Journal articles: 'Learning Analytics (LA)'

1

Søby, Morten. "Learning Analytics." Nordic Journal of Digital Literacy 9, no. 02 (2014): 89–91. http://dx.doi.org/10.18261/issn1891-943x-2014-02-01.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Slade, Sharon, and Paul Prinsloo. "Learning Analytics." American Behavioral Scientist 57, no. 10 (2013): 1510–29. http://dx.doi.org/10.1177/0002764213479366.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Siemens, George. "Learning Analytics." American Behavioral Scientist 57, no. 10 (2013): 1380–400. http://dx.doi.org/10.1177/0002764213498851.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Ifenthaler, Dirk. "Learning Analytics." Zeitschrift SEMINAR 28 (September 23, 2022): 52–63. http://dx.doi.org/10.3278/sem2203w005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Ebner, Martin, Kinshuk, David Wohlhart, Benham Taraghi, and Vive Kumar. "Learning Analytics." JUCS - Journal of Universal Computer Science 21, no. (1) (2015): 1–6. https://doi.org/10.3217/jucs-021-01.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Webb, Stephen. "Learning analytics explained." Innovations in Education and Teaching International 54, no. 6 (2017): 625–26. http://dx.doi.org/10.1080/14703297.2017.1380692.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Chatti, Mohamed Amine, Anna Lea Dyckhoff, Ulrik Schroeder, and Hendrik Thüs. "Forschungsfeld Learning Analytics." i-com 11, no. 1 (2012): 22–25. http://dx.doi.org/10.1524/icom.2012.0007.

Full text

Abstract:

Summary Learning analytics has attracted a great deal of attention in technology enhanced learning (TEL) in recent years as educational institutions and researchers are increasingly seeing the potential that learning analytics has to support the learning process. Learning analytics has been identified as a possible key future trend in learning and teaching (Johnson et al., 2011). Analytics can be a powerful tool to support learning. There are, however, a number of issues that need to be addressed before starting analytics projects. In this paper, we identify various challenges and research opportunities in the emerging area of learning analytics.

APA, Harvard, Vancouver, ISO, and other styles

8

Pistilli, Matthew D. "Learning Analytics Explained." Open Learning: The Journal of Open, Distance and e-Learning 33, no. 3 (2018): 267–69. http://dx.doi.org/10.1080/02680513.2018.1486187.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Suzani, Mohamad Samuri. "LACLOD: Learning Analytics for Children's Logic Development." International Journal of Multimedia & Its Applications (IJMA) 13, no. 1/2 (2021): 1–14. https://doi.org/10.5281/zenodo.4899482.

Full text

Abstract:

Learning Analytics for Children's Logic Development (LACLOD) is a web-based and mobile friendly learning analytic platform for assessing the logic development of children age 3 to 4 years old in TASKA PERMATA UPSI Malaysia. The platform is developed using Unity and connected through Google Analytics (GA) plugin where it tracked the user interaction for the application. LACLOD is designed only for mobile or tablet which is using Android. In this paper, the development of this learning analytic platform is presented. For evaluation of this system, observation and survey have been used, to get the feedback from 2 teachers (female) and 3 children (2 female and 1 male). Based on the evaluation, it can be seen that there are still rooms for improvement. Female children found it quit hard to understand the game but the male children looked satisfy because he knew on how to navigate the app and he actively played the app by himself. As for teachers, the acceptance to this kind of assessment is moderate, however they agree that this application can better improve the children’s learning especially in logic development.

APA, Harvard, Vancouver, ISO, and other styles

10

Maseleno, Andino, Noraisikin Sabani, Miftachul Huda, Roslee Ahmad, Kamarul Azmi Jasmi, and Bushrah Basiron. "Demystifying Learning Analytics in Personalised Learning." International Journal of Engineering & Technology 7, no. 3 (2018): 1124. http://dx.doi.org/10.14419/ijet.v7i3.9789.

Full text

Abstract:

This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in considering personalised learning within the context framework of learning analytics.

APA, Harvard, Vancouver, ISO, and other styles

11

Narayan Koranchirath, Nithin. "Impact of Machine Learning on Healthcare Analytics." International Journal of Science and Research (IJSR) 13, no. 2 (2024): 942–47. http://dx.doi.org/10.21275/sr24210203022.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Siemens, George. "The Journal of Learning Analytics: Supporting and Promoting Learning Analytics Research." Journal of Learning Analytics 1, no. 1 (2014): 3–5. http://dx.doi.org/10.18608/jla.2014.11.2.

Full text

Abstract:

The paper gives a brief overview of the main activities for the development of the emerging field of learning analytics led by the Society for Learning Analytics Research (SoLAR). The place of the Journal of Learning Analytics is identified Analytics is the most significant new initiative of SoLAR.

APA, Harvard, Vancouver, ISO, and other styles

13

Shahril Khuzairi, Nur Maisarah, Manjit Singh Sidhu, and Zaihisma Che Cob. "Learning Analytics and Teaching Analytics: The Similarities and Differences." International Journal of Humanities, Management and Social Science 3, no. 2 (2020): 52–58. http://dx.doi.org/10.36079/lamintang.ij-humass-0302.135.

Full text

Abstract:

Analytics in education which constitutes of Learning Analytics and Teaching Analytics arouses great attention among researchers and practitioners in the current climate. The use of analytics in education enables educational data to be collected and analysed to serve the needs of all stakeholders to improve the educational process. The present paper gives an overview of Learning Analytics and Teaching Analytics and explores its similarities and differences, as well as the confusion that has been raised between the two defined terms. Alongside, the analytics selection flowchart presented in this paper provides a breakdown on the analytics research direction for Learning Analytics and Teaching Analytics. A deeper and varied understanding of Learning Analytics and Teaching Analytics is imperative for establishing effective and accurate analytical tools alongside with recommendations for improvement in the future.

APA, Harvard, Vancouver, ISO, and other styles

14

James, Nikki. "Learning Analytics to Support Experiential Learning." Experiential Learning and Teaching in Higher Education 3, no. 3 (2022): 8. http://dx.doi.org/10.46787/elthe.v3i3.3420.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Hernandez Leo, Davinia, María Jesús Rodríguez-Triana, Paul Salvador Inventado, and Yishay Mor. "Connecting Learning Design and Learning Analytics." Interaction Design and Architecture(s), no. 33 (June 20, 2017): 3–8. http://dx.doi.org/10.55612/s-5002-033-001psi.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Kalshetti, Prof U. M., Keyur Kulkarni, Deepenkumar Patel, and Sharang Nimbalkar. "Students Learning Evaluation Using Learning Analytics." International Journal of Advanced Engineering Research and Science 4, no. 4 (2017): 89–92. http://dx.doi.org/10.22161/ijaers.4.4.11.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Hansen, Randy. "Learning Analytics: Data for Learning Design." Journal of Digital Learning in Teacher Education 32, no. 2 (2016): 48–49. http://dx.doi.org/10.1080/21532974.2016.1158029.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Joksimovic, Srecko, Dragan Gasevic, and Marek Hatala. "Learning Analytics for Networked Learning Models." Journal of Learning Analytics 1, no. 3 (2014): 191–94. http://dx.doi.org/10.18608/jla.2014.13.20.

Full text

Abstract:

Teaching and learning in networked setting has attained a significant amount of attention recently. The central topic of networked learning research is human-human and human-information interactions that occur within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach in analyzing their effects. Therefore, the main goal of this research is the development of a theoretical model that allows for a comprehensive and scalable analysis of how and why learners engage into collaboration in networked communities. The proposed research method, anticipated research outcomes and contributions to the learning analytics field are discussed.

APA, Harvard, Vancouver, ISO, and other styles

19

Pechenizkiy, Mykola, and Dragan Gasevic. "Introduction into Sparks of the Learning Analytics Future." Journal of Learning Analytics 1, no. 3 (2015): 145–49. http://dx.doi.org/10.18608/jla.2014.13.8.

Full text

Abstract:

This section offers a compilation of 16 extended abstracts summarizing research of the doctoral students who participated in the Second Learning Analytics Summer Institute (LASI 2014) held at Harvard University in July 2014. The abstracts highlight the motivation, main goals and expected contributions to the field from the ongoing learning analytics doctoral research around the globe. These works cover several major topics in learning analytics including novel methods for automated annotations, longitudinal analytic studies, networking analytics, multi-modal analytics, dashboards, and data-driven feedback and personalization. The assumed settings include the traditional classroom, online and mobile learning, blended learning, and massive open online course education models.

APA, Harvard, Vancouver, ISO, and other styles

20

Knobbout, Justian, and Esther Van der Stappen. "A Capability Model for Learning Analytics Adoption: Identifying Organizational Capabilities from Literature on Learning Analytics, Big Data Analytics, and Business Analytics." International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI) 2, no. 1 (2020): 47. http://dx.doi.org/10.3991/ijai.v2i1.12793.

Full text

Abstract:

Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?” Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – Data, Management, People, Technology, and Privacy &amp; Ethics. Capabilities presently absent from existing learning analytics frameworks concern sourcing and integration, market, knowledge, training, automation, and connectivity. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.

APA, Harvard, Vancouver, ISO, and other styles

21

Wang, Shouhong, and Hai Wang. "Knowledge Analytics." International Journal of Business Analytics 7, no. 4 (2020): 14–23. http://dx.doi.org/10.4018/ijban.2020100102.

Full text

Abstract:

Big data has raised challenges and opportunities for the education sector. Educational analytics encompass a variety of computational techniques to process educational big data for effective teaching, learning, research, service, and administrative decision making. Learning analytics and academic analytics have been widely discussed in the literature of education; however, knowledge analytics have not been discussed in the educational analytics field. Knowledge analytics are a relatively new subject in the knowledge management area. Knowledge analytics lie outside of the definitions of learning analytics and academic analytics, and encompass analytical activities for knowledge management among educators in teaching, research, and services. This paper discusses potential applications of knowledge analytics in educational institutions and issues related to implementation of knowledge analytics in the educational environment.

APA, Harvard, Vancouver, ISO, and other styles

22

Wise, Alyssa, Yuting Zhao, and Simone Hausknecht. "Learning Analytics for Online Discussions: Embedded and Extracted Approaches." Journal of Learning Analytics 1, no. 2 (2014): 48–71. http://dx.doi.org/10.18608/jla.2014.12.4.

Full text

Abstract:

This paper describes an application of learning analytics that builds on an existing research program investigating how students contribute and attend to the messages of others in asynchronous online discussions. We first overview the E-Listening research program and then explain how this work was translated into analytics that students and instructors could use to reflect on their discussion participation. Two kinds of analytics were designed: some embedded in the learning environment to provide students with real-time information on their activity in-progress; and some extracted from the learning environment and presented to students in a separate digital space for reflection. In addition, we describe the design of an intervention though which use of the analytics can be introduced as an integral course activity. Findings from an initial implementation of the application indicated that the learning analytics intervention supported changes in students’ discussion participation. Five issues for future work on learning analytics in online discussions are presented. One, unintentional versus purposeful change; two, differing changes prompted by the same analytic; three, importance of theoretical buy-in and calculation transparency for perceived analytic value; four, affective components of students’ reactions; and five, support for students in the process of enacting analytics-driven changes.

APA, Harvard, Vancouver, ISO, and other styles

23

Macfadyen, Leah P., Lori Lockyer, and Bart Rienties. "Learning Design and Learning Analytics: Snapshot 2020." Journal of Learning Analytics 7, no. 3 (2020): 6–12. http://dx.doi.org/10.18608/jla.2020.73.2.

Full text

Abstract:

“Learning design” belongs to that interesting class of concepts that appear on the surface to be simple and self-explanatory, but which are actually definitionally vague and contested in practice. Like “learning analytics,” the field of learning design aspires to improve teaching practice, the learning experience, and learning outcomes. And like learning analytics, this interdisciplinary field also lacks a shared language, common vocabulary, or agreement over its definition and purpose, resulting in uncertainty even about who its practitioners are — Educators? Designers? Researchers? All of these? (Law, Li, Farias Herrera, Chan & Pong, 2017). Almost a decade ago, however, learning analytics researchers pointed to the rich potential for synergies between learning analytics and learning design (Lockyer & Dawson, 2011). These authors (and others since, as cited below) argued that effective alignment of learning analytics and learning design would benefit both fields, and would offer educators and investigators the evidence they need that their efforts and innovations in learning design are “worth it” in terms of improving teaching practice and learning: "The integration of research related to both learning design and learning analytics provides the necessary contextual overlay to better understand observed student behavior and provide the necessary pedagogical recommendations where learning behavior deviates from pedagogical intention" (Lockyer & Dawson, 2011, p. 155).

APA, Harvard, Vancouver, ISO, and other styles

24

Zainuddin, Muhammad Izzat Izzuddin bin, and Hairulliza Mohamad Judi. "Personalised Learning Analytics: Promoting Student’s Achievement and Enhancing Instructor’s Intervention in Self-regulated Meaningful Learning." International Journal of Information and Education Technology 12, no. 11 (2022): 1243–47. http://dx.doi.org/10.18178/ijiet.2022.12.11.1745.

Full text

Abstract:

Academic monitoring is implemented at higher learning institutions to allow students and instructors to communicate academically, especially learning progress. However, the system cannot monitor student performance on an ongoing basis, such as class attendance, continuous assessment records and assignment submissions. Personalised learning analytics use student-generated data and analytical models to gather learning patterns so that instructors may advise on students’ learning. Although various studies provide insight into the analytical framework of learning, attention to self-regulated meaningful learning is still insufficient. This study aims to propose a personalised learning analytics system designed by a student that unifies the self-regulated learning components: plan, monitor, and evaluate the learning commitment, and activates alert of student’s achievement for close monitoring and further intervention by the instructor. For this reason, the procedure for analysing the learning pattern for experiment subjects such as Internet of Things, Data Analysis and System Management. Personalised learning analytics has been designed to deliver an interactive learning analytics environment that stimulates students to focus on the achievement of problem-solving skills and enhance the instructor’s decision to support students’ concern.

APA, Harvard, Vancouver, ISO, and other styles

25

Bonnin, Geoffray, and Anne Boyer. "Apport des Learning Analytics." Administration & �ducation N�146, no. 2 (2015): 125. http://dx.doi.org/10.3917/admed.146.0125.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Ifenthaler, Dirk, and Clara Schumacher. "Learning Analytics im Hochschulkontext." WiSt - Wirtschaftswissenschaftliches Studium 45, no. 4 (2016): 176–81. http://dx.doi.org/10.15358/0340-1650-2016-4-176.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Koh, Ying Yun Juliana, Henk G. Schmidt, Naomi Low-Beer, and Jerome I. Rotgans. "Team-Based Learning Analytics." Academic Medicine 95, no. 6 (2020): 872–78. http://dx.doi.org/10.1097/acm.0000000000003157.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Verbert, Katrien, Erik Duval, Joris Klerkx, Sten Govaerts, and José Luis Santos. "Learning Analytics Dashboard Applications." American Behavioral Scientist 57, no. 10 (2013): 1500–1509. http://dx.doi.org/10.1177/0002764213479363.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Keshavamurthy, Usha, and Dr H. S. Guruprasad. "Learning Analytics: A Survey." International Journal of Computer Trends and Technology 18, no. 6 (2014): 260–64. http://dx.doi.org/10.14445/22312803/ijctt-v18p155.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Hershkovitz, Arnon, Simon Knight, Shane Dawson, Jelena Jovanović, and Dragan Gašević. "About "Learning" and "Analytics"." Journal of Learning Analytics 3, no. 2 (2016): 1–5. http://dx.doi.org/10.18608/jla.2016.32.1.

Full text

Abstract:

This issue of the Journal of Learning Analytics features three special sections that look into topics of learning analytics for 21st century skills, multimodal learning analytics, and sharing of datasets for learning analytics. The issue also features a paper that looks at models for early detection of students at risk in tertiary education. The editorial concludes with a summary of the changes in the editorial team of the journal.

APA, Harvard, Vancouver, ISO, and other styles

31

Johannes, Paul C., Christian L. Geminn, and Maxi Nebel. "Learning Analytics nach Satzung." Datenschutz und Datensicherheit - DuD 47, no. 11 (2023): 715–20. http://dx.doi.org/10.1007/s11623-023-1849-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Chahal, Ayushi, Preeti Gulia, and Nasib Singh Gill. "Different analytical frameworks and bigdata model for internet of things." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1159–66. https://doi.org/10.11591/ijeecs.v25.i2.pp1159-1166.

Full text

Abstract:

Sensor devices used in internet of things (IoT) enabled environment produce large amount of data. This data plays a major role in bigdata landscape. In recent years, correlation, and implementation of bigdata and IoT is being extrapolated. Nowadays, predictive analytics is gaining attention of many researchers for big IoT data analytics. This paper summarizes different sort of IoT analytical platforms which consist in-built features for further use in machine learning, MATLAB, and data security. It emphasizes on different machine learning algorithms that plays important role in big IoT data analytics. Besides different analytical frameworks, this paper highlights the proposed model for bigdata in IoT domain and elaborates different forms of data analytical methods. Proposed model comprises different phases i.e., data storing, data cleaning, data analytics, and data visualization. These phases cover the basic characteristics of bigdata V’s model and most important phase is data analytics or big IoT analytics. This model is implemented using an IoT dataset and results are presented in graphical and tabular form using different machine learning techniques. This study enhances researchers’ knowledge about various IoT analytical platforms and usability of these platforms in their respective problem domains.

APA, Harvard, Vancouver, ISO, and other styles

33

J, Prathipa. "Stock Market Analytics: Statistical and Machine Learning Techniques." International Journal of Psychosocial Rehabilitation 24, no. 1 (2020): 1828–33. http://dx.doi.org/10.37200/ijpr/v24i1/pr200284.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Tadi, Venkata. "Transparent Machine Learning: Building Trust in Data Analytics." International Journal of Science and Research (IJSR) 9, no. 12 (2020): 1850–57. http://dx.doi.org/10.21275/sr24709205430.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Ngulube, Patrick, and Mthokozisi Masumbika Ncube. "Leveraging Learning Analytics to Improve the User Experience of Learning Management Systems in Higher Education Institutions." Information 16, no. 5 (2025): 419. https://doi.org/10.3390/info16050419.

Full text

Abstract:

This systematic review examines the application of learning analytics to enhance user experience within Learning Management Systems in higher education institutions. Addressing a salient knowledge gap regarding the optimal integration of learning analytics for diverse learner populations, this study identifies analytical approaches and delineates implementation challenges that contribute to data misinterpretation and underutilisation. Consequently, the absence of a systematic evaluation of analytical methodologies impedes the capacity of higher education institutes to tailor learning processes to individual student needs. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a search was conducted across five academic databases. Studies employing learning analytics within Learning Management Systems environments to improve user experience in higher education institutions were included, while purely theoretical or non-higher education institution studies were excluded, resulting in a final corpus of 41 studies. Methodological rigour was assessed using the Critical Appraisal Skills Programme Checklist. This study revealed diverse learning analytics methodologies and a dual research focus on specific platforms and broader impacts on Learning Management Systems. However, ethical, implementation, generalisability, interpretation, personalisation, and system quality challenges impede effective learning analytics integration for user experience improvement, demanding rigorous and contextually aware strategies. This study’s reliance on existing literature introduces potential selection and database biases. As such, future research should prioritise empirical validation and cross-institutional studies to address these limitations.

APA, Harvard, Vancouver, ISO, and other styles

36

Bozkurt, Aras, and C. Sharma Ramesh. "Exploring the learning analytics equation: What about the carpe diem of teaching and learning?" Asian Journal of Distance Education 17, no. 2 (2022): i—xiv. https://doi.org/10.5281/zenodo.7402312.

Full text

Abstract:

Humans have always been lured by the idea that they can use data to understand a phenomenon and make predictions about it. Learning analytics, in this sense, promise to understand and optimize learning and the environments in which it occurs by collecting data from learners and learning contexts. In this regard, this study systematically examines research on learning analytics through bibliometric, data mining, and analytics approaches. This paper argues that research interest in learning analytics is increasing steadily; some countries, higher education institutions, and researchers have a specific research agenda that indicates their intention to specialize in that field. It is also noted that there is a need for more interdisciplinary studies on learning analytics and a further need to merge technological capabilities with pedagogy. Based on the findings of text-mining and social network analysis, the following themes were identified: (1) learning analytics to improve teaching and personalize learning, (2) hegemony of data-driven teaching and learning practices, (3) multimodal learning analytics as the next generation practice, (4) learning design for learning analytics, (5) formative assessment through learning analytics, (6) learning analytics for social online learning spaces, and (7) privacy and ethical concerns to overcome. This paper suggests focusing on issues such as ethics and privacy and warns researchers to pay attention to the risks of both an educational panoptic society and quantified decision-making processes. Furthermore, rather than relying on algorithms, it is suggested to incorporate social values and center the learners in the learning analytics processes. Finally, this paper asks the following: “If we quantify the learning processes, how can we benefit from the carpe diem moment of teaching and learning and then seize the beauty of educational processes?”

APA, Harvard, Vancouver, ISO, and other styles

37

Prof., Suman Acharya. "Crime Analytics using Machine Learning." International Journal of Inventive Engineering and Sciences (IJIES) 10, no. 3 (2023): 1–5. https://doi.org/10.35940/ijies.F7444.0310323.

Full text

Abstract:

Abstract: Crime is one of the most significant and pervasive problems in our society, and preventing it is a crucial duty. A large number of crimes are perpetrated each day. Maintaining and analyzing crime data to forecast and solve crimes is the current issue. This project analyzes a large dataset of crimes and predicts future crimes based on conditions. This project uses data science and machine learning for India's crime data prediction. Thus, Decision Tree, Logistic Regression, Multi-Regression, k-NN, Lasso & Ridge, and Random Forest are all involved in the supervised classification problem. Predicting crimes and classifying effective pattern detection and visualization equipment Utilizing crime data trends from the past allows us to correlate aspects that may help us comprehend the breadth of crimes in the future. This study uses visualization and machine learning methods to estimate future crime rates. First, raw datasets were processed and displayed

APA, Harvard, Vancouver, ISO, and other styles

38

Reinders, Hayo. "Learning Analytics for Language Learning and Teaching." JALT CALL Journal 14, no. 1 (2018): 77–86. http://dx.doi.org/10.29140/jaltcall.v14n1.225.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Reinders, Hayo. "Learning analytics for language learning and teaching." JALT CALL Journal 14, no. 1 (2018): 77–86. http://dx.doi.org/10.29140/jaltcall.v14n1.j225.

Full text

Abstract:

If only we could know what our students were up to at any given moment in class. Who is paying attention, and who is falling asleep? Who understands the past perfect and who thinks it is about something wonderful that happened yesterday? And wouldn’t it be great if we knew who is motivated and who is ready to drop out of the course? Language teachers perhaps struggle with these questions even more than teachers in other domains, because their students are not able to communicate their preferences and needs as well as l1 speakers. Learning analytics involves monitoring student engagement and comprehension and can be used as a way to identify potential problems early on in a course. In this short article I will describe what learning analytics is, how it can work in practice, as well as its potential benefits and drawbacks for language learning and teaching.

APA, Harvard, Vancouver, ISO, and other styles

40

Chen, Weiqin. "Knowledge-Aware Learning Analytics for Smart Learning." Procedia Computer Science 159 (2019): 1957–65. http://dx.doi.org/10.1016/j.procs.2019.09.368.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Emerson, Andrew, Elizabeth B. Cloude, Roger Azevedo, and James Lester. "Multimodal learning analytics for game‐based learning." British Journal of Educational Technology 51, no. 5 (2020): 1505–26. http://dx.doi.org/10.1111/bjet.12992.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Pfeiffer, Anke, and Dieter Uckelmann. "Fostering Lab-Based Learning with Learning Analytics." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 14 (2022): 4–27. http://dx.doi.org/10.3991/ijoe.v18i14.35073.

Full text

Abstract:

Digital learning environments, such as online laboratories offer many opportunities for collecting data for Learning Analytics (LA). This article presents a systematic literature review for LA in laboratory based learning environments for Higher Engineering Education, which yielded 23 key references. The focus of the study was formed by the following research questions (RQ): What types of data are currently collected in online laboratories (RQ 1)? How is LA used to support learning and teaching processes as well as the design of the online-laboratory environment (RQ 2)? What design recommendations for the use of LA in laboratory-based learning environments can be derived (RQ 3)? The gained results show that LA can be used to provide feedback for simple as well as for complex learning processes in online laboratories. Moreover, it assists data-informed decision making for teaching and learning processes as well as for the design of the lab environment. Implications for future research projects were derived based on the findings and should contribute to the advancement of research on LA in online laboratories.

APA, Harvard, Vancouver, ISO, and other styles

43

Agus, Rahmadi, and Suzani Mohamad Samuri. "Learning Analytics Contribution in Education and Child Development: A Review on Learning Analytics." Asian Journal of Assessment in Teaching and Learning 8 (December 23, 2018): 36–47. http://dx.doi.org/10.37134/ajatel.vol8.4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Agus, Rahmadi, and Suzani Mohamad Samuri. "Learning Analytics Contribution in Education and Child Development: A Review on Learning Analytics." Asian Journal of Assessment in Teaching and Learning 8 (December 23, 2018): 36–47. http://dx.doi.org/10.37134/ajatel.vol8.4.2018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Viberg, Olga, Barbara Wasson, and Agnes Kukulska-Hulme. "Mobile-assisted language learning through learning analytics for self-regulated learning (MALLAS): A conceptual framework." Australasian Journal of Educational Technology 36, no. 6 (2020): 34–52. http://dx.doi.org/10.14742/ajet.6494.

Full text

Abstract:

Many adult second and foreign language learners have insufficient opportunities to engage in language learning. However, their successful acquisition of a target language is critical for various reasons, including their fast integration in a host country and their smooth adaptation to new work or educational settings. This suggests that they need additional support to succeed in their second language acquisition. We argue that such support would benefit from recent advances in the fields of mobile-assisted language learning, self-regulated language learning, and learning analytics. In particular, this paper offers a conceptual framework, mobile-assisted language learning through learning analytics for self-regulated learning (MALLAS), to help learning designers support second language learners through the use of learning analytics to enable self-regulated learning. Although the MALLAS framework is presented here as an analytical tool that can be used to operationalise the support of mobile-assisted language learning in a specific exemplary learning context, it would be of interest to researchers who wish to better understand and support self-regulated language learning in mobile contexts. Implications for practice and policy:  MALLAS is a conceptual framework that captures the dimensions of self-regulated language learning and learning analytics that are required to support mobile-assisted language learning. Designers of mobile-assisted language learning solutions using MALLAS will have a solution with sound theoretically underpinned solution. Learning designers can use MALLAS as a guide to direct their design choices regarding the development of mobile-assisted language learning apps and services.

APA, Harvard, Vancouver, ISO, and other styles

46

Dyulicheva, Yu Yu. "Application of Learning Analytics in Higher Education: Datasets, Methods and Tools." Vysshee Obrazovanie v Rossii = Higher Education in Russia 33, no. 5 (2024): 86–111. http://dx.doi.org/10.31992/0869-3617-2024-33-5-86-111.

Full text

Abstract:

The accumulation of big educational data on the platforms of universities and social media leads to the need to develop tools for extracting regularities from educational data, which can be used for understanding the behavioral patterns of students and teachers, improve teaching methods and the quality of the educational process, as well as form sound strategies and policies for universities development. This article provides an analysis and systematization of datasets on available repositories, taking into account the learning analytics problems solved on their basis. In particular, the article notes the predominance of datasets aimed at solving analytical problems at the level of student’s behavior understanding, Datasets aimed at solving analytical problems at the level of understanding the needs of teachers and administrative and managerial staff of universities are practically absent. Meanwhile, the full potential of learning analytics tools can only be revealed by introducing an integrated approach to the analysis of educational data, taking into account the needs of all participants and organizers of the educational process.This review article discusses learning analytics methods related to the study of social interaction patterns between students and teachers, and learning analytics tools from the implementation of simple dashboards to complex frameworks that explore various levels of learning analytics. The problems and limitations that prevent learning analytics from realizing its potential in universities are considered. It is noted that universities are generally interested in introducing learning analytics tools that can improve the quality of the educational process by developing strategies for targeted support for individual groups of students, however, teachers treat such initiatives with caution due to a lack of data analysis skills and correct interpretation of analysis results. The novelty of this analytical review is associated with the consideration of learning analytics at different levels of its implementation in the context of approaches to openness, processing and analysis of educational data.This article will be of interest to developers of learning analytics tools, scientific and pedagogical workers, and administrative and managerial staff of universities from the point of view of forming an idea of the integrity of the university analytics process, taking into account various levels of analytics implementation aimed at understanding the needs and requirements of all participants in the educational process.

APA, Harvard, Vancouver, ISO, and other styles

47

Mangaroska, Katerina, and Michail Giannakos. "Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning." IEEE Transactions on Learning Technologies 12, no. 4 (2019): 516–34. http://dx.doi.org/10.1109/tlt.2018.2868673.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Tretow-Fish, Tobias Alexander Bang, and Md Saifuddin Khalid. "Methods for Evaluating Learning Analytics and Learning Analytics Dashboards in Adaptive Learning Platforms: A Systematic Review." Electronic Journal of e-Learning 21, no. 5 (2023): 430–49. http://dx.doi.org/10.34190/ejel.21.5.3088.

Full text

Abstract:

This research paper highlights and addresses the lack of a systematic review of the methods used to evaluate Learning Analytics (LA) and Learning Analytics Dashboards (LAD) of Adaptive Learning Platforms (ALPs) in the current literature. Addressing this gap, the authors built upon the work of Tretow-Fish and Khalid (2022) and analyzed 32 papers, which were grouped into six categories (C1-6) based on their themes. The categories include C1) the evaluation of LA and LAD design and framework, C2) the evaluation of user performance with LA and LAD, C3) the evaluation of adaptivity, C4) the evaluation of ALPs through perceived value, C5) the evaluation of Multimodal methods, and C6) the evaluation of the pedagogical implementation of ALP’s LA and LAD. The results include a tabular summary of the papers including the categories, evaluation unit(s), methods, variables and purpose. While there are numerous studies in categories C1-4 that focus on the design, development, and impact assessment of ALP's LA and LAD, there are only a few studies in categories C5 and C6. For the category of C5), very few studies applied any evaluation methods assessing the multimodal features of LA and LADs on ALPs. Especially for C6), evaluating the pedagogical implementation of ALP's LA and LAD, the three dimensions of signature pedagogy are used to assess the level of pedagogy evaluation. Findings showed that no studies focus on evaluating the deep or implicit structure of ALP's LA. All studies examine the structural surface dimension of learning activities and interactions between students, teachers, and ALP's LA and LAD, as examined in categories C2-C5. No studies were exclusively categorized as a C6 category, indicating that all studies evaluate ALP's LA and LAD on the surface structure dimension of signature pedagogy. This review highlights the lack of pedagogical methodology and theory in ALP's LA and LAD, which are recommended to be emphasized in future research and ALP development and implementation.

APA, Harvard, Vancouver, ISO, and other styles

49

Klašnja-Milićević, Aleksandra, Mirjana Ivanović, and Bela Stantić. "Designing Personalized Learning Environments — The Role of Learning Analytics." Vietnam Journal of Computer Science 07, no. 03 (2020): 231–50. http://dx.doi.org/10.1142/s219688882050013x.

Full text

Abstract:

Learning analytics, as a rapidly evolving field, offers an encouraging approach with the aim of understanding, optimizing and enhancing learning process. Learners have the capabilities to interact with the learning analytics system through adequate user interface. Such systems enables various features such as learning recommendations, visualizations, reminders, rating and self-assessments possibilities. This paper proposes a framework for learning analytics aimed to improve personalized learning environments, encouraging the learner’s skills to monitor, adapt, and improve their own learning. It is an attempt to articulate the characterizing properties that reveals the association between learning analytics and personalized learning environment. In order to verify data analysis approaches and to determine the validity and accuracy of a learning analytics, and its corresponding to learning profiles, a case study was performed. The findings indicate that educational data for learning analytics are context specific and variables carry different meanings and can have different implications on learning success prediction.

APA, Harvard, Vancouver, ISO, and other styles

50

Holmes, Wayne, Quan Nguyen, Jingjing Zhang, Manolis Mavrikis, and Bart Rienties. "Learning analytics for learning design in online distance learning." Distance Education 40, no. 3 (2019): 309–29. http://dx.doi.org/10.1080/01587919.2019.1637716.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Learning Analytics (LA)'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles