A Design Methodology for Learning Analytics Information Systems: Informing Learning Analytics Development with Learning Design

The paper motivates, presents and demonstrates a methodology for developing and evaluating learning analytics information systems (LAIS) to support teachers as learning designers. In recent years, there has been increasing emphasis on the benefits of learning analytics to support learning and teaching. Learning analytics can inform and guide teachers in the iterative design process of improving pedagogical practices. This conceptual study proposed a design approach for learning analytics information systems which considered the alignment between learning analytics and learning design activities. The conceptualization incorporated features from both learning analytics, learning design, and design science frameworks. The proposed development approach allows for rapid development and implementation of learning analytics for teachers as designers. The study attempted to close the loop between learning analytics and learning design. In essence, this paper informs both teachers and education technologists about the interrelationship between learning design and learning analytics

Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education

This research study delves into the conceptualization, development, and deployment of an innovative learning analytics tool, leveraging the capabilities of OpenAI's GPT-4 model. This tool is designed to quantify student engagement, map learning progression, and evaluate the efficacy of diverse instructional strategies within an educational context. Through the analysis of various critical data points such as students' stress levels, curiosity, confusion, agitation, topic preferences, and study methods, the tool offers a rich, multi-dimensional view of the learning environment. Furthermore, it employs Bloom's taxonomy as a framework to gauge the cognitive levels addressed by students' questions, thereby elucidating their learning progression. The information gathered from these measurements can empower educators by providing valuable insights to enhance teaching methodologies, pinpoint potential areas for improvement, and craft personalized interventions for individual students. The study articulates the design intricacies, implementation strategy, and thorough evaluation of the learning analytics tool, underscoring its prospective contributions to enhancing educational outcomes and bolstering student success. Moreover, the practicalities of integrating the tool within existing educational platforms and the requisite robust, secure, and scalable technical infrastructure are addressed. This research opens avenues for harnessing AI's potential in shaping the future of education, facilitating data-driven pedagogical decisions, and ultimately fostering a more conducive, personalized learning environment.Comment: 22 pages, 7 figures, 8537 word

Exploring The Effect of Online Course Design on Preservice Teachers’ Knowledge Transfer and Retention Through Learning Analytics

There is a vast amount of data collected on e-learning platforms that can provide insight and guidance to both learners and educators. However, this data is rarely used for evaluation and understanding the learning process. Hence, to fill this gap in the literature this study explored the effect of online course design on students’ transfer and retention of knowledge through learning analytics. The aim was to reveal study behaviours of participants over a short time while exploring their academic performance. Using a mixed method approach, this research is conducted in two different countries in a limited time. The results showed that the more times students visited the learning module and the longer these visits, the higher the students’ transfer knowledge scores in this module. Most importantly, the only variable found to be a significant predictor of students’ transfer learning outcome was the number of sessions in the module website

Data Analytics in Higher Education: An Integrated View

Data analytics in higher education provides unique opportunities to examine, understand, and model pedagogical processes. Consequently, the methodologies and processes underpinning data analytics in higher education have led to distinguishing, highly correlative terms such as Learning Analytics (LA), Academic Analytics (AA), and Educational Data Mining (EDM), where the outcome of one may become the input of another. The purpose of this paper is to offer IS educators and researchers an overview of the current status of the research and theoretical perspectives on educational data analytics. The paper proposes a set of unified definitions and an integrated framework for data analytics in higher education. By considering the framework, researchers may discover new contexts as well as areas of inquiry. As a Gestalt-like exercise, the framework (whole) and the articulation of data analytics (parts) may be useful for educational stakeholders in decision-making at the level of individual students, classes of students, the curriculum, schools, and educational systems

MEDIA PEMBELAJARAN MATEMATIKA MENYONGSONG INDUSTRY 4.0: TINJAUAN LITERATUR SISTEMATIS UNTUK ANALISIS KEBUTUHAN

Industry 4.0  yang menarik perhatian banyak peneliti, menuntut beberapa kompetensi yang harus dikuasai oleh siswa,dan untuk itu diperlukan penyesuaian media pembelajaran matematika. Dengan melaksanakan tinjauan sistematis terhadap literatur yang diterbitkan dari tahun 2012 hingga tahun 2019, dapat disimpulkan bahwa media pembelajaran matematika harus mempunyai komponen kelengkapan: (1) berorientasi pada STEAM, (2) memperhatikan saran-saran dari hasil penelitian bidang neuroscience dalam pembelajaran matematika, dan dalam m-learning, (3) mendorong pemberdayaan keterampilan digital untuk melaksanakan literasi, (4) mendorong kolaborasi virtual antara guru dengan siswa dan siswa dengan siswa, (5) memberdayakan VR, (6) dilengkapi dengan sistem ujian, (7) akuntabel, (8) aman, (9) memungkinkan pengambilan keputusan yang tepat berdasarkan analisa pembelajaran berbantuan kecerdasan buatan, (10) dapat dikembangkan dengan cepat

Oppimisanalytiikan hyödyntäminen erityisopetuksessa

Tiivistelmä. Oppimisanalytiikka on oppijoista ja oppimisympäristöistä kerätyn tiedon analysointia oppimisen tukemiseksi ja ymmärtämiseksi. Erityisopetus on oppimisen tukemista ja arviointi sekä diagnostiikka ovat erityisopetuksellisia työtapoja. Oppimisanalytiikka kehittää uusia menetelmiä ja työkaluja diagnostisiin tarpeisiin. Pedagogisia oppimisanalytiikan käyttökohteita ovat oppimisen yksilöllistäminen, oppijoiden itsereflektion kehittäminen sekä opetuksen suunnittelu. Erityisopetuksellisessa soveltamisessa esiin nousi oppimisympäristöjen tutkiminen ja kehittäminen, oppijoiden itsereflektion kehittäminen sekä oppimispelien analytiikka.Learning analytics in special education. Abstract. Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and supporting learning. Special education focuses on supporting learning and assessment and diagnostics are special educational work methods. Learning analytics develops new methods and tools for diagnostic needs. Pedagogical uses of learning analytics include personalization, learner’s self-reflection development and learning design. In special educational application, research and development of learning environments, learner’s self-reflection development and analytics of serious games

What types of data are used in learning analytics? An overview of six cases

The rapid development of learning analytics makes it difficult for the readership of research literature to gain a structured overview over the different types of data available and subject to the application of learning analytics techniques or methods. This special issue reunites six examples of application of different learning analytics approaches using various data types, aiming to achieve different goals, and employing different instruments and methods: eye tracking, automated online dialog analysis, survey data from school ecosystems, log data analysis at individual and collaborative level, and visual learning analytics applied to Internet-of-Things data. These case studies are provided in the framework of the observed process–data–transformation–analysis–output pattern of practices. Brief conclusions pertaining to advantages, limitations and future work are drawn

What types of data are used in learning analytics? An overview of six cases

The rapid development of learning analytics makes it difficult for the readership of research literature to gain a structured overview over the different types of data available and subject to the application of learning analytics techniques or methods. This special issue reunites six examples of application of different learning analytics approaches using various data types, aiming to achieve different goals, and employing different instruments and methods: eye tracking, automated online dialog analysis, survey data from school ecosystems, log data analysis at individual and collaborative level, and visual learning analytics applied to Internet-of-Things data. These case studies are provided in the framework of the observed process–data–transformation–analysis–output pattern of practices. Brief conclusions pertaining to advantages, limitations and future work are drawn

Learning Analytics aplicado ao curso online da L?ngua Brasileira de Sinais

O objetivo desta disserta??o ? apresentar os limites e as propostas que o Learning Analytics, aplicadas ao curso online de L?ngua Brasileira de Sinais da Universidade Federal de Minas Gerais, podem proporcionar. Como problema de pesquisa, tem-se a alta taxa de reprova??o da disciplina nos cursos de gradua??o em que ? obrigat?ria e o volume de dados para an?lise ? muito alto para ser feito sem o apoio do computador. Ademais, a base de dados possui poucas vari?veis de an?lise, que s?o: a frequ?ncia de acesso dos alunos aos Objetos de Aprendizagem; as notas dos alunos; os hor?rios de acesso e os cursos de proced?ncia, todos dispon?veis no Ambiente Virtual de Aprendizagem Moodle. Em especial, dada a limita??o do n?mero de vari?veis de an?lise, consideram-se os enunciados dos Objetos de Aprendizagem como as vari?veis de an?lise que distinguem esta pesquisa, aplicada ? L?ngua Brasileira de Sinais, de quaisquer outras disciplinas online. Como metodologia de pesquisa, a partir desses dados, foram gerados gr?ficos e computadas com data mining as regras que classificam os Objetos de Aprendizagem como os mais relevantes para se aprender L?ngua Brasileira de Sinais no formato online. A partir dos dados analisados nesse processo, como resultado da pesquisa, foi identificado o Objeto de Aprendizagem ?Produ??o de v?deo? como um dos principais Objetos de Aprendizagem do curso. Assim, foi esbo?ada uma proposta de um modelo visual para acompanhamento da aprendizagem no formato online tendo como base o Visual Learning Analytics. Nessa configura??o, s?o utilizados os par?metros da L?ngua Brasileira de Sinais, os n?veis lingu?sticos, os tipos de exerc?cios e o estilo de aprendizagem do discente como extens?o da rede de atividades do Visual Learning Analytics, com a finalidade de agrupar as similaridades, refinar o percurso dos alunos e permitir a interven??o do professor de modo mais eficiente.Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2019.The purpose of this dissertation is to present the limits and proposals from Learning Analytics, wich applied to the online course of Brazilian Sign Language of the Federal University of Minas Gerais, can provide. As a research problem, there is the high disapproval rate of this discipline in undergraduate courses where it is obligatory and the volume of data for analysis is too high to be done without computer support. In addition, the database has few variables to analyze, which are: the student?s access? frequency to the learning objects; student grades; access times and background courses, all available in the Moodle Virtual Learning Environment. In particular, given the limitation of the number of variables to analyze, the Learning Object statements are considered in the analysis distinguishing this research, applied to the Brazilian Sign Language, from any other online disciplines. As a research methodology, from these data, were generated graphs and computed with data mining the rules that classify Learning Objects as the most relevant to learn Brazilian Sign Language in online format. From the data analyzed in this process, as a result of the research, the Learning Object ?Video Production? was identified as one of the main Learning Objects of this course. Thus, a proposal of a visual model for tracking online learning based on Visual Learning Analytics was sketched. In this configuration, the parameters of the Brazilian Sign Language, the language levels, the types of exercises and the student?s learning style are used as an extension of the Visual Learning Analytics network of activities, in order to group the similarities, refine the students way on the course and enable the teacher to mediate more efficiently

Perspectives of IR Professionals Regarding the Impact of Data Analytic Systems on Institutional Decision- Making.

The capacity for data analytical decision-making is not always optimal in institutions of higher education (Hawkins & Bailey, 2020). Data analytic decision making for this study is defined as any decision utilized to improve the process or outcome for any function of higher educational administration (Nguyen et al., 2020) including but not limited to: state appropriated funding (e.g. Campbell, 2018) improving graduation rates (e.g Moscoso-Zea, Saa & Luján-Mora, 2019), teacher instruction (e.g. Cai & Zhu, 2015), or student success (e.g. Foster & Francis, 2020). Many IR professionals still face obstacles pertaining to their ability to both utilize data analytical software as well as share data analytical findings across their respective clientele units outside of institutional research to impact institutional decision-making (Lehman, 2017). The literature is lacking concerning how IR professionals experience and navigate these critical aspects of data analytical decision-making support in higher educational institutions. The purpose of this study was to address the gap in the research by assessing the perspectives of IR professionals regarding their ability to utilize data analytic systems (e.g., analyzing, interpreting, sharing of data) to impact and strengthen institutional decision-making. The purpose of this study was also to understand how institutional culture (e.g., policies, operational processes, relevancy, conduciveness) influences the ability of IR professionals to utilize data analytic systems when sharing data findings or collaborating across their respective institutions to enhance institutional decision-making. Recommendations based on the study findings included stronger data governance for dashboards and data visualizations, expanding predictive analytics to enhance student success, and data literacy training with both utilizing data analytics software and interpreting data findings according to the context of individual institutions