Students’ engagements reflect their level of involvement in an ongoing learning process which can be estimated through their interactions with a computer-based learning or assessment system. A ...pre-requirement for stimulating student engagement lies in the capability to have an approximate representation model for comprehending students’ varied (dis)engagement behaviors. In this paper, we utilized model-based clustering for this purpose which generates
K
mixture Markov models to group students’ traces containing their (dis)engagement behavioral patterns. To prevent the Expectation–Maximization (EM) algorithm from getting stuck in a
local maxima
, we also introduced a K-means-based initialization method named as K-EM. We performed an experimental work on two real datasets using the three variants of the EM algorithm: the original EM, emEM, K-EM; and, non-mixture baseline models for both datasets. The proposed K-EM has shown very promising results and achieved significant performance difference in comparison with the other approaches particularly using the Dataset1. Hence, we suggest to perform further experiments using large dataset(s) to validate our method. Additionally, visualization of the resultant clusters through first-order Markov chains reveals very useful insights about (dis)engagement behaviors depicted by the students. We conclude the paper with a discussion on the usefulness of our approach, limitations and potential extensions of this work.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The heterogeneous data acquired by educational institutes about students’ careers (e.g., performance scores, course preferences, attendance record, demographics, etc.) has been a source of ...investigation for Educational Data Mining (EDM) researchers for over two decades. EDM researchers have primarily focused on course-specific data analyses of students’ performances, and rare attempts are made at the domain level that may benefit the educational institutes at large to gauge and improve their institutional effectiveness. Our work aims to fill this gap by examining students’ transcripts data for identifying similar groups of students and patterns that might associate with these different cohorts of students based on: (a) difficulty level of a course category, (b) formation of course trajectories, and, (c) transitioning of students between different performance groups. We have exploited descriptive data mining and visualization methods to analyze transcript data of 1398 undergraduate Computer Science students of a private university in Pakistan. The dataset includes students’ transcript data of 124 courses from nine distinct course categories. In the end, we have discussed our findings in detail, challenges, and, future work directions.
Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction. Often, ...the design of such models is based on some ad-hoc assumptions about the corresponding data distributions, which are not necessarily in accordance with the non-parametric distributions typically observed with process data. Moreover, mainstream machine-learning approaches tend to ignore the challenges posed by concurrency in operational processes. Data encoding is a key element to smooth the mismatch between these assumptions but its potential is poorly exploited. In this paper, we argue that a deeper understanding of the challenges associated with training machine learning models on process data is essential for establishing a robust integration of process mining and machine learning. Our analysis aims to lay the groundwork for a methodology that aligns machine learning with process mining requirements. We encourage further research in this direction to advance the field and effectively address these critical issues.
Business processes have undergone a significant transformation with the advent of the process-oriented view in organizations. The increasing complexity of business processes and the abundance of ...event data have driven the development and widespread adoption of process mining techniques. However, the size and noise of event logs pose challenges that require careful analysis. The inclusion of different sets of behaviors within the same business process further complicates data representation, highlighting the continued need for innovative solutions in the evolving field of process mining. Trace clustering is emerging as a solution to improve the interpretation of underlying business processes. Trace clustering offers benefits such as mitigating the impact of outliers, providing valuable insights, reducing data dimensionality, and serving as a preprocessing step in robust pipelines. However, designing an appropriate clustering pipeline can be challenging for non-experts due to the complexity of the process and the number of steps involved. For experts, it can be time-consuming and costly, requiring careful consideration of trade-offs. To address the challenge of pipeline creation, the paper proposes a genetic programming solution for trace clustering pipeline synthesis that optimizes a multi-objective function matching clustering and process quality metrics. The solution is applied to real event logs, and the results demonstrate improved performance in downstream tasks through the identification of sub-logs.
We present a way of building ontologies that proceeds in a bottom-up fashion, defining concepts as clusters of concrete XML objects. Our rough bottom-up ontologies are based on simple relations like ...association and inheritance, as well as on value restrictions, and can be used to enrich and update existing upper ontologies. Then, we show how automatically generated assertions based on our bottom-up ontologies can be associated with a flexible degree of trust by nonintrusively collecting user feedback in the form of implicit and explicit votes. Dynamic trust-based views on assertions automatically filter out imprecisions and substantially improve metadata quality in the long run
Over recent years, interest in the development of smart health technologies aimed at supporting independent living for older populations has increased. The integration of innovative technologies, ...such as the Internet of Things, wearable technologies, artificial intelligence, and ambient-assisted living applications, represents a valuable solution for this scope. Designing such an integrated system requires addressing several aspects (eg, equipment selection, data management, analytics, costs, and users' needs) and involving different areas of expertise (eg, medical science, service design, biomedical and computer engineering).
The objective of this study is 2-fold; we aimed to design the functionalities of a smart health platform addressing 5 chronic conditions prevalent in the older population (ie, hearing loss, cardiovascular diseases, cognitive impairments, mental health problems, and balance disorders) by considering both older adults' and clinicians' perspectives and to evaluate the identified smart health platform functionalities with a small group of older adults.
Overall, 24 older adults (aged >65 years) and 118 clinicians were interviewed through focus group activities and web-based questionnaires to elicit the smart health platform requirements. Considering the elicited requirements, the main functionalities of smart health platform were designed. Then, a focus group involving 6 older adults was conducted to evaluate the proposed solution in terms of usefulness, credibility, desirability, and learnability.
Eight main functionalities were identified and assessed-cognitive training and hearing training (usefulness: 6/6, 100%; credibility: 6/6, 100%; desirability: 6/6, 100%; learnability: 6/6, 100%), monitoring of physiological parameters (usefulness: 6/6, 100%; credibility: 6/6, 100%; desirability: 6/6, 100%; learnability: 5/6, 83%), physical training (usefulness: 6/6, 100%; credibility: 6/6, 100%; desirability: 5/6, 83%; learnability: 2/6, 33%), psychoeducational intervention (usefulness: 6/6, 100%; credibility: 6/6, 100%; desirability: 4/6, 67%; learnability: 2/6, 33%), mood monitoring (usefulness: 4/6, 67%; credibility: 4/6, 67%; desirability: 3/6, 50%; learnability: 5/6, 50%), diet plan (usefulness: 5/6, 83%; credibility: 4/6, 67%; desirability: 1/6, 17%; learnability: 2/6, 33%), and environment monitoring and adjustment (usefulness: 1/6, 17%; credibility: 1/6, 17%; desirability: 0/6, 0%; learnability: 0/6, 0%). Most of them were highly appreciated by older participants, with the only exception being environment monitoring and adjustment. The results showed that the proposed functionalities met the needs and expectations of users (eg, improved self-management of patients' disease and enhanced patient safety). However, some aspects need to be addressed (eg, technical and privacy issues).
The presented smart health platform functionalities seem to be able to meet older adults' needs and desires to enhance their self-awareness and self-management of their medical condition, encourage healthy and independent living, and provide evidence-based support for clinicians' decision-making. Further research with a larger and more heterogeneous pool of stakeholders in terms of demographics and clinical conditions is needed to assess system acceptability and overall user experience in free-living conditions.
In the context of online banking, new users have to register their information to become clients through mobile applications; this process is called digital onboarding. Fraudsters often commit ...identity fraud by impersonating other people to obtain access to banking services by using personal data obtained illegally and causing damage to the organisation’s reputation and resources. Detecting fraudulent users by their onboarding process is not a trivial task, as it is difficult to identify possible vulnerabilities in the process to be exploited. Furthermore, the modus operandi for differentiating the behaviour of fraudulent actors and legitimate users is unclear. In this work, we propose the usage of a process mining (PM) approach to detect identity fraud in digital onboarding using a real fintech event log. The proposed PM approach is capable of modelling the behaviour of users as they go through a digital onboarding process, while also providing insight into the process itself. The results of PM techniques and the machine learning classifiers showed a promising 80% accuracy rate in classifying users as fraudulent or legitimate. Furthermore, the application of process discovery in the event log dataset produced an insightful visual model of the onboarding process.
This paper proposes a methodology for sensor data interpretation that can combine sensor outputs with contexts represented as sets of annotated business rules. Sensor readings are interpreted to ...generate events labeled with the appropriate type and level of uncertainty. Then, the appropriate context is selected. Reconciliation of different uncertainty types is achieved by a simple technique that moves uncertainty from events to business rules by generating combs of standard Boolean predicates. Finally, context rules are evaluated together with the events to take a decision. The feasibility of our idea is demonstrated via a case study where a context-reasoning engine has been connected to simulated heartbeat sensors using prerecorded experimental data. We use sensor outputs to identify the proper context of operation of a system and trigger decision-making based on context information.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can ...have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Network (OSN). Domain knowledge is used to define tailored strategies that can decrease the budget and time required for mining while increasing the recall. An ontology supports our filtering layer in evaluating the relatedness of nodes. Our approach demonstrates that the same mechanism can be advanced to prompt recommendations to users. Our test cases and experimental results emphasize the importance of the strategy definition step in our social miner and the application of ontologies on the knowledge graph in the domain of recommendation analysis.
•Framework for sampling social network.•Tailored strategies to decrease the time and the cost of mining.•Scalable social network filters using Event Subscribers.•A graph-based recommender system using an improved Adamic Adar algorithm.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This paper surveys the current state-of-the-art of technology, systems and applications in Augmented Reality. It describes work performed by many different research groups, the purpose behind each ...new Augmented Reality system, and the difficulties and problems encountered when building some Augmented Reality applications. It surveys mobile augmented reality systems challenges and requirements for successful mobile systems. This paper summarizes the current applications of Augmented Reality and speculates on future applications and where current research will lead Augmented Reality’s development. Challenges augmented reality is facing in each of these applications to go from the laboratories to the industry, as well as the future challenges we can forecast are also discussed in this paper. Section
1
gives an introduction to what Augmented Reality is and the motivations for developing this technology. Section
2
discusses Augmented Reality Technologies with computer vision methods, AR devices, interfaces and systems, and visualization tools. The mobile and wireless systems for Augmented Reality are discussed in Section
3
. Four classes of current applications that have been explored are described in Section
4
. These applications were chosen as they are the most famous type of applications encountered when researching AR apps. The future of augmented reality and the challenges they will be facing are discussed in Section
5
.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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