From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining

Trčka, N.; Pechenizkiy, Mykola

doi:10.1109/isda.2009.159

Cited by 38 publications

(38 citation statements)

References 10 publications

(10 reference statements)

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“…On the other hands, the application of EDM to analyze the program's curriculum data has been receiving increasing attention. The seminal work of utilizing process mining on curriculum data is proposed in [38]. In [28], a notion called curriculum mining is brought up and the curriculum data is directly targeted.…”

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confidence: 99%

A data‐mining‐based approach to informed decision‐making in engineering education

Yahya

Osman

2019

Comp Applic In Engineering

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In outcome‐based academic programs, Program Educational Objectives (PEOs) and Student Outcomes (SOs) are two cores around which all programs’ components and processes revolve. Needless to say, the PEOs‐SOs mapping is very critical for program success and, therefore, a deep understanding of PEOs, SOs, and their intra/inter‐correlations is very important for effective program's decisions making. In this context, this paper proposes a data mining‐based approach to discover hidden knowledge of PEOs, SOs, and their mapping and correlations in engineering programs. More specifically, the proposed approach employs association rule mining techniques to generate association rules, among and between PEOs and SOs, from PEOs‐SOs mapping data of a set of engineering programs, which can be then filtered and manipulated to discover the desired knowledge. To this end, a set of 152 self‐study reports of engineering programs, accredited by American Board for Engineering and Technology‐Engineering Accreditation Commission (ABET‐EAC), are collected and the mapping data between their PEOs and ABET‐EAC SOs are extracted. The dataset is processed and transformed into a representation suitable for association rules mining. This involves developing a set of PEOs labels, annotating data with PEOs labels, and extracting three target datasets. Apriori algorithm is then applied to each dataset to generate three sets of association rules. The generated association rules are then filtered and manipulated to discover the knowledge of PEOs, ABET‐EAC SOs, and their mapping and correlations. Finally, a discussion on the informativeness of the discovered knowledge for the decisions making in engineering education is given.

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confidence: 99%

A data‐mining‐based approach to informed decision‐making in engineering education

Yahya

Osman

2019

Comp Applic In Engineering

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“…Most work in PM has concentrated on (business) workflow systems and the discovery of Petri nets representations of workflows. 7 The methods described by these labels take information from event logs as input and produce process models that describe the information in the event logs in a comprehensive manner. 8 The process-oriented view helps to exceed the mainly isolated view of datasets that dominates traditional DM.…”

Section: Background and Related Areasmentioning

confidence: 99%

“…Taking this into consideration, EPM is able to build complete, compact educational process models that are able to reproduce all the observed behaviors, check to see if the modeling behavior matches the behavior observed, and project extracted information from the registrations in the pattern to make the tacit knowledge explicit, and to facilitate a better understanding of the process. 7 Likewise, the term Learnflow Mining in correspondence with Workflow Mining has been used by some authors 9,10 whereas many more authors 1,[11][12][13] prefer the term EPM in correspondence with PM, which is the currently the most commonly used term.…”

Section: Background and Related Areasmentioning

confidence: 99%

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A survey on educational process mining

Bogarín

Cerezo

Romero

2017

WIREs Data Min & Knowl

159

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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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“…The solution is to 'divide' the process into smaller groups of tasks/steps and at each group, build a model accordingly [3], [4]. The shortcoming of these local models is that the coherence and the interaction between events from different event logs are lost.…”

Section: Introductionmentioning

confidence: 99%

Sequential Clustering for Event Sequences and Its Impact on Next Process Step Prediction

Nauck

Gabrys

et al. 2014

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Abstract. Next step prediction is an important problem in process analytics and it can be used in process monitoring to preempt failure in business processes. We are using logfiles from a workflow system that record the sequential execution of business processes. Each process execution results in a timestamped event. The main issue of analysing such event sequences is that they can be very diverse. Models that can effectively handle diverse sequences without losing the sequential nature of the data are desired. We propose an approach which clusters event sequences. Each cluster consists of similar sequences and the challenge is to identify a similarity measure that can cope with the sequential nature of the data. After clustering we build individual predictive models for each group. This strategy addresses both the sequential and diverse characteristics of our data. We first employ K-means and extent it into a categorical-sequential clustering algorithm by combining it with sequential alignment. Finally, we treat each resulting cluster by building individual Markov models of different orders, expecting that the representative characteristics of each cluster are captured.

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From Local Patterns to Global Models: Towards Domain Driven Educational Process Mining

Cited by 38 publications

References 10 publications

A data‐mining‐based approach to informed decision‐making in engineering education

A data‐mining‐based approach to informed decision‐making in engineering education

A survey on educational process mining

Sequential Clustering for Event Sequences and Its Impact on Next Process Step Prediction

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