Study regarding the influence of a student’s personality and an LMS usage profile on learning performance using machine learning techniques

Academic performance (AP) is crucial for lifelong success. Unfortunately, many students fail to meet expected academic benchmarks, leading to altered career paths or university dropouts. This issue is particularly pronounced in the early stages of higher education, highlighting the need for the instructors of these foundational courses to have access to simple yet effective tools for the early identification of students at high risk of academic failure. In this study, we propose a streamlined conceptual model inspired by the Model of Human Behavior (MHB) to which we have incorporated two dimensions: capacity and willingness. These dimensions are assessed through the definition of three variables: Prior Academic Performance (PAP), Personality and Academic Engagement, whose measurements can easily be obtained by the instructors. Furthermore, we outline a Machine Learning (ML) process that higher education instructors can use to create their own tailored models in order to predict AP and identify risk groups with high levels of transparency and interpretability. The application of our approach to a sample of 322 Spanish undergraduates studying two mathematical subjects at a Spanish university demonstrates its potential to detect failure early in the semester with a precision that is comparable with that of more complex models found in literature. Our tailored model identified that capacity was the primary predictor of AP, with a gain-to-baseline improvement of 21%, and the willingness variables increasing this to 27%. This approach is consistent over time. Implications for instructors are discussed and an open prediction and analysis tool is developed.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has been partially funded by the Instituto de Ciencias de la Educación (ICE) of the University of Alicante through their ‘Programa de redes de investigación en docencia universitaria’ in the 2023/2024 edition (Red 5942), and the UCLM group, cofinanced with ERDF funds (research grant 2022-GRIN-34113)

Influence of personality and modality on peer assessment evaluation perceptions using Machine Learning techniques

The successful instructional design of self and peer assessment in higher education poses several challenges that instructors need to be aware of. One of these is the influence of students’ personalities on their intention to adopt peer assessment. This paper presents a quasi-experiment in which 85 participants, enrolled in the first-year of a Computer Engineering programme, were assessed regarding their personality and their acceptance of three modalities of peer assessment (individual, pairs, in threes). Following a within-subjects design, the students applied the three modalities, in a different order, with three different activities. An analysis of the resulting 1195 observations using ML techniques shows how the Random Forest algorithm yields significantly better predictions for three out of the four adoption variables included in the study. Additionally, the application of a set of eXplainable Artificial Intelligence (XAI) techniques shows that Agreeableness is the best predictor of Usefulness and Ease of Use, while Extraversion is the best predictor of Compatibility, and Neuroticism has the greatest impact on global Intention to Use. The discussion highlights how, as it happens with other innovations in educational processes, low levels of Consciousness is the most consistent predictor of resistance to the introduction of peer assessment processes in the classroom. Also, it stresses the value of peer assessment to augment the positive feelings of students scoring high on Neuroticism, which could lead to better performance. Finally, the low impact of the peer assessment modality on student perceptions compared to personality variables is debated.This work has been partially funded by the University of Alicante’s Redes-I3CE de investigación en docencia universitaria del Instituto de Ciencias de la Educación (REDES-I3CE-2020-5069), by the EU Erasmus+ Programme (EduTech (609785-EPP-1-2019-1-ES-EPPKA2-CBHE-JP) and SkoPS (2020-1-DE01-KA226HE-005772) projects), by the Spanish Ministry of Science and Innovation (Access@IoT (PID2019-111196RB-I00) project), by the GVA (AICO/2020/143) project, and by the UCLM group cofinanced with ERDF funds (research grant 2021-GRIN-30993)

New Proposals to Improve a MAC Layer Protocol in Wireless Sensor Networks

The evolution of Wireless Sensor Networks has led to the development of protocols that must comply with their new restrictions while being efficient in terms of energy consumption and time. We focus on a collision resolution protocol, the so-called Two Cell Sorted (2CS-WSN). We propose three different ways to improve its performance by minimising the collision resolution time or the energy consumption. After evaluating these proposals and carrying out the comparison with the original protocol, we recommend an improvement to the protocol which reduces the elapsed time by early 8% and the number of retries and conflicts more than 40%

Complex Event Processing Modeling by Prioritized Colored Petri Nets

Complex event processing (CEP) is a technology that allows us to process and correlate large volumes of data by using event patterns, aiming at promptly detecting specific situations that could require special treatment. The event types and event patterns for a particular application domain are implemented by using an event processing language (EPL). Although some current model-driven tools allow end users to easily define these patterns, which are then transformed automatically into a particular EPL, the generated code is syntactically but not semantically validated. To deal with this problem, a prioritized colored Petri net (PCPN) model for CEP is proposed and conducted in this paper. This well-known graphical formalism together with CPNTools makes possible the modeling, simulation, analysis, and semantic validation of complex event-based systems. To illustrate this approach, a case study is presented, as well as a discussion on the benefits from using PCPN for modeling CEP-based systems.El procesamiento de eventos complejos (CEP) es una tecnología que nos permite procesar y correlacionar grandes volúmenes de datos utilizando patrones de eventos, con el objetivo de detectar rápidamente situaciones específicas que podrían requerir un tratamiento especial. Los tipos de eventos y patrones de eventos para un dominio de aplicación particular se implementan utilizando un lenguaje de procesamiento de eventos (EPL). Aunque algunas herramientas actuales impulsadas por modelos permiten a los usuarios finales definir fácilmente estos patrones, que luego se transforman automáticamente en un EPL particular, el código generado se valida sintácticamente pero no semánticamente. Para abordar este problema, en este documento se propone y lleva a cabo un modelo de red de Petri coloreada y priorizada (PCPN) para CEP. Este formalismo gráfico bien conocido junto con CPNTools hace posible la modelización, simulación, análisis y validación semántica de sistemas basados en eventos complejos. Para ilustrar este enfoque, se presenta un estudio de caso, así como una discusión sobre los beneficios de usar PCPN para modelar sistemas basados en CEP.This work was supported in part by the Spanish Ministry of Science and Innovation and the European Union FEDER Funds with the Project DArDOS entitled Formal development and analysis of complex systems in distributed contexts: foundations, tools and applications under Grant TIN2015-65845-C3, subprojects 2-R and 3-R, and the Research Network on Services Science and Engineering under Grant TIN2014-53986-REDT, and in part by the University of Cádiz under Project PR2016-032

Influencia de la evaluación entre pares consensuada en la precisión de las autoevaluaciones

En este trabajo se analiza el impacto del proceso de evaluación entre pares en la precisión con la que el alumno es capaz de autoevaluar su trabajo. El propósito de este análisis es discernir si la modalidad de evaluación entre pares a la que se ve expuesto el alumno (corrección individual, en parejas o en tríos) afecta su capacidad de autoevaluación. Para ello, se presenta un quasi-experimento realizado en la Universidad de Castilla-La Mancha con una muestra de 82 estudiantes de primer curso del grado en Ingeniería Informática divididos en tres grupos (A1, A2, B1). Los estudiantes realizaron tres entregas, y en cada una evaluaron a sus compañeros con una modalidad distinta. Antes de comenzar dicha evaluación, los estudiantes autoevaluaron su propio trabajo. Asimismo, los estudiantes volvieron a autoevaluarse tras evaluar a sus compañeros. La calificación del profesor de esas mismas entregas se usó para calcular la precisión de la autoevaluación. Por último, se analizaron las diferencias en precisión de los estudiantes antes y después de participar en el proceso de evaluación entre pares. Los resultados muestran que la modalidad de evaluación entre pares aplicada no afecta significativamente a la precisión de los alumnos a la hora de evaluar su propio trabajo.This paper analyzes the impact of the peer evaluation process on the accuracy with which the student is able to self-evaluate his work. The purpose of this analysis is to discern whether the modality of peer evaluation to which the student is exposed (individual correction, in pairs or in trios) affects his self-assessment ability. For this, a quasi-experiment is presented at the Universidad de Castilla-La Mancha. The study used a sample of 82 first year students of the Computer Engineering degree. The students were divided into three groups (A1, A2, B1). Then, they were asked to complete three assignments. In each one they evaluated their classmates with a different peer evaluation modality. Additionally, before beginning each evaluation, the students self-assessed their own work. Likewise, the students reassessed themselves after evaluating their classmates. The teacher’s grade of those same assignments was used to calculate the accuracy of the self-assessment. Finally, differences in student accuracy were analyzed before and after participating in the peer review process. The results show that the peer evaluation modality applied does not significantly affect the accuracy of the students when evaluating their own work.El presente trabajo ha sido parcialmente financiado por el programa Redes-I3CE de investigación en docencia universitaria del Instituto de Ciencias de la Educación de la Universidad de Alicante (REDES-I3CE-2019-4607) y por el proyecto EduTech (609785-EPP-1-2019-1-ES-EPPKA2-CBHE-JP), cofinanciado por el Programa Erasmus+ de la UE

Facilitating the Quantitative Analysis ofComplexEvents through a Computational Intelligence Model-Driven Tool

Complex event processing (CEP) is a computational intelligence technology capable of analyzing big data streams for event pattern recognition in real time. In particular, this technology is vastly useful for analyzing multicriteria conditions in a pattern, which will trigger alerts (complex events) upon their fulfillment. However, one of the main challenges to be faced by CEP is how to define the quantitative analysis to be performed in response to the produced complex events. In this paper, we propose the use of the MEdit4CEP-CPN model-driven tool as a solution for conducting such quantitative analysis of events of interest for an application domain, without requiring knowledge of any scientific programming language for implementing the pattern conditions. Precisely, MEdit4CEP-CPN facilitates domain experts to graphically model event patterns, transform them into a Prioritized Colored Petri Net (PCPN) model, modify its initial marking depending on the application scenario, and make the quantitative analysis through the simulation and monitor capabilities provided by CPN tools

Influencia de la evaluación entre pares consensuada en la precisión de las autoevaluaciones

En este trabajo se analiza el impacto del proceso de evaluación entre pares en la precisión con la que el alumno es capaz de autoevaluar su trabajo. El propósito de este análisis es discernir si la modalidad de evaluación entre pares a la que se ve expuesto el alumno (corrección individual, en parejas o en tríos) afecta su capacidad de autoevaluación. Para ello, se presenta un quasi-experimento realizado en la Universidad de Castilla-La Mancha con una muestra de 82 estudiantes de primer curso del grado en Ingeniería Informática divididos en tres grupos (A1, A2, B1). Los estudiantes realizaron tres entregas, y en cada una evaluaron a sus compañeros con una modalidad distinta. Antes de comenzar dicha evaluación, los estudiantes autoevaluaron su propio trabajo. Asimismo, los estudiantes volvieron a autoevaluarse tras evaluar a sus compañeros. La calificación del profesor de esas mismas entregas se usó para calcular la precisión de la autoevaluación. Por último, se analizaron las diferencias en precisión de los estudiantes antes y después de participar en el proceso de evaluación entre pares. Los resultados muestran que la modalidad de evaluación entre pares aplicada no afecta significativamente a la precisión de los alumnos a la hora de evaluar su propio trabajo.This paper analyzes the impact of the peer evaluation process on the accuracy with which the student is able to self-evaluate his work. The purpose of this analysis is to discern whether the modality of peer evaluation to which the student is exposed (individual correction, in pairs or in trios) affects his self-assessment ability. For this, a quasi-experiment is presented at the Universidad de Castilla-La Mancha. The study used a sample of 82 first year students of the Computer Engineering degree. The students were divided into three groups (A1, A2, B1). Then, they were asked to complete three assignments. In each one they evaluated their classmates with a different peer evaluation modality. Additionally, before beginning each evaluation, the students self-assessed their own work. Likewise, the students reassessed themselves after evaluating their classmates. The teacher’s grade of those same assignments was used to calculate the accuracy of the self-assessment. Finally, differences in student accuracy were analyzed before and after participating in the peer review process. The results show that the peer evaluation modality applied does not significantly affect the accuracy of the students when evaluating their own work

A Compositional Approach for Complex Event Pattern Modeling and Transformation to Colored Petri Nets with Black Sequencing Transitions

Prioritized Colored Petri Nets (PCPNs) are a well-known extension of plain Petri nets in which transitions can have priorities and the tokens on the places carry data information. In this paper, we propose an extension of the PCPN model with black sequencing transitions (BPCPN). This extension allows us to easily model the ordered firing of the same transition using an ordered set of tokens on one of its precondition places. Black sequencing transitions are then presented as a shorthand notation in order to model the processing of a flow of events, represented by one of their precondition places. We then show how black sequencing transitions can be encoded into PCPNs, and their application to model Complex Event Processing (CEP), defining a compositional approach to translate some of the most relevant event pattern operators. We have developed MEdit4CEP-BPCPN, an extension of the MEdit4CEP tool, to provide tool support for this novel technique, thus allowing end users to easily define event patterns and obtain an automatic translation into BPCPNs. This can, in turn, be transformed into a corresponding PCPN, and then be immediately used in CPN Tools. Finally, a health case study concerning the monitoring of pregnant women is considered to illustrate how the event patterns are created and how the BPCPN and PCPN models are obtained by using the MEdit4CEP-BPCPN tool

MEdit4CEP-CPN: An approach for complex event processing modeling by prioritized colored petri nets

Complex Event Processing (CEP) is an event-based technology that allows us to process and correlate large data streams in order to promptly detect meaningful events or situations and respond to them appropriately. CEP implementations rely on the so-called Event Processing Languages (EPLs), which are used to implement the specific event types and event patterns to be detected for a particular application domain. To spare domain experts this implementation, the MEdit4CEP approach provides them with a graphical modeling editor for CEP domain, event pattern and action definition. From these graphical models, the editor automatically generates a corresponding Esper EPL code. Nevertheless, the generated code is syntactically but not semantically validated. To address this problem, MEdit4CEP is extended in this paper by Prioritized Colored Petri Net (PCPN) formalism, resulting in the MEdit4CEP-CPN approach. This approach provides both a novel PCPN domain-specific modeling language and a graphical editor. By using model transformations, event pattern models can be automatically transformed into PCPN models, and then into the corresponding PCPN code executable by CPN Tools. In addition, by using PCPNs we can compare the expected output with the actual output and can even conduct a quantitative analysis of the scenarios of interest. To illustrate our approach, we have conducted an air quality level detection case study and we show how this novel approach facilitates the modeling, simulation, analysis and semantic validation of complex event-based systems