A Learning Analytics Approach to Correlate the Academic Achievements of Students with Interaction Data from an Educational Simulator

Vahdat, Mehrnoosh; Oneto, Luca; Anguita, Davide; Funk, Mathias; Rauterberg, Matthias

doi:10.1007/978-3-319-24258-3_26

Cited by 51 publications

(49 citation statements)

References 29 publications

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“…Cairns et al 31 LTL-Conformance checker X Cairns et al 13 Fuzzy Miner LTL-Conformance checker X X Mukala et al 34 Fuzzy Miner Conformance checker X Vahdat et al 42 Fuzzy Miner…”

Section: Professional Trainingmentioning

confidence: 99%

“…Professional training Cairns et al 32 To analyze social networks Cairns et al 31 To discover learning flows Doleck et al 2016 41 To gain a better understanding of the underlying educational process and detect learning difficulties Vahdat et al 42 Ariouat et al, 2016 43 To gain a better understanding of the underlying educational process Curriculum mining Trcka and Pechenizkiy 7 To gain a better understanding of the underlying educational process…”

Section: Professional Trainingmentioning

confidence: 99%

“…Howard et al 51 To detect learning difficulties Jeong et al 52 3D educational virtual worlds Fernández-Gallego et al 53 To discover learning flows picture about clinical diagnosis reasoning. Vahdat et al 42 carried out their study in a simulation environment for learning digital electronics. They exploited PM methods to investigate and compare the learning processes of professionals.…”

Section: Structured Inquiry Cyclementioning

confidence: 99%

“…Southavilay et al 39 To gain a better understanding of the underlying educational process and detect learning difficulties To gain a better understanding of the underlying educational process and detect learning difficulties Vahdat et al 42 Ariouat et al, 2016 43 To gain a better understanding of the underlying educational process…”

mentioning

confidence: 99%

“…Vahdat et al 42 carried out their study in a simulation environment for learning digital electronics. They exploited PM methods to investigate and compare the learning processes of professionals.…”

mentioning

confidence: 99%

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A survey on educational process mining

Bogarín

Cerezo

Romero

2017

WIREs Data Min & Knowl

162

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Educational process mining (EPM) is an emerging field in educational data mining (EDM) aiming to make unexpressed knowledge explicit and to facilitate better understanding of the educational process. EPM uses log data gathered specifically from educational environments in order to discover, analyze, and provide a visual representation of the complete educational process. This paper introduces EPM and elaborates on some of the potential of this technology in the educational domain. It also describes some other relevant, related areas such as intentional mining, sequential pattern mining and graph mining. It highlights the components of an EPM framework and it describes the different challenges when handling event logs and other generic issues. It describes the data, tools, techniques and models used in EPM. In addition, the main work in this area is described and grouped by educational application domains.

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“…Cairns et al 31 LTL-Conformance checker X Cairns et al 13 Fuzzy Miner LTL-Conformance checker X X Mukala et al 34 Fuzzy Miner Conformance checker X Vahdat et al 42 Fuzzy Miner…”

Section: Professional Trainingmentioning

confidence: 99%

Section: Professional Trainingmentioning

confidence: 99%

Section: Structured Inquiry Cyclementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A survey on educational process mining

Bogarín

Cerezo

Romero

2017

WIREs Data Min & Knowl

162

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Application of learning analytics for sequential patterns detection associated with moments of distraction in students in e‐learning platforms

Rodríguez,

Riaño,

García

et al. 2023

Comp Applic In Engineering

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Owing to the positive impact of technology on education, in recent years, e‐learning platforms have increased in popularity and implementation. However, different authors agree that, despite the benefits of using these platforms, one of the main problems is the distraction during the interaction between the student and the E‐Learning platform, since the students visit web resources as social networks, which is not related to their learning process. to identify patterns associated with moments of distraction that students can present on these platforms. Following the above, this study aims to apply Learning Analytics using the Density‐based spatial clustering of applications with noise (DBSCAN) clustering technique and the PrefixSpan algorithm, to identify frequent sequential patterns of interaction, before a distraction event of the student from their interaction with a virtual learning platform. From this, clustering with a Silhouette coefficient close to 0.75 is obtained, presenting good separation, high cohesion, and compaction in the generated groups. Additionally, a slight characterization of each group is achieved based on the most frequent sequential patterns of interaction. Finally, in this study, a successful integration between DBSCAN and PrefixSpan is achieved, to identify sequential interaction patterns associated with moments of distraction in E‐Learning platforms, which generates a starting point for later studies to make the sequential recommendation of educational resources from these patterns.

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Targeted projection pursuit similarity based attribute selection for academic performance prediction

Ramanathan

Balasubramanian

2022

Concurrency and Computation

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The performance of student in the academic field reveals the consideration over researchers to enhance student's weakness. With the consumption of high potential factors from the dataset, accurate student performance prediction is carried out. Targeted projection pursuit similarity based attribute selection (TPPS-AS) technique is designed to improve the student academic performance prediction. TPPS is a machine learning technique that observes the given input. The relevant attributes from the multidimensional space is determined by TPPS. Several research works were recognized recently to conclude the high potential factors for observing student academic performances. A novel technique is designed in this research work to improve the student academic performance prediction in a taken dataset by choosing more relevant attributes. To detect the student academic performance with better accuracy and lesser time, proposed TPPS-AS technique is employed. The performance of student academic performance prediction is improved by TPPS-AS technique through the attribute selection with higher accuracy. With this proposed technique, prediction accuracy of the student academic performance is increased after the relevant attributes selection process.

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A Learning Analytics Approach to Correlate the Academic Achievements of Students with Interaction Data from an Educational Simulator

Cited by 51 publications

References 29 publications

A survey on educational process mining

A survey on educational process mining

Application of learning analytics for sequential patterns detection associated with moments of distraction in students in e‐learning platforms

Targeted projection pursuit similarity based attribute selection for academic performance prediction

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