An intelligent fuzzy-based emergency alert generation for persons with episodic memory decline problems

Many humans deal with problems that concern episodic memory decline. These problems can cause annoying, and sometimes, dangerous incidents, like failing to recall the name of a friend or forgetting to take a medicine or turn off the cooker. Concerning the above, this paper presents a fuzzy rule-based mechanism that generates emergency alerts when a dangerous situation is caused by an inconsistency in the human's actions in the home environment. In such a way, the system protects persons with episodic memory decline problems or lapses of attention from dangerous situations that may be caused by their memory disorder and allows them to complete an everyday activity. For the paper's needs the application of the presented mechanism is limited to the usage of a cooker. The system takes as input the time, the size and the content of the pot that is used to the cooker, calculates the degree of emergency and describes it using fuzzy sets and, finally, applies rules over the fuzzy sets to generate alert messages that notify the monitored person about the next action that s/he has to do in order to complete a particular activity. For the evaluation of the system, we have developed a simulation program that asks users to complete some activities during a specific time period. The system embeds the presented fuzzy rule-based mechanism and monitors the user's actions and generated alerts, which concern the usage of the stove burner. The simulation software was used by 15 users. Their reactions and opinions about the system's alerts and the assistance it offers, are positive. © 2021 - IOS Press. All rights reserved

Learning Feedback Based on Dispositional Learning Analytics

The combination of trace data captured from technology-enhanced learning support systems, formative assessment data and learning disposition data based on self-report surveys, offers a very rich context for learning analytics applications. In previous research, we have demonstrated how such Dispositional Learning Analytics applications not only have great potential regarding predictive power, e.g. with the aim to promptly signal students at risk, but also provide both students and teacher with actionable feedback. The ability to link predictions, such as a risk for drop-out, with characterizations of learning dispositions, such as profiles of learning strategies, implies that the provision of learning feedback is not the end point, but can be extended to the design of learning interventions that address suboptimal learning dispositions. Building upon the case studies we developed in our previous research, we replicated the Dispositional Learning Analytics analyses in the most recent 17/18 cohort of students based on the learning processes of 1017 first-year students in a blended introductory quantitative course. We conclude that the outcomes of these analyses, such as boredom being an important learning emotion, planning and task management being crucial skills in the efficient use of digital learning tools, help both predict learning performance and design effective interventions

Fuzzy student modeling for personalization of e-learning courses

In the context of e-learning courses, personalization is a more and more studied issue, being its advantage in terms of time and motivations widely proved. Course personalization basically means to understand student's needs: to this aim several Artificial Intelligence methodologies have been used to model students for tailoring e-learning courses and to provide didactic strategies, such as planning, case based reasoning, or fuzzy logic, just to cite some of them. Moreover, in order to disseminate personalised e-learning courses, the use of known and available Learning Management System is mandatory. In this paper we propose a fine-grained student model, embedded into an Adaptive Educational Hypermedia, LS-Plan provided as plug-in for Moodle. In this way we satisfy the two most important requirements: a fine-grained personalization and a large diffusion. In particular, the substantial modification proposed in this contribution regards the methodology to evaluate the knowledge of the single student which currently has a low granularity level. The experiments showed that the new system has improved the evaluation mechanism by adding information that students and teachers can use to keep track of learning progress

A Pilot Study on Estimating Players Dispositional Profiles from Game Traces Analysis

International audienc

Design of an Algebraic Concept Operator for Adaptive Feedback in Physics

International audienceIn an adaptive learning environment, the feedback provided during problem-solving requires a means, target, goal, and strategy. One of the challenges of representing feedback to meet these criteria, is the representation of the effect of multiple concepts on a single concept. Currently, most of the methods (linguistic knowledge base, expert knowledge base, and ontology) used in representing knowledge in an adaptive learning environment only provide relationships between a pair of concept. However, a cognitive knowledge base which represents a concept as an object, attribute, and relations (OAR) model, provides a means to determine the effect of multiple concepts on a single concept. Using the OAR model, the relationships between multiple pedagogical, domain, and student attributes are represented for providing adaptive feedback. Most researchers have proposed adaptive feedback methods that are not fully grounded in pedagogical principles. In addition, the three knowledge components of the learning environment (pedagogical, domain and student models) are mostly treated in isolation. A reason for this could be the complex nature of representing multiple adaptive feedback characteristics across the main components of a learning environment. Thus, there is a need to design a concept operator that can relate the three facets of knowledge in an adaptive learning environment. Using the algebraic concept operator $$R_{i}^{in}$$, the effect of multiple attributes of the three knowledge components on the student’s performance is represented. The algebraic concept operator introduced in this article will allow teachers and pedagogy experts to understand and utilize a variety of effective feedback approaches