nach oben

TechTrends

Erschienen in:

14.12.2016 | Original Paper

Designing Technology-Enabled Instruction to Utilize Learning Analytics

verfasst von: Randall Davies, Robert Nyland, Robert Bodily, John Chapman, Brian Jones, Jay Young

Erschienen in: TechTrends | Ausgabe 2/2017

Einloggen, um Zugang zu erhalten

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

A key notion conveyed by those who advocate for the use of data to enhance instruction is an awareness that learning analytics has the potential to improve instruction and learning but is not currently reaching that potential. Gibbons (2014) suggested that a lack of learning facilitated by current technology-enabled instructional systems may be due in part to the natural tendency of many designers to focus on the surface layers of instruction (i.e., the content and control layers), while failing to adequately design the internal (less visible) aspects (e.g., the data management layer). In this paper, we outline phases in the design process related to the data management layer that should be considered when integrating learning analytics into a technology-enabled learning system.

Vorheriger Artikel TechTrends 2010–2015: A Content Analysis

Nächster Artikel The Effects of Game Design Experience on Teachers’ Attitudes and Perceptions regarding the Use of Digital Games in the Classroom

Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (2nd ed., pp. 253–274). Cambridge, UK: Cambridge University Press. Retrieved from http://www.columbia.edu/~rsb2162/BakerSiemensHandbook2013.pdf

Baker, R. S. J. D., & Yacef, K. (2009). The state of educational data mining in 2009: a review and future visions. Journal of Educational Data Mining, 1(1), 3–16.

Behrens, J., & DiCerbo, K. (2013). Harnessing the currents of the digital ocean. In J. A. Larusson & B. White (Eds.), Learning analytics: from research to practice (pp. 38–60). New York: Springer.

Behrens, J., & DiCerbo, K. (2014). Harnessing the currents of the digital ocean. In J. A. Larusson & B. White (Eds.), Learning analytics: from research to practice (pp. 39–60). New York: Springer.

Brooks, C., Greer, J., & Gutwin, C. (2013). The data-assisted approach to building intelligent technology-enhanced learning environments. In J. A. Larusson & B. White (Eds.), Learning analytics: from research to practice (pp. 123–156). New York: Springer.

Buckingham Shum, S. (2012). Learning analytics. UNESCO policy brief. Retrieved from http://iite.unesco.org/pics/publications/en/files/3214711.pdf

Campbell, J. P., & Oblinger, D. G. (2007). Academic analytics. Educause. Retrieved from https://net.educause.edu/ir/library/pdf/PUB6101.pdf

Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: a new tool for a new era. EDUCAUSE Review, 42(4), 40–42.

Chung, G. K. W. K. (2014). Toward the relational management of educational measurement data. Teachers College Record, 116(11). Retrieved from http://www.tcrecord.org/Content.asp?ContentId=17650

Chung, G. K., & Kerr, D. S. (2012). A primer on data logging to support extraction of meaningful information from educational games: an example from save patch. (CRESST Report 814). National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 27. Retrieved from http://files.eric.ed.gov/fulltext/ED531284.pdf

Cizek, G. J. (2010). An introduction to formative assessment: history, characteristics, and challenges. In G. J. Cizek & H. L. Andrade (Eds.), Handbook of formative assessment (pp. 3–17). New York: Routledge.

Cuban, L., Kirkpatrick, H., & Peck, C. (2001). High access and low use of technologies in high school classrooms: explaining an apparent paradox. American Educational Research Journal, 38(4), 813–834.CrossRef

Davies, R., & West, R. E. (2014). Technology integration in school settings. In M. Spector, M. J. Bishop, M. D. Merrill, & J. Elen (Eds.), Handbook of research on educational communications and technology (4th ed., pp. 841–853). New York: Lawrence Erlbaum.CrossRef

DiCerbo, K., & Behrens, J. (2012). Implications of the digital ocean on current and future assessment. In R. Lissitz & H. Jiao (Eds.), Computers and their impact on state assessment: recent history and predictions for the future (pp. 273–306). Charlotte: Information Age.

Dron, J., & Anderson, T. (2009). On the design of collective applications. Proceedings of the 2009 International Conference on Computational Science and Engineering, 4, 368–374.

Dron, J., & Anderson, T. (2011). Three generations of distance education pedagogy. International Review of Research in Open and Distance Learning, 12(3), 80–97.

ElAtia, S., Ipperciel, D., & Hammad, A. (2012). Implications and challenges to using data mining in educational research in the Canadian context. Canadian Journal of Education, 35(2), 101–119.

Elias, T. (2011) Learning analytics : definitions, processes and potential. Learning. Retrieved from http://learninganalytics.net/LearningAnalyticsDefinitionsProcessesPotential.pdf

Fairweather, P. G., & Gibbons, A. S. (2000). Distributed learning: two steps forward, one back? One forward, two back? IEEE Concurrency, 8(2), 8–9,79.

Frech, A., & Damaske, S. (2012). Learning analytics. Journal of Health and Social Behavior, 53(4), 395. doi:10.1177/0022146512469014.CrossRef

Gibbons, A. S. (2014). An architectural approach to instructional design. New York: Routledge.

Gibbons, A. S., Merrill, P. F., Swan, R., Campbell, J. O., Christensen, E., Insalaco, M., et al. (2008). Reexamining the implied role of the designer. The Quarterly Review of Distance Education, 9(2), 127–137.

Graesser, A. C., Conley, M. W., & Olney, A. M. (2012). Intelligent tutoring systems. In K. R. Harris, S. Graham, & T. Urdan (Eds.), The APA educational psychology handbook (pp. 451–473). Washington, DC: American Psychological Association. doi:10.1037/13275-018.

Greller, W., & Drachsler, H. (2012). Translating learning into numbers: a generic framework for learning analytics. Journal of Educational Technology & Society, 15(3), 42–57.

Hendricks, M., Plantz, M. C., & Pritchard, K. J. (2008). Measuring outcomes of United Way funded programs: expectations and reality. In J. G. Carman & K. A. Fredricks (Eds.), Nonprofits and evaluation. New Directions for Evaluation, 119, 13–35.

Keefe, J. (2007). What is personalization? Phi Delta Kappan, 89(3), 217–223.CrossRef

Kleiman, G. M. (2004). Myths and realities about technology in k-12 schools: Five years later. Contemporary Issues in Technology and Teacher Education [Online serial], 4(2). Retrieved from http://www.citejournal.org/vol4/iss2/seminal/article2.cfm

Koskas, E. O. (2004). U.S. Patent No. 6,711,563. Washington, DC: U.S. Patent and Trademark Office.

Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: a proof of concept. Computers & Education, 54, 588–599. doi:10.1016/j.compedu.2009.09.008.CrossRef

Marzano, R. J. (2009). Formative versus summative assessments as measures of student learning. In T. J. Kowalski & T. J. Lasley (Eds.), Handbook of data-based decision making in education (pp. 261–271). New York: Routledge.

Mayer, M. (2009). Innovation at Google: The physics of data. PARC Forum. Retrieved from http://www.parc.com/event/936/innovation-atgoogle.html

Meyer, J. H. F., Land, R., & Davies, P. (2006). Implications of threshold concepts for course design and evaluation. In J. H. G. Meyer & R. Land (Eds.), Overcoming barriers to student understanding: threshold concepts and troublesome knowledge. New York: Routledge.

Nicholson, P. (2007). A history of e-learning: echoes of the pioneers. In B. Fernández Manjón (Ed.), Computers and education: e-Learning, from theory to practice (pp. 1–11). Dordrecht: Springer.CrossRef

Oblinger, D. G. (2012). Let’s talk analytics. EDUCAUSE Review, 47(4), 10–13.

Papamitsiou, Z., & Economides, A. (2014). Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. Educational Technology and Society, 17(4), 49–64.

Pardoe, A. (2013). Designing learning analytics experiences. In J. A. Larusson & B. White (Eds.), Learning analytics: from research to practice (pp. 15–38). New York: Springer.

Picciano, A. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Networks, 16(3), 9–20.

Pistilli, M., Willis, J., & Campbell, J. (2013). Analytics through an institutional lens: definition, theory, design, and impact. In J. A. Larusson & B. White (Eds.), Learning analytics: from research to practice (pp. 77–102). New York: Springer.

Reigeluth, C. M. (1999). What is instructional-design theory and how is it changing? In instructional-design theories and models: A new paradigm of instructional theory (pp. 5–30). Mahwah, NJ: Lawrence Erlbaum Associates.

Robson, R. (2013). The changing nature of e-learning content. In R. H. Kinshuk & J. M. Spector (Eds.), Reshaping learning: frontiers of learning technology in a global context (pp. 177–196). Berlin Heidelberg: Springer.CrossRef

Robson, R., & McElroy P. (2008) Transforming to next generation digital learning content. Retrieved from http://www.adobe.com/resources/E-learning/pdfs/95010540_digitallearning_wp_ue.pdf

Salkind, N. J. (2010). Encyclopedia of research design. Thousand Oaks: SAGE Publications.CrossRef

Siemens, G. (2011). Learning and academic analytics. Retrieved from http://www.learninganalytics.net/?p=131

Skinner, B. F. (1968). The technology of teaching. New York: Appleton-CenturyCrofts.

Thille, C., Schneider, E., Kizilcec, R. F., Piech, C., Halawa, S. A., & Greene, D. K. (2014). The future of data-enriched assessment. Research & Practice in Assessment. Special Issues: Big Data & Learning Analytics, 9, 5–16.

Tyler, R. W. (1949). Basic principles of curriculum and instruction. Chicago: The University of Chicago Press.

U.S. Department of Education, Office of Educational Technology. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An Issue Brief. Washington, D.C., 2012.

Watters, A. (2012). Learning Analytics: Lots of Education Data... Now What? Paper presented at LAK12. Retrieved from http://hackeducation.com/2012/05/04/learning-analytics-lak12

White, B., & Larusson, J. (2013). Identifying points for pedagogical intervention based on student writing: two case studies for the “point of originality.”. In J. A. Larusson & B. White (Eds.), Learning analytics: from research to practice (pp. 157–190). New York: Springer.

Woolf, B. P. (2010). A roadmap for education technology. Retrieved from http://www.cra.org/ccc/docs/groe/GROE%20Roadmap%20for%20Education%20Technology%20Final%20Report.pdf

Titel: Designing Technology-Enabled Instruction to Utilize Learning Analytics
verfasst von: Randall Davies
Robert Nyland
Robert Bodily
John Chapman
Brian Jones
Jay Young
Publikationsdatum: 14.12.2016
Verlag: Springer US
Erschienen in: TechTrends / Ausgabe 2/2017
Print ISSN: 8756-3894
Elektronische ISSN: 1559-7075
DOI: https://doi.org/10.1007/s11528-016-0131-7

Springer Professional

Designing Technology-Enabled Instruction to Utilize Learning Analytics

Abstract

Premium Partner

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Weitere Artikel der Ausgabe 2/2017

Interactive Storytelling: Opportunities for Online Course Design

The Effects of Game Design Experience on Teachers’ Attitudes and Perceptions regarding the Use of Digital Games in the Classroom

Uncreativity: a Discussion on Working Creativity Before and After Ideation with Dr. Chris Bilton

Gender Differences in Teens’ Digital Propensity and Perceptions and Preferences With Regard to Digital and Printed Text

Graduate Student Reflections on Participating in the AECT Design and Development Competition

An Alternative Approach: Openness in Education Over the Last 100 Years

Premium Partner