Resource Usage Analysis from a Different Perspective on MOOC Dropout

We present a novel learning analytics approach, for analyzing the usage of resources in MOOCs. Our target stakeholders are the course designers who aim to evaluate their learning materials. In order to gain insight into the way educational resources are used, we view dropout behaviour in an atypical manner: Instead of using it as an indicator of failure, we use it as a mean to compute other features. For this purpose, we developed a prototype, called RUAF, that can be applied to the data format provided by FutureLearn. As a proof of concept, we perform a study by applying this tool to the interaction data of learners from four MOOCs. We also study the quality of our computations, by comparing them to existing process mining approaches. We present results that highlight patterns showing how learners use resources. We also show examples of practical conclusions a course designer may benefit from.Comment: 30 pages, 40 figure

Repairing Alignments of Process Models

Process mining represents a collection of data driven techniques that support the analysis, understanding and improvement of business processes. A core branch of process mining is conformance checking, i.e., assessing to what extent a business process model conforms to observed business process execution data. Alignments are the de facto standard instrument to compute such conformance statistics. However, computing alignments is a combinatorial problem and hence extremely costly. At the same time, many process models share a similar structure and/or a great deal of behavior. For collections of such models, computing alignments from scratch is inefficient, since large parts of the alignments are likely to be the same. This paper presents a technique that exploits process model similarity and repairs existing alignments by updating those parts that do not fit a given process model. The technique effectively reduces the size of the combinatorial alignment problem, and hence decreases computation time significantly. Moreover, the potential loss of optimality is limited and stays within acceptable bounds

Making sense of actor behaviour: an algebraic filmstrip pattern and its implementation

Sense-making with respect to actor-based systems is challenging because of the non-determinism arising from concurrent behaviour. One strategy is to produce a trace of event histories that can be processed post-execution. Given a semantic domain, the histories can be translated into visual representations of the semantics in the form of filmstrips. This paper proposes a general pattern for the production of filmstrips from actor histories that can be implemented in a way that is independent of the particular data types used to represent the events, semantics and graphical displays. We demonstrate the pattern with respect to a simulation involving predators and prey which is a typical agent-based application

Receipt phase of an environmental permit application process (‘WABO’), CoSeLoG project

This data originates from the CoSeLoG project executed under NWO project number 638.001.211. Within the CoSeLoG project the (dis)similarities between several processes of different municipalities in the Netherlands has been investigated. This event log contains the records of the execution of the receiving phase of the building permit application process in an anonymous municipality

Towards Improving the Representational Bias of Process Mining

International audienceProcess mining techniques are able to extract knowledge from event logs commonly available in today’s information systems. These techniques provide new means to discover, monitor, and improve processes in a variety of application domains. Process discovery—discovering a process model from example behavior recorded in an event log—is one of the most challenging tasks in process mining. A variety of process discovery techniques have been proposed. Most techniques suffer from the problem that often the discovered model is internally inconsistent (i.e., the model has deadlocks, livelocks or other behavioral anomalies). This suggests that the search space should be limited to sound models. In this paper, we propose a tree representation that ensures soundness. We evaluate the impact of the search space reduction by implementing a simple genetic algorithm that discovers such process trees. Although the result can be translated to conventional languages, we ensure the internal consistency of the resulting model while mining, thus reducing the search space and allowing for more efficient algorithms

Mining process performance from event logs

In systems where process executions are not strictly enforced by a predefined process model, obtaining reliable performance information is not trivial. In this paper, we analyzed an event log of a real-life process, taken from a Dutch financial institute, using process mining techniques. In particular, we exploited the alignment technique [2] to gain insights into the control flow and performance of the process execution. We showed that alignments between event logs and discovered process models from process discovery algorithms reveal insights into frequently occurring deviations and how such insights can be exploited to repair the original process models to better reflect reality. Furthermore, we showed that the alignments can be further exploited to obtain performance information. All analysis in this paper is performed using plug-ins within the open-source process mining toolkit ProM

Analysing Structured Learning Behaviour in Massive Open Online Courses (MOOCs): An Approach Based on Process Mining and Clustering

The increasing use of digital systems to support learning leads to a growth in data regarding both learning processes and related contexts. Learning Analytics offers critical insights from these data, through an innovative combination of tools and techniques. In this paper, we explore students’ activities in a MOOC from the perspective of personal constructivism, which we operationalized as a combination of learning behaviour and learning progress. This study considers students’ data analyzed as per the MOOC Process Mining: Data Science in Action. We explore the relation between learning behaviour and learning progress in MOOCs, with the purpose to gain insight into how passing and failing students distribute their activities differently along the course weeks, rather than predict students' grades from their activities. Commonly-studied aggregated counts of activities, specific course item counts, and order of activities were examined with cluster analyses, means analyses, and process mining techniques. We found four meaningful clusters of students, each representing specific behaviour ranging from only starting to fully completing the course. Process mining techniques show that successful students exhibit a more steady learning behaviour. However, this behaviour is much more related to actually watching videos than to the timing of activities. The results offer guidance for teachers

ProDiGy:Human-in-the-loop process discovery

\u3cp\u3eProcess mining is a discipline that combines the two worlds of business process management and data mining. The central component of process mining is a graphical process model that provides an intuitive way of capturing the logical flow of a process. Traditionally, these process models are either modeled by a user relying on domain expertise only; or discovered automatically by relying entirely on event data. In an attempt to address this apparent gap between user-driven and data-driven process discovery, we present ProDiGy, an alternative approach that enables interactive process discovery by allowing the user to actively steer process discovery. ProDiGy provides the user with automatic recommendations to edit a process model, and quantify and visualize the impact of each recommendation. We evaluated ProDiGy (i) objectively by comparing it with automated discovery approaches and (ii) subjectively by performing a user study with healthcare researchers. Our results show that ProDiGy enables inclusion of domain knowledge in process discovery, which leads to an improvement of the results over the traditional process discovery techniques. Furthermore, we found that ProDiGy also increases the comprehensibility of a process model by providing the user with more control over the discovery of the process model.\u3c/p\u3

Uncovering learning patterns in a MOOC through conformance alignments

Web-based learning is now offered in multiple forms. One of these is the phenomenon of Massive Open Online Courses (MOOCs). Several approaches in Learning Analytics (LA) attempt to analyze and explain students learning patterns in MOOCs. In addition to traditional data mining techniques, online surveys constitute another way used in LA for analyzing students’ learning habits in MOOCs. However, such an approach can be error-prone with data collection. Therefore, we adopt the use of process mining techniques. Process mining techniques provide more robust ways of extracting, analyzing and visualizing students’ activities trail. In this paper, we make use of alignment-based conformance checking to extract and analyse students’ learning patterns in MOOCs. The aim is to provide a guideline and demonstrate how process mining can provide critical insights in tems of students’ learning and quiz submissions behavior, their resulting performance and the correlation therein. Keywords: Learning Analytics, Mooc, Coursera, Educational Data Mining, Process Mining,Online Learning, Conformance Checkin