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Item Evaluating Learning Outcomes Through Curriculum Analytics: Actionable Insights for Curriculum Decision-making: A Design-based research approach to assess learning outcomes in higher education(ACM, 2025-03-05) Hernández-Campos, Mónica; Hilliger, Isabel; García-Peñalvo, Francisco JoséLearning analytics (LA) emerged with the promise of improving student learning outcomes (LOs), however, its effectiveness in informing actionable insights remains a challenge. Curriculum analytics (CA), a subfield of LA, seeks to address this by using data to inform curriculum development. This study explores using CA to evaluate LOs through direct standardized measures at the subject level, examining how this process informs curriculum decision-making. Conducted at an engineering-focused higher education institution, the research involved 32 administrators and 153 faculty members, serving 9.906 students across nine programs. By utilizing the Integrative Learning Design Framework, we conducted three phases of this framework and present key results. Findings confirm the importance of stakeholder involvement throughout different design phases, highlighting the need for ongoing training and support. Among the actionable insights that emerged from LOs assessments, we identified faculty reflections regarding the need to incorporate active learning strategies, improve course planning, and acknowledge the need for education-specific training for faculty development. Although the study does not demonstrate whether these insights lead to improvements in LOs, this paper contributes to the CA field by offering a practical approach to evaluating LOs and translating these assessments into actionable improvements within an actual-world educational context.Item Filling the gap in K-12 data literacy competence assessment: Design and initial validation of a questionnaire(Elsevier, 2025-03-01) Donate-Beby, Belén; García-Peñalvo, Francisco José; Amo-Filva, Daniel; Aguayo-Mauri, SofíaAs the integration of AI-powered technologies in education grows, data literacy has become a key competence for educators, shaping their ability to navigate and utilize vast amounts of educational data. This study details the development of the Educators Data Literacy Self-Assessment (EDLSA), a questionnaire designed to assess perceived data literacy among K-12 teachers, focusing on its behavioural implications. The development of the EDLSA was rigorous. It involved an exhaustive qualitative review of frameworks and a pilot test in a teachers' Spanish sample (n = 66) provided relevant insights for refining the instrument. Finally, we conducted a comprehensive statistical analysis, which confirmed the instrument's robust reliability (α = 0.976) in measuring teachers' data management competence. The results of the factorial analysis in piloting primary and secondary education samples led to the readjustment of the proposed dimensions into three categories: comprehensive educational analytics, educational problem-solving through data, and promoting meta-learning students through data and ethical implications. Stemmed from the assessed competencies, the EDLSA instrument provides a comprehensive understanding of the human-computer interaction over data in educational settings. Overall, this self-assessment tool presents robust psychometric properties and a framework definition that paves the way for further development among teachers and researchers.Item Personal Data Broker Instead of Blockchain for Students’ Data Privacy Assurance(Springer, 2019-04-01) Amo, D.; Fonseca, D.; Alier, M.; García-Peñalvo, F. J.; Casañ, M. J.Data logs about learning activities are being recorded at a growing pace due to the adoption and evolution of educational technologies (Edtech). Data analytics has entered the field of education under the name of learning analytics. Data analytics can provide insights that can be used to enhance learning activities for educational stakeholders, as well as helping online learning applications providers to enhance their services. However, despite the goodwill in the use of Edtech, some service providers use it as a means to collect private data about the students for their own interests and benefits. This is showcased in recent cases seen in media of bad use of students’ personal information. This growth in cases is due to the recent tightening in data privacy regulations, especially in the EU. The students or their parents should be the owners of the information about them and their learning activities online. Thus they should have the right tools to control how their information is accessed and for what purposes. Currently, there is no technological solution to prevent leaks or the misuse of data about the students or their activity. It seems appropriate to try to solve it from an automation technology perspective. In this paper, we consider the use of Blockchain technologies as a possible basis for a solution to this problem. Our analysis indicates that the Blockchain is not a suitable solu-tion. Finally, we propose a cloud-based solution with a central personal point of management that we have called Personal Data Broker.Item Learning Analytics as a Breakthrough in Educational Improvement(Springer Singapore, 2020-05-12) García-Peñalvo, F. J.Learning analytics has become a reference area in the field of Learning Technologies as the mixture of different technical and methodological approaches in the capture, treatment and representation of educational data for later use in decision-making pro-cesses. With approximately ten years of development, it can be considered that learn-ing analytics have abandoned their stage of dispersion and are heading towards a state of maturity that will position them as a fundamental piece in educational practice mediated by technology. However, it cannot be ignored that the power and good-ness of these analytics must be channelled to improve learning itself and, therefore, the learning-teaching process, always acting from an ethical sense and preserving the privacy of the people who participate because it is straightforward to invade personal spaces in favour of the objectives sought. This chapter presents, from a conceptual perspective, the reference models that support the creation of educational strategies based on learning analytics that integrate the most current trends in the field, defined from a critical perspective that balances the undoubted benefits with the potential risksItem GDPR Security and Confidentiality compliance in LMS’ a problem analysis and engineering solution proposal(ACM, 2019-10-16) Amo, D.; Alier, M.; García-Peñalvo, F. J.; Fonseca, D.; Casany, M. J.We have studied the main Learning Management Systems (LMSs) to comprehend how personal data is processed and stored. We found that all the users' personal information, activity, and logs are stored unencrypted on the server filesystem and databases. A user with access to such resources may have full access to all the personal information and meta-information stored. Therefore, the LMSs are very vulnerable to information leaks in front of targeted hacker attacks due to weak GDPR compliance. In this paper, we analyze this problem from a technical and operational perspective for the open-source market leader LMS Moodle, and we propose a solution and a prototype of implementation.Item Clickstream for learning analytics to assess students’ behavior with Scratch(2019-01-01) Amo Filvà, D.; Alier Forment, M.; García-Peñalvo, F. J.; Fonseca Escudero, D.; Casañ, M. J.The construction of knowledge through computational practice requires to teachers a substantial amount of time and effort to evaluate programming skills, to understand and to glimpse the evolution of the students and finally to state a quantitative judgment in learning assessment. The field of learning analytics has been a common practice in research since last years due to their great possibilities in terms of learning improvement. Both, Big and Small data techniques support the analysis cycle of learning analytics and risk of students’ failure prediction. Such possibilities can be a strong positive contribution to the field of computational practice such as programming. Our main objective was to help teachers in their assessments through to make those possibilities effective. Thus, we have developed a functional solution to categorize and understand students’ behavior in programming activities based in Scratch. Through collection and analysis of data generated by students’ clicks in Scratch, we proceed to execute both exploratory and predictive analytics to detect patterns in students’ behavior when developing solutions for assignments. We concluded that resultant taxonomy could help teachers to better support their students by giving real-time quality feedback and act before students deliver incorrectly or at least incomplete tasks.Item Using web analytics tools to improve the quality of educational resources and the learning process of students in a gamified situation(IATED Academy, 2018-03-05) Amo Filva, D.; Valls, A.; Alier Forment, Marc; Canaleta, X.; García-Peñalvo, F. J.; Fonseca, D.; Redondo, E.In this paper we propose a businessification approximation to measure and analyse students' engagement in a gamified learning context. Gamification in education is used to enhance students experience and improve learning outcomes. Its technics such as points, leaderboard, badges or ranking are also used in learning instructions with the aim to improve students' engagement. This engagement can be considered as the metric to measure the success of gamified instructions. The gamification model can also be used in an online learning environment. In this virtual context teachers have to have some tools to see what happens during the learning process. Such virtual context is usually web based. In this specific context the resources used such as images, videos or audios are fundamental to engage students. In order to help teachers to enhance engagement we propose the use of web analytical tools in such web based gamified learning contexts to track, analyse and finally enhance such resourcesItem Comparing Hierarchical Trees in Statistical Implicative Analysis & Hierarchical Cluster in Learning Analytics(ACM, 2017-10-18) Pazmiño-Maji, R. A.; García-Peñalvo, F. J.; Conde-González, M. Á.Learning Analytics has been and is still an emerging technology in education; the amount of research on learning analysis is increasing every year. The integration of new open source tools, analysis methods, and other calculation options are important. This paper aims to compare hierarchical trees in Statistical Implicative Analysis (SIA) and some hierarchical clusters in Learning Analytics. To this end, we must use a quasi-experimental design with random binary data. A comparison is about the time it takes to evaluate the function for execute the four cluster algorithms: cohesion tree (ASI), similarity tree (ASI), agnes (cluster R package) and hclust (R base function). This paper provides an alternative hierarchical cluster used in Statistical Implicative Analysis that is possible to use in Learning Analytics (LA). Also, provides a comparative R-program used and identifies future research about software performance.Item SIA’s asymmetric rules approximation to hierarchical clustering in Learning Analytics: mathematical issues(2017-07-17) Pazmiño, R. A.; García-Peñalvo, Francisco J.; Conde-González, M. Á.Bichsel, proposes an analytics maturity model used to evaluate the progress in the use of academic and learning analytics. In the progress, there are positive results but, most institutions are below 80% level. Most institutions also scored low for data analytics tools, reporting, and expertise"]. In addition, a task with the methods of Data Mining and Learning Analytics is analyze them (precision, accuracy, sensitivity, coherence, fitness measures, cosine, confidence, lift, similarity weights) for optimize and adapt them]. Learning Analytics was and continues to be an emerging technology. The time to adoption Horizon is one year or less but, how many institutions, teachers, learners and data analytics tools, are ready? Statistical Implicative Analysis (SIA) was created for Regis Gras , 45 years ago, SIA is a statistical theory which provides a group of data analytics tools to extract knowledge. The approach is performed starting from the generation of asymmetric rules similar to dendrograms used in the hierarchical clusters. But, the asymmetric rules can be used like a hierarchical clusters? The principal aim of this paper is to give mathematical issues of asymmetric rules to hierarchical clustering in Learning Analytics.Item Problems and opportunities in the use of technology to manage informal learning(2014-10) García-Peñalvo, Francisco J.; Griffiths, Dai; Jonhson, M.; Sharples, P.; Sherlock, D.There is a mismatch between the enthusiasm of policy makers and other actors for initiatives to support the validation of informal learning, and the lack of adoption of systems in practice. This problem is explored, with reference to the creation of the Informal Learning Collector in the European Commission TRAILER project. It is proposed that formality in learning can be usefully understood as a measure of the degree of managerial control over the learning process. It is then argued that the use of managerial tools, such as validation and competence catalogues, runs the danger of constraining the scope for informal learning. Analytics techniques offer the possibility of providing insight into practice by examining documents, without the need for formal description or tagging. However, these methods raise problems of surveillance (by companies and the state), confidentiality, and security of data. A prototype system is described which tests the feasibility of the approach.