borderline in terms of emotions (p = .056) and sentiment (p = .077). Also, the EPDM + ML model showed to be a good predictor and efficient method in determining what the students' recommendation scores for the teachers would be, going by the high and acceptable values of the precision (1.00), recall (1.00), specificity (1.00), accuracy (1.00), F1-score (1.00) and zero error-rate (0.00) which we validated using the k-fold cross-validation method, with 63.6% of optimal k-values observed. In theory, we note that not only does the proposed method (EPDM + ML) proves to be useful towards effective analysis of SET and its implications within the educational domain. But can be utilized to determine prominent factors that influences the students' evaluation and recommendation of the teachers, as well as helps provide solutions to the ever-increasingly need to advance and support the teaching-learning processes and/or students' learning experiences in a rapidly changing educational environment or ecosystem.
Journal ArticleDOI
Learning analytics in higher education: a preponderance of analytics but very little learning?
Carolina Guzmán-Valenzuela,Carolina Gómez-González,Andrés Felipe Rojas-Murphy Tagle,Alejandro Lorca-Vyhmeister +3 more
TL;DR: In this article, the publication patterns on learning analytics in higher education and their main challenges are examined by means of both a bibliometric and a content analysis, and the authors conclude that the focus is more on analytics than on learning.
Journal ArticleDOI
Guest Editorial: Special Issue on Early Prediction and Supporting of Learning Performance
TL;DR: The task of predicting students’ performance is one of the oldest and most studied tasks in Educational Data Mining (EDM) and Learning Analytics (LA), and a wide range of classification and regression approaches have been successfully applied.
Proceedings ArticleDOI
An initial review of learning analytics in Latin America
TL;DR: In this paper, the authors identify Learning Analytics initiatives in Latin America by conducting a systematic mapping of papers from Latin American authors, and also by analyzing data about research groups from Latin America (collected through an open survey).
Journal ArticleDOI
Automatic Question Classifiers: A Systematic Review
TL;DR: A systematic review of the literature on automatic question classifiers and the technology directly involved revealed that SVM is the main algorithm of the Machine Learning used, while BOW and TF-IDF are the main techniques for feature extraction and selection, respectively.
References
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Journal ArticleDOI
Educational Data Mining: A Review of the State of the Art
TL;DR: The most relevant studies carried out in educational data mining to date are surveyed and the different groups of user, types of educational environments, and the data they provide are described.
Journal ArticleDOI
Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge
TL;DR: An effort to model students' changing knowledge state during skill acquisition and a series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process.
Journal ArticleDOI
Educational data mining: A survey from 1995 to 2005
TL;DR: This paper surveys the application of data mining to traditional educational systems, particular web- based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems.
Proceedings ArticleDOI
The State of Educational Data Mining in 2009: A Review and Future Visions
Ryan S. Baker,Kalina Yacef +1 more
TL;DR: This paper reviewed the history and current trends in the field of EDM and discussed trends and shifts in the research conducted by this community, and discussed the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries, and the reduction in the frequency of relationship mining within the EDM community.
Journal ArticleDOI
Data mining in course management systems: Moodle case study and tutorial
TL;DR: This work describes the full process for mining e-learning data step by step as well as how to apply the main data mining techniques used, such as statistics, visualization, classification, clustering and association rule mining of Moodle data.
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