{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T22:48:30Z","timestamp":1762642110096,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,8,10]],"date-time":"2018-08-10T00:00:00Z","timestamp":1533859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The increasing availability of educational data provides the educational researcher with numerous opportunities to use analytics to extract useful knowledge to enhance teaching and learning. While learning analytics focuses on the collection and analysis of data about students and their learning contexts, teaching analytics focuses on the analysis of the design of the teaching environment and the quality of learning activities provided to students. In this article, we propose a data science approach that incorporates the analysis and delivery of data-driven solution to explore the role of teaching analytics, without compromising issues of privacy, by creating pseudocode that simulates data to help develop test cases of teaching activities. The outcome of this approach is intended to inform the development of a teaching outcome model (TOM), that can be used to inspire and inspect quality of teaching. The simulated approach reported in the research was accomplished through Splunk. Splunk is a Big Data platform designed to collect and analyse high volumes of machine-generated data and render results on a dashboard in real-time. We present the results as a series of visual dashboards illustrating patterns, trends and results in teaching performance. Our research aims to contribute to the development of an educational data science approach to support the culture of data-informed decision making in higher education.<\/jats:p>","DOI":"10.3390\/bdcc2030024","type":"journal-article","created":{"date-parts":[[2018,8,10]],"date-time":"2018-08-10T10:52:01Z","timestamp":1533898321000},"page":"24","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Data Science Approach for Simulating Educational Data: Towards the Development of Teaching Outcome Model (TOM)"],"prefix":"10.3390","volume":"2","author":[{"given":"Ifeanyi G.","family":"Ndukwe","sequence":"first","affiliation":[{"name":"Educational Technology, Higher Education Development Centre, University of Otago, Dunedin 9016, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1173-8225","authenticated-orcid":false,"given":"Ben K.","family":"Daniel","sequence":"additional","affiliation":[{"name":"Educational Technology, Higher Education Development Centre, University of Otago, Dunedin 9016, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Russell J.","family":"Butson","sequence":"additional","affiliation":[{"name":"Educational Technology, Higher Education Development Centre, University of Otago, Dunedin 9016, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,10]]},"reference":[{"key":"ref_1","unstructured":"Henke, N., Libarikian, A., and Wiseman, B. (2016). Straight talk about big data. McKinsey Quarterly, McKinsey & Company. Available online: https:\/\/www.mckinsey.com\/business-functions\/digital...\/straight-talk-about-big-data."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1111\/bjet.12230","article-title":"Big Data and analytics in higher education: Opportunities and challenges","volume":"46","author":"Daniel","year":"2015","journal-title":"Br. J. Educ. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1108\/ICT-10-2016-0069","article-title":"From Big Data to Big Impact: Analytics for teaching and learning in higher education","volume":"49","author":"Chaurasia","year":"2017","journal-title":"Ind. Commer. Train."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1111\/bjet.12273","article-title":"Learning design, teacher inquiry into student learning and learning analytics: A call for action","volume":"46","author":"Mor","year":"2015","journal-title":"Br. J. Educ. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1111\/bjet.12233","article-title":"A method for teacher inquiry in cross-curricular projects: Lessons from a case study","volume":"46","author":"Avramides","year":"2015","journal-title":"Br. J. Educ. Technol."},{"key":"ref_6","unstructured":"OECD (2013). Teachers for the 21st Century: Using Evaluation to Improve Teaching, OECD Publishing Organisation for Economic Co-Operation Development."},{"key":"ref_7","unstructured":"Santiago, P., and Benavides, F. (2009, January 1\u20132). Teacher evaluation: A conceptual framework and examples of country practices. Proceedings of the Paper for Presentation at the OECD Mexico, Mexico City, Mexico."},{"key":"ref_8","unstructured":"Daniel, B.K., and Butson, R. (2013, January 22\u201324). Technology Enhanced Analytics (TEA) in Higher Education. Proceedings of the International Association for Development of the Information Society, Fort Worth, TX, USA."},{"key":"ref_9","first-page":"269","article-title":"How leaders can support teachers with data-driven decision making: A framework for understanding capacity building","volume":"43","author":"Marsh","year":"2015","journal-title":"Educ. Manag. Adm. Lead."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kaufman, T.E., Graham, C.R., Picciano, A.G., Popham, J.A., and Wiley, D. (2014). Data-driven decision making in the K-12 classroom. Handbook of Research on Educational Communications and Technology, Springer.","DOI":"10.1007\/978-1-4614-3185-5_27"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s40593-015-0043-2","article-title":"Evaluation methods for intelligent tutoring systems revisited","volume":"26","author":"Greer","year":"2016","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"ref_12","first-page":"2017","article-title":"Empowering instructors in learning management systems: Interactive heat map analytics dashboard","volume":"2","author":"Bueckle","year":"2017","journal-title":"Retrieved Nov."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ong, V.K. (2015, January 12\u201316). Big data and its research implications for higher education: Cases from UK higher education institutions. Proceedings of the 2015 IIAI 4th International Congress on Advanced Applied Informatics, Okayama, Japan.","DOI":"10.1109\/IIAI-AAI.2015.178"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/00461520.2012.667064","article-title":"A perfect time for data use: Using data-driven decision making to inform practice","volume":"47","author":"Mandinach","year":"2012","journal-title":"Educ. Psychol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Prinsloo, P., and Slade, S. (2013, January 8\u201312). An evaluation of policy frameworks for addressing ethical considerations in learning analytics. Proceedings of the Third International Conference on Learning Analytics and Knowledge, Leuven, Belgium.","DOI":"10.1145\/2460296.2460344"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Daniel, B. (2017). Big Data and data science: A critical review of issues for educational research. Br. J. Educ. Technol.","DOI":"10.1111\/bjet.12595"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1093\/nsr\/nwt032","article-title":"Challenges of big data analysis","volume":"1","author":"Fan","year":"2014","journal-title":"Natl. Sci. Rev."},{"key":"ref_18","first-page":"30","article-title":"Penetrating the fog: Analytics in learning and education","volume":"46","author":"Siemens","year":"2011","journal-title":"EDUCAUSE Rev."},{"key":"ref_19","first-page":"32","article-title":"Data changes everything: Delivering on the promise of learning analytics in higher education","volume":"47","author":"Wagner","year":"2012","journal-title":"EDUCAUSE Rev."},{"key":"ref_20","first-page":"6","article-title":"Reclaiming the lead: Higher education\u2019s future and implications for technology","volume":"45","author":"Hrabowski","year":"2010","journal-title":"EDUCAUSE Rev."},{"key":"ref_21","first-page":"9","article-title":"The evolution of big data and learning analytics in American higher education","volume":"16","author":"Picciano","year":"2012","journal-title":"J. Asynchronous Learn. Netw."},{"key":"ref_22","unstructured":"Borgman, C.L., Abelson, H., Dirks, L., Johnson, R., Koedinger, K.R., Linn, M.C., Lynch, C.A., Oblinger, D.G., Pea, R.D., and Salen, K. (2008). Fostering Learning in the Networked World: The Cyberlearning Opportunity and Challenge, National Science Foundation. A 21st-Century Agenda for the National Science Foundation."},{"key":"ref_23","unstructured":"Butson, R., and Daniel, B. (2017). The Rise of Big Data and Analytics in Higher Education. The Analytics Process, Auerbach Publications."},{"key":"ref_24","first-page":"1082","article-title":"A framework for evaluating digital library services","volume":"8","author":"Choudhury","year":"2002","journal-title":"D-Lib Mag."},{"key":"ref_25","first-page":"103","article-title":"Teaching Analytics: A Clustering and Triangulation Study of Digital Library User Data","volume":"15","author":"Xu","year":"2012","journal-title":"Educ. Technol. Soc."},{"key":"ref_26","first-page":"1","article-title":"Big data analytics","volume":"19","author":"Russom","year":"2011","journal-title":"TDWI Best Practices Rep."},{"key":"ref_27","first-page":"4","article-title":"Business analytics: The next frontier for decision sciences","volume":"43","author":"Evans","year":"2012","journal-title":"Decis. Line"},{"key":"ref_28","unstructured":"Hong, W., and Bernacki, M.L. (July, January 29). A Prediction and Early Alert Model Using Learning Management System Data and Grounded in Learning Science Theory. Proceedings of the 10th International Conference on Educational Data Mining, Raleigh, NC, USA."},{"key":"ref_29","first-page":"3","article-title":"Big Data computing and clouds: Trends and future directions","volume":"79","author":"Calheiros","year":"2015","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.ijinfomgt.2016.05.013","article-title":"Big data reduction framework for value creation in sustainable enterprises","volume":"36","author":"Chang","year":"2016","journal-title":"Int. J. Inf. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.ijinfomgt.2014.10.007","article-title":"Beyond the hype: Big data concepts, methods, and analytics","volume":"35","author":"Gandomi","year":"2015","journal-title":"Int. J. Inf. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/MITP.2013.61","article-title":"Big data and transformational government","volume":"15","author":"Joseph","year":"2013","journal-title":"IT Prof."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1111\/jbl.12010","article-title":"Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management","volume":"34","author":"Waller","year":"2013","journal-title":"J. Bus. Logist."},{"key":"ref_34","first-page":"95","article-title":"A comparative study of data analysis techniques","volume":"3","author":"Bihani","year":"2014","journal-title":"Int. J. Emerg. Trends Technol. Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bousbia, N., and Belamri, I. (2014). Which contribution does EDM provide to computer-based learning environments. Educational Data Mining, Springer.","DOI":"10.1007\/978-3-319-02738-8_1"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.chb.2012.07.020","article-title":"Examining students\u2019 online interaction in a live video streaming environment using data mining and text mining","volume":"29","author":"He","year":"2013","journal-title":"Comput. Hum. Behav."},{"key":"ref_37","first-page":"24","article-title":"Data mining model for higher education system","volume":"43","author":"Ayesha","year":"2010","journal-title":"Eur. J. Sci. Res."},{"key":"ref_38","first-page":"1","article-title":"Mining educational data to reduce dropout rates of engineering students","volume":"4","author":"Pal","year":"2012","journal-title":"Int. J. Inf. Eng. Electron. Bus."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Parack, S., Zahid, Z., and Merchant, F. (2012, January 3\u20135). Application of data mining in educational databases for predicting academic trends and patterns. Proceedings of the 2012 IEEE International Conference on Technology Enhanced Education (ICTEE), Kerala, India.","DOI":"10.1109\/ICTEE.2012.6208617"},{"key":"ref_40","unstructured":"Gupta, S., and Choudhary, J. (2015, January 28). Academic Analytics: Actionable Intelligence in Teaching and Learning for Higher Education in Indian Institutions. Proceedings of the International Conference on Skill Development & Technological Innovations for Economic Growth (ICST-2015), Ghaziabad, India."},{"key":"ref_41","first-page":"n1","article-title":"Signals: Applying academic analytics","volume":"33","author":"Arnold","year":"2010","journal-title":"Educ. Q."},{"key":"ref_42","unstructured":"Arnold, K.E., and Pistilli, M.D. (May, January 29). Course signals at Purdue: Using learning analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, BC, Canada."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Pantazos, K., and Vatrapu, R. (2016, January 5\u20138). Enhancing the Professional Vision of Teachers: A Physiological Study of Teaching Analytics Dashboards of Students\u2019 Repertory Grid Exercises in Business Education. Proceedings of the 49th Hawaii International Conference on System Sciences (HICSS), Grand Hyatt, Kauai, HI, USA.","DOI":"10.1109\/HICSS.2016.14"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rienties, B., Boroowa, A., Cross, S., Kubiak, C., Mayles, K., and Murphy, S. (2016). Analytics4Action evaluation framework: A review of evidence-based learning analytics interventions at the Open University UK. J. Interact. Media Educ., 2016.","DOI":"10.5334\/jime.394"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Daniel, B. (2016). Big Data and Learning Analytics in Higher Education: Current Theory and Practice, Springer.","DOI":"10.1007\/978-3-319-06520-5_1"},{"key":"ref_46","first-page":"2","article-title":"Ethical issues in the big data industry","volume":"14","author":"Martin","year":"2015","journal-title":"MIS Q. Executive"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Prinsloo, P., and Slade, S. (2017). Big data, higher education and learning analytics: Beyond justice, towards an ethics of care. Big Data and Learning Analytics in Higher Education, Springer.","DOI":"10.1007\/978-3-319-06520-5_8"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Marks, A., and Maytha, A.A. (2018). Higher Education Analytics: New Trends in Program Assessments. World Conference on Information Systems and Technologies, Springer.","DOI":"10.1007\/978-3-319-77703-0_72"},{"key":"ref_49","first-page":"87","article-title":"Learning analytics considered harmful","volume":"16","author":"Dringus","year":"2012","journal-title":"J. Asynchronous Learn. Netw."},{"key":"ref_50","first-page":"58","article-title":"Design and implementation of a learning analytics toolkit for teachers","volume":"15","author":"Dyckhoff","year":"2012","journal-title":"J. Educ. Technol. Soc."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Roberts, L.D., Chang, V., and Gibson, D. (2017). Ethical considerations in adopting a university-and system-wide approach to data and learning analytics. Big Data and Learning Analytics in Higher Education, Springer.","DOI":"10.1007\/978-3-319-06520-5_7"},{"key":"ref_52","first-page":"1","article-title":"The implications of analytics for teaching practice in higher education","volume":"1","author":"Griffiths","year":"2012","journal-title":"CETIS Anal. Ser."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.compedu.2012.10.023","article-title":"Factors influencing beliefs for adoption of a learning analytics tool: An empirical study","volume":"62","author":"Ali","year":"2013","journal-title":"Comput. Educ."},{"key":"ref_54","unstructured":"U.S. Department Education (2018, August 09). Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief, Available online: https:\/\/tech.ed.gov\/wp-content\/uploads\/2014\/03\/edm-la-brief.pdf."},{"key":"ref_55","first-page":"7","article-title":"Optimizing Data Analysis with a Semi-structured Time Series Database","volume":"10","author":"Bitincka","year":"2010","journal-title":"SLAML"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Van Loggerenberg, F., Vorovchenko, T., and Amirian, P. (2017). Introduction\u2014Improving Healthcare with Big Data. Big Data in Healthcare, Springer.","DOI":"10.1007\/978-3-319-62990-2_1"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zadrozny, P., and Kodali, R. (2013). Big Data Analytics Using Splunk: Deriving Operational Intelligence from Social Media, Machine Data, Existing Data Warehouses, and Other Real-Time Streaming Sources, Apress.","DOI":"10.1007\/978-1-4302-5762-2"},{"key":"ref_58","unstructured":"Carasso, D. (2012). Exploring Splunk, Published by CITO Research."},{"key":"ref_59","unstructured":"Emery, S. (2012). Factors for consideration when developing a bring your own device (BYOD) strategy in higher education. Applied Information Management Master\u2019s Capstone Projects and Papers, University of Oregon."},{"key":"ref_60","first-page":"5","article-title":"What data and analytics can and do say about effective learning","volume":"2","author":"Lodge","year":"2017","journal-title":"Sci. Learn."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/2\/3\/24\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:18:05Z","timestamp":1760195885000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/2\/3\/24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,10]]},"references-count":60,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,9]]}},"alternative-id":["bdcc2030024"],"URL":"https:\/\/doi.org\/10.3390\/bdcc2030024","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2018,8,10]]}}}