Exploring data mining possibilities on computer based problem solving data

Pejic, Aleksandar; Molcer, Piroska Stanic

doi:10.1109/sisy.2016.7601491

Cited by 8 publications

(5 citation statements)

References 6 publications

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“…Executive function and problem-solving driven by conscious processes are closely related to each other. Computer-based assessment of problem-solving skills and the cognitive processes it involves, is an intensively studied topic [21]- [22].…”

Section: Discussionmentioning

confidence: 99%

Study of Algorithmic Problem-Solving and Executive Function

Kővári¹

2020

ACTA POLYTECH HUNG

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Section: Discussionmentioning

confidence: 99%

Study of Algorithmic Problem-Solving and Executive Function

Kővári¹

2020

ACTA POLYTECH HUNG

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“…Except for the study of Vista et al (2017), which studied data from the ATC21S project, all journal papers in this layer analyzed data from PISA assessments, with four of them (Chen et al, 2019;Han et al, 2019;Pejic & Molcer, 2016;Xu et al, 2018) exploring respondent's actions in the log file from the same item (Climate Control, Figure 3).…”

Section: Item-level Analysis (Layer 1)mentioning

confidence: 99%

“…The key feature of these studies is the exploration of log file data using data mining techniques (e.g., naïve Bayes classifier (Pejic & Molcer, 2016), random forest algorithm (Han et al, 2019;Qiao & Jiao, 2018), exploratory network analysis (Vista et al, 2017)); identification of (latent) groups with differential problem solving strategies via a modified multilevel mixture item response theory (IRT) modeling (Liu et al, 2018), latent class analysis (Xu et al, 2018), cluster analysis (Ren et al, 2019); prediction of duration and final outcome via event history analysis (Chen et al, 2019); or investigating the relationship of task performance and observed variables from log-file data through confirmatory factor analysis (De Boeck & Scalise, 2019).…”

Section: Item-level Analysis (Layer 1)mentioning

confidence: 99%

Process Data Analysis in ILSAs

Costa

Netto²

2022

International Handbook of Comparative Large-Scale Studies in Education

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Computational advancements in the last couple of decades have brought forth a new era of international large-scale assessments (ILSAs) in which the administration of computer-based tests is becoming the norm. Beyond collecting the correct/incorrect answers for each item, computerbased assessments are also able to collect a range of actions performed by the respondents in the computer testing application during the course of the test administration. Both respondents' actions-starting a unit, clicking a button, spending time until inputting or submitting an answer, and so forth-and their overall behavior-what they do with their keyboards, mice, and even their own eyes-are recorded as a new set of data called "process data", which are normally timestamped and can be stored in so-called log files. There may be plenty of useful insight into the respondent's cognitive process, and process data can potentially become a relevant element in the scoring process of an assessment, validate test score interpretations, to name a few possibilities for the analysis of such data. This chapter aims to contribute to the body of knowledge in this area by offering (1) an introduction to process data, what kind of data it contains, its relation to the cognitive process and how it can be organized into a proposed 6-layered ecological framework that facilitates its analysis; (2) a literature review of 37 seminal and state-of-the-art publications produced in the last decade on the topic, which are then analyzed both in their chronological perspective as well as in how they fit into the ecological framework; and (3) a discussion of the potential and limitations of using process data in the assessment framework, including a view of what could be the next steps in the analysis of process data from ILSAs.

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“…The output obtained from the mapper are then combined in the combiner and then sent to the reducer. The authors in [84] developed educational models to predict how learning materials might be designed to fit the knowledge of the student. Their approach used educational data mining to develop educational models to predict how learning materials might be designed to fit the knowledge of the student.…”

Section: ) Courses Selectionmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario. INDEX TERMS Big data, learning technologies, educational data, learning analytics.

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Exploring data mining possibilities on computer based problem solving data

Cited by 8 publications

References 6 publications

Study of Algorithmic Problem-Solving and Executive Function

Study of Algorithmic Problem-Solving and Executive Function

Process Data Analysis in ILSAs

Big Educational Data & Analytics: Survey, Architecture and Challenges

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