Behavioral constraint template-based sequence classification

In this paper we present the interesting Behavioral Constraint Miner (iBCM), a new approach towards classifying sequences. The prevalence of sequential data, i.e., a collection of ordered items such as text, website navigation patterns, traffic management, and so on, has incited a surge in research interest towards sequence classification. Existing approaches mainly focus on retrieving sequences of itemsets and checking their presence in labeled data streams to obtain a classifier. The proposed iBCM approach, rather than focusing on plain sequences, is template-based and draws its inspiration from behavioral patterns used for software verification. These patterns have a broad range of characteristics and go beyond the typical sequence mining representation, allowing for a more precise and concise way of capturing sequential information in a database. Furthermore, it is possible to also mine for negative information, i.e., sequences that do not occur. The technique is benchmarked against other state-of-the-art approaches and exhibits a strong potential towards sequence classification. Code related to this chapter is available at: http://feb.kuleuven.be/public/u0092789/status: publishe

Comparing Entropy and Beta as Measures of Risk in Asset Pricing

The paper establishes entropy as a measure of risk in asset pricing models by comparing its explanatory power with that of classic capital asset pricing model’s beta to describe the diversity in expected risk premiums. Three different non‑parametric estimation procedures are considered to evaluate financial entropy, namely kernel density estimated Shannon entropy, kernel density estimated Rényi entropy and maximum likelihood Miller‑Madow estimated Shannon entropy. The comparison is provided based on the European stock market data, for which the basic risk‑return trade‑off is generally negative. Kernel density estimated Shannon entropy provides the most efficient results not dependent on the choice of the market benchmark and without imposing any prior model restrictions

Understanding automated feedback in learning processes by mining local patterns

status: Published onlin

Discovering the Impact of Students’ Modeling Behavior on their Final Performance

Part 7: Teaching ChallengesInternational audienceConceptual modeling is an important part of Enterprise Modeling, which is a challenging field for both teachers and learners. Creating conceptual models is a so-called ‘ill-structured’ task, i.e. multiple good solutions are possible, and thus students can follow very distinct modeling processes to achieve successful learning outcomes. Nevertheless, it is possible that some principles of modeling behavior are more typical for high-performing rather than low-performing students, and vice versa. In this study, we aimed to discover those patterns by analyzing logged student modeling behavior with process mining, a set of tools for dealing with event-based data. We analyzed data from two individual conceptual modeling assignments in the JMermaid modeling environment based on the MERODE method. The study identified the presence of behavioral patterns in the modeling process that are indicative for better/worse learning outcomes, and showed what these patterns are. Another important finding is that students’ performance in intermediate assignments is as well indicative of their performance in the whole course. Thus, predicting these problems as early as possible can help teachers to support students and change their final outcomes to better ones

Mining Behavioral Sequence Constraints for Classification

status: Published onlin

Mining Behavioral Sequence Constraints for Classification

status: accepte

Introduction to the Minitrack on Learning Analytics at HICCS 2022

CC BY-NC-ND 4.0</p

The Past, Present and Future of Learning Analytics : Minitrack paper

In the user’s interaction with systems, waiting and interruptions often constitute a source of negative experiences. However, system response time can be difficult or impossible to control, due to for example poor internet connection. This study explores “subjective experienced time”, which refers to the users’ assessment of system response timeliness. The aim of this study is to gain increased knowledge of user satisfaction and subjectively experienced time in interaction with mobile applications. Thirty participants used and evaluated three mobile applications, containing unique stimuli in progress indicators. The results show correlation between progress indicators’ degree of feedback and the subjectively experienced time and user satisfaction. Contributions include increased insight into the somewhat complex connection between the degree of feedback, subjectively experienced time and user satisfaction, as well as design implications for user-centred design