Real life, real users and real needs: a study and analysis of user queries on the web

Evaluating student learning effect plays an essential role in education, which is typically done by assessing student’s final deliverables. However, the student’s learning process has not been properly explored in the past.In this paper, we propose an interactive student learning effect evaluation framework which focuses on in-process learning effect evaluation. In particular, our proposal analyzes students modeling assignment based on their operation records by using techniques of frequent sequential pattern mining, user behavior analysis, feature engineering, and process mining. A comprehensive online modeling platform has been developed to enable data collection. We have carried out a case study, in which we applied our approach to a real teaching scenario, consisting of student online modeling behavior data collected from 24 students majoring in computer science. We also associate our process mining results with the numeric evaluation values. The preliminary result of case analysis has shown good potential to mine student modeling patterns and interpret their behaviors, contributing to student learning effect evaluation.

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Recognizing patterns of student’s modeling behaviour patterns via process mining

This full paper of the research-to-practice category addresses the problem of organizing instructional materials and assessment activities in e-learning courses and their effects on learning outcomes. Usually, the teacher organizes the course sequence according to his didactic-pedagogical strategies and expects this help to guide the student through his learning process in the course. However, unless restrictions are imposed, students may choose to follow different paths than those indicated in the material’s organization. A question emerges from this context: what are the impacts on the students learning outcomes when they take learning paths other than expected by the teacher? In Virtual Learning Environments, student’s interaction with course materials can be stored in the so-called event logs. With the support of Educational Process Mining, it is possible to track the path of how and what specific actions students perform during learning, resulting in process models and historical statistical information. This paper aims to present the application results of PM techniques to verify the students learning paths in an introductory programming course. We used a Moodle event log containing 24605 events collected from 73 undergraduate students. For experiments, we divided this original log file into five other segments of datasets among passed and failed students variations. Techniques to obtain statistical information, Heuristic Miner algorithm to process discovery, and other techniques were applied from the implementations available in ProM Framework and scripts based on PM4Py library. The results showed that overall approved and failed students took different paths and event numbers to perform activities in the course. Besides, we obtained control-flows and frequencies of the activities and connections, thus making it possible to identify the dependencies, which resources started or ended the process, among other things. The analysis of these results provides general and specific information on students’ learning paths and can help teachers observe students’ behavior patterns and progress.

Educational Process Mining for Verifying Student Learning Paths in an Introductory Programming Course

Bridging the gap between academia and industry is an important issue to promote the practicality of i* framework Researchers have been dealing with this issue from various perspectives, such as simplifying the meta-models or modeling processes of i* framework In this paper, we exclusively focus on the scalability issue in laying out large-scale i* models and propose a two-level layout approach to automatically lay out i* models in an efficient and comprehensible manner, contributing to the adoption of i* framework in the industry The proposed approach is designed by considering the semantics of i* constructs and layout conventions of i* models in order to produce meaningful layouts and can appropriately handle both the SD (Strategic Dependency) view and the SR (Strategic Rationale) view of i* models We have implemented our approach in an open-access prototype tool, which is able to be integrated with existing iStarML-compatible modeling tools We have conducted a controlled experiment, a case study, and performance testing to empirically and comprehensively evaluate the utility of our approach, the results of which show that our proposal can efficiently produce meaningful layouts that are as comprehensible as manually laid out models in most cases

Toward practical adoption of i* framework: an automatic two-level layout approach

iStar framework is an effective means for modeling and analyzing goals and interactions among social agents. Most of existing iStar modeling tools build iStar models via a graphical manner, which has a steep learning curve and suffer from scalability issues. Especially when dealing with large-scale models in industrial settings, it is very time consuming to play with the layout of models. In this paper, we present a tool which allows users to build iStar models from textual descriptions of systems. And the established models are then visualized with reasonable layouts. In particular, our tool supports both SD (Strategic Dependency) view and SR (Strategic Rationale) view of iStar models.

T-Star: A Text-Based iStar Modeling Tool

As education is taking an increasingly significant role in society today, efficient and precise evaluation of student learning effect is calling for more attention. With recent advances of information technology, learning effect can now be evaluated via mining student’s learning process. This paper proposes an interactive student learning effect evaluation framework which focuses on in-process learning effect evaluation. In particular, our proposal analyzes students modeling assignment based on their operation records by using techniques of frequent sequential pattern mining, user behavior analysis, and feature engineering. In order to enable effective student learning evaluation and deliver practical value, we have developed a comprehensive online modeling platform to collect operation data of modelers and to support the corresponding analysis. We have carried out a case study, in which we applied our approach to a real dataset, consisting of student online modeling behavior data collected from 24 students majoring in computer science. The results of our analysis show that our approach can effectively and practically mine student modeling patterns and interpret their behaviors, contributing to assessment of their learning effect.

Evaluating Student Learning Effect Based on Process Mining

Being able to model and analyze social dependencies is one of the most important feature of iStar modeling language. However, typical layout algorithms (e.g., hierarchical layout and circular layout) are not suitable for laying out iStar models, especially, strategic dependency models. As a result, iStar models are typically constructed and laid out in a manual way, which is time-consuming and tedious, especially when dealing with large-scale models. In this paper, we present a prototype tool which can automatically lay out iStar model in a meaningful way by taking into account the semantics of iStar model elements. In particular, we have developed and integrated an istarml parser with our tool, enabling automatic layout of iStar models that are specified in terms of istarml. We believe our tool would be particularly interesting for people who used to deal with large-scale iStar models.

A Prototype for Generating Meaningful Layout of iStar Models

Precise and timely detection of anomalous modeling behaviors is critical for both teaching and application of modeling methods. Existing methods usually focus on evaluating the modeling results rather than mining the knowledge hidden in the modeling process. In this paper, we propose to monitor and analyze the modeling process in order to timely detect anomalous modeling behaviors, potentially contributing to a comprehensive assessment of the modeling practice. Specifically, we propose to systematically build a goal model for characterizing the normal modeling behaviors, which establishes the connections between modelers' high-level modeling behaviors and low-level modeling operations. On top of such a goal model, we propose a data mining-based approach to semi-automatically validate the design of the goal model and explore other normal modeling behaviors. Then, we propose to automatically detect anomalous modeling behaviors by capturing normal modeling behaviors obtained from the goal model and actual modeling sequences. We have developed and deployed a data-flow diagram modeling platform, which implemented our proposed approach. We have conducted an experiment with 57 participants, the preliminary results of which show that our approach can effectively detect modelers' anomalous behaviors. The experiment results are beneficial for not only assessing the modelers' performance but also identifying the usability issues of the modeling tool. 

Detection of anomalous modeling behavior: a goal-driven data mining approach

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Papers

Recognizing patterns of student’s modeling behaviour patterns via process mining

Evaluating Student Learning Effect Based on Process Mining

A Prototype for Generating Meaningful Layout of iStar Models

Detection of anomalous modeling behavior: a goal-driven data mining approach