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A Convergence of Mining and Machine Learning: The New Angle for Educational Data Mining
Published in Vishal Jain, Akash Tayal, Jaspreet Singh, Arun Solanki, Cognitive Computing Systems, 2021
In [10], Beck and Woolf have developed models using machine learning for predicting student behavior and support decision making. According to Dede [11], there is a lot of development in distance education with emerging technologies and distributed learning. The author has also focused on pedagogical strategies and designs in the educational system. In [12], Demšar et al. have discussed the Orange framework for machine learning and data mining. This framework supports the following: (a) data preprocessing, (b) modeling, (c) evaluation, and (d) data mining classification and clustering algorithms. Mitchell [13] has discussed machine learning and data mining. According to him, data mining improves future decisions using historical data and discovers irregularities. The author has discussed scientific issues; basic technologies helpful in learning analytics are as follows: (a) learning from structured and unstructured data, (b) experimentations, (c) explorations, (d) optimizing decisions, and (e) inventing new features to improve accuracy. These learning approaches are helpful in applications such as healthcare, marketing, manufacturing, financial and intelligent data analysis, etc. Baker and Inventado [14] have discussed the relationship between the educational data mining and learning analytics, which are the emerging areas. In [15], Burgos et al. have used data mining techniques for modeling students’ performance. They have proposed the predictive model to prevent the dropout rate in e-learning courses using knowledge discovery techniques. In educational data mining, there is a need to derive and innovate new approaches using statistical techniques, machine learning, psychometrics, and scientific computing to transform the existing system. Naren [16] has discussed data mining applications for predicting behavioral patterns of the students. In [17], the educational data mining approach is used for analyzing and predicting students learning behavior and experiences. Educational data mining helps in designing smarter and intelligent learning, which can better inform learners and educators [18].
Change-Detection Machine Learning Model for Educational Management
Published in Cybernetics and Systems, 2023
Many previous studies have analyzed learning history data by utilizing various data mining techniques to investigate learning behaviors of students. He (2013) proposed an approach to analyze students’ online interaction in a live video streaming system using data mining and text mining techniques. Romero and Ventura (2007) discussed how to apply these techniques in educational systems. Baker and Yacef (2009) surveyed the most cited papers in educational data mining for 10 years and described their contributions. Calders and Pechenizkiy (2012) presented different application areas for data mining in education and mapped educational data mining tasks for traditional data mining problems. Summarily, the techniques applied to educational data mining tasks include clustering, classification, outlier detection, pattern matching, text mining, and mining association rules (Romero and Ventura 2010).
Artificial Neural Networks for Educational Data Mining in Higher Education: A Systematic Literature Review
Published in Applied Artificial Intelligence, 2021
Emmanuel Okewu, Phillip Adewole, Sanjay Misra, Rytis Maskeliunas, Robertas Damasevicius
In a bid to improve higher education and make more responsive to the needs of industry, advances in research have led to a number of pedagogical measures such as face-to-face learning, virtual learning, blended learning, and online learning. However, none of these have taken advantage of the huge learner-related data generated during learning to enhance decision making in the education domain. In a marked departure, educational data mining is a data-based technology-enhanced pedagogical approach that leverages on data science techniques like artificial intelligence (AI), data mining, knowledge discovery in databases (KDD), and data warehouse that harnesses learning-related data for informed decision making in the learning environment. Educational data mining (EDM) is the analysis of huge sets of learner-related (Barneveld, Arnold, and Campbell 2012; Siemens et al. 2011) with the aid of methods like KDD, business intelligence, educational data mining, social network analysis, operational research, machine learning, and information visualization with the aim of informing and shaping the learners’ environments (Ali et al. 2013; Fournier, Kop, and Sitlia 2011).
Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data
Published in Human–Computer Interaction, 2019
Roberto Martinez-Maldonado, Judy Kay, Simon Buckingham Shum, Kalina Yacef
There has also been a growing interest in exploiting users’ data for teaching and learning. Research fields such as Educational Data Mining (Baker & Yacef, 2009), Artificial Intelligence in Education (Roll & Wylie, 2016), and Learning Analytics (Siemens, 2013) focus on exploiting student data to enhance self-regulation (for students); to provide automated feedback and to improve coaching and authentic assessment (for students and teachers); or to support post hoc analysis of collaboration, interventions, and so on (for researchers). However, most of the R&D in these fields has focused on networked collaborative learning environments, and it is only in recent years that educational Data Science has been brought to bear on collocated interaction with interactive surfaces, digital tangibles, and sensors (Martinez-Maldonado et al., 2016).