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Making Use of Data for Assessments: Harnessing Analytics and Data Science

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Second Handbook of Information Technology in Primary and Secondary Education

Abstract

The increased availability of vast and highly varied amounts of data from learners, teachers, learning environments, and administrative systems within educational settings is overwhelming. The focus of this chapter is on how data with a large number of records, of widely differing datatypes, and arriving rapidly from multiple sources can be harnessed for meaningful assessments and supporting learners in a wide variety of learning situations. Distinct features of analytics-driven assessments may include self-assessments, peer assessments, and semantic rich and personalized feedback as well as adaptive prompts for reflection. The chapter concludes with future directions in the broad area of analytics-driven assessments for teachers and educational researchers.

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References

  • Almond, R. G., Steinberg, L. S., & Mislevy, R. J. (2002). Enhancing the design and delivery of assessment systems: A four process architecture. Journal of Technology, Learning, and Assessment, 1(5), 3–63.

    Google Scholar 

  • Baker, R. S. J. d., & Siemens, G. (2015). Educational data mining and learning analytics. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 253–272).

    Google Scholar 

  • Berland, M., Baker, R. S. J. d., & Bilkstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1–2), 205–220. https://doi.org/10.1007/s10758-014-9223-7.

    Article  Google Scholar 

  • Black, P. J. (1998). Testing: Friend or foe? The theory and practice of assessment and testing. London: Falmer Press.

    Google Scholar 

  • Bloom, B. S., Hastings, J. T., & Madaus, G. F. (1971). Handbook of formative and summative evaluation of student learning. New York: McGraw-Hill.

    Google Scholar 

  • Boud, D. (2000). Sustainable assessment: Rethinking assessment for the learning society. Studies in Continuing Education, 22(2), 151–167. https://doi.org/10.1080/713695728.

    Article  Google Scholar 

  • Buckingham Shum, S., & Ferguson, R. (2012). Social learning analytics. Educational Technology & Society, 15(3), 3–26.

    Google Scholar 

  • Carless, D. (2007). Learning-oriented assessment: Conceptual bases and practical implications. Innovations in Education and Teaching International, 44(1), 57–66.

    Article  Google Scholar 

  • Cleophas, T. J., & Zwinderman, A. H. (2013). Machine learning in medicine. Amsterdam: Springer.

    Book  Google Scholar 

  • Coronges, K. A., Stacy, A. W., & Valente, T. W. (2007). Structural comparison of cognitive associative networks in two populations. Journal of Applied Social Psychology, 37(9), 2097–2129. https://doi.org/10.1111/j.1559-1816.2007.00253.x.

  • d’Aquin, M., Dietze, S., Herder, E., Drachsler, H., & Taibi, D. (2014). Using linked data in learning analytics. eLearning Papers, 36, 1–9.

    Google Scholar 

  • Drachsler, H., Hummel, H., & Koper, R. (2008). Personal recommender systems for learners in lifelong learning: Requirements, techniques, and model. International Journal of Learning Technologies, 3(4), 404–423.

    Article  Google Scholar 

  • Ellis, C. (2013). Broadening the scope and increasing usefulness of learning analytics: The case for assessment analytics. British Journal of Educational Technology, 44(4), 662–664. https://doi.org/10.1111/bjet.12028.

    Article  Google Scholar 

  • Ge, X., & Ifenthaler, D. (2017). Designing engaging educational games and assessing engagement in game-based learning. In R. Zheng & M. K. Gardner (Eds.), Handbook of research on serious games for educational applications (pp. 255–272). Hershey: IGI Global.

    Google Scholar 

  • Gibson, D. C., & Ifenthaler, D. (2017). Preparing the next generation of education researchers for big data in higher education. In B. Kei Daniel (Ed.), Big data and learning analytics: Current theory and practice in higher education (pp. 29–42). New York: Springer.

    Chapter  Google Scholar 

  • Gibson, D. C., & Webb, M. (2015). Data science in educational assessment. Education and Information Technologies, 20(4), 697–713. https://doi.org/10.1007/s10639-015-9411-7.

    Article  Google Scholar 

  • Gibson, D. C., Ifenthaler, D., & Orlic, D. (2016). Open assessment resources for deeper learning. In P. Blessinger & T. J. Bliss (Eds.), Open education: International perspectives in higher education (pp. 257–279). Cambridge, UK: Open Book Publishers.

    Google Scholar 

  • Gierl, M. J. (2007). Making diagnostic inferences about cognitive attributes using the rule-space model and attribute hierarchy method. Journal of Educational Measurement, 44(4), 325–340. https://doi.org/10.1111/j.1745-3984.2007.00042.x.

    Article  Google Scholar 

  • Goldhammer, F., Naumann, J., Stelter, A., Toth, K., Rölke, H., & Klieme, E. (2014). The time on task effect in reading and problem solving is moderated by task difficulty and skill. Insights from a computer-based large-scale assessment. Journal of Educational Psychology, 106, 608–626.

    Article  Google Scholar 

  • Greiff, S., Niepel, C., Scherer, R., & Martin, R. (2016). Understanding students’ performance in a computer-based assessment of complex problem solving: An analysis of behavioral data from computer-generated log files. Computers in Human Behavior, 61, 36–46. https://doi.org/10.1016/j.chb.2016.02.095.

    Article  Google Scholar 

  • Greiff, S., Molnar, G., Martin, R., Zimmermann, J., & Csapo, B. (submitted). Students’ exploration strategies in complex problem environments. A latent class approach. Journal of Educational Psychology.

    Google Scholar 

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

    Google Scholar 

  • Ifenthaler, D. (2009). Model-based feedback for improving expertise and expert performance. Technology, Instruction, Cognition and Learning, 7(2), 83–101.

    Google Scholar 

  • Ifenthaler, D. (2012). Determining the effectiveness of prompts for self-regulated learning in problem-solving scenarios. Journal of Educational Technology & Society, 15(1), 38–52.

    Google Scholar 

  • Ifenthaler, D. (2015). Learning analytics. In J. M. Spector (Ed.), The SAGE encyclopedia of educational technology (Vol. 2, pp. 447–451). Thousand Oaks: Sage.

    Google Scholar 

  • Ifenthaler, D. (2016). Automated grading. In S. Danver (Ed.), The SAGE encyclopedia of online education (p. 130). Thousand Oaks: Sage.

    Google Scholar 

  • Ifenthaler, D. (2017). Are higher education institutions prepared for learning analytics? TechTrends, 61(4), 366–371. https://doi.org/10.1007/s11528-016-0154-0.

    Article  Google Scholar 

  • Ifenthaler, D., & Dikli, S. (2015). Automated scoring of essays. In J. M. Spector (Ed.), The SAGE encyclopedia of educational technology (Vol. 1, pp. 64–68). Thousand Oaks: Sage.

    Google Scholar 

  • Ifenthaler, D., & Seel, N. M. (2013). Model-based reasoning. Computers & Education, 64, 131–142. https://doi.org/10.1016/j.compedu.2012.11.014.

  • Ifenthaler, D., & Pirnay-Dummer, P. (2014). Model-based tools for knowledge assessment. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed., pp. 289–301). New York: Springer.

    Chapter  Google Scholar 

  • Ifenthaler, D., & Seel, N. M. (2005). The measurement of change: Learning-dependent progression of mental models. Technology, Instruction, Cognition and Learning, 2(4), 317–336.

    Google Scholar 

  • Ifenthaler, D., & Widanapathirana, C. (2014). Development and validation of a learning analytics framework: Two case studies using support vector machines. Technology, Knowledge and Learning, 19(1–2), 221–240. https://doi.org/10.1007/s10758-014-9226-4.

    Article  Google Scholar 

  • Ifenthaler, D., Pirnay-Dummer, P., & Seel, N. M. (Eds.). (2010). Computer-based diagnostics and systematic analysis of knowledge. New York: Springer.

    Google Scholar 

  • Ifenthaler, D., Eseryel, D., & Ge, X. (2012). Assessment for game-based learning. In D. Ifenthaler, D. Eseryel, & X. Ge (Eds.), Assessment in game-based learning. Foundations, innovations, and perspectives (pp. 3–10). New York: Springer.

    Chapter  Google Scholar 

  • Klosgen, W., & Zytkow, J. (2002). Handbook of data mining and knowledge discovery. New York: Oxford University Press.

    Google Scholar 

  • Lehmann, T., Haehnlein, I., & Ifenthaler, D. (2014). Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning. Computers in Human Behavior, 32, 313–323. https://doi.org/10.1016/j.chb.2013.07.051

  • Loh, C. S., Sheng, Y., & Ifenthaler, D. (2015). Serious games analytics: Theoretical framework. In C. S. Loh, Y. Sheng, & D. Ifenthaler (Eds.), Serious games analytics. Methodologies for performance measurement, assessment, and improvement (pp. 3–29). New York: Springer.

    Google Scholar 

  • Long, P. D., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, 46(5), 31–40.

    Google Scholar 

  • Mertler, C. A. (2009). Teachers’ assessment knowledge and their perceptions of the impact of classroom assessment professional development. Improving Schools, 12(2), 101–113. https://doi.org/10.1177/1365480209105575.

    Article  Google Scholar 

  • Narciss, S. (2008). Feedback strategies for interactive learning tasks. In J. M. Spector, M. D. Merrill, J. J. G. van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (pp. 125–143). New York: Taylor & Francis Group.

    Google Scholar 

  • Newton, P. E. (2007). Clarifying the purposes of educational assessment. Assessment in Education: Principles, Policy & Practice, 14(2), 149–170. https://doi.org/10.1080/09695940701478321.

    Article  Google Scholar 

  • OECD. (2013a). OECD skills outlook 2013: First results from the survey of adult skills. Paris: OECD Publishing.

    Google Scholar 

  • OECD. (2013b). PISA 2012 assessment and analytical framework. Paris: OECD Publishing.

    Book  Google Scholar 

  • OECD. (2014). PISA 2012 results: Creative problem solving. Paris: OECD Publishing.

    Book  Google Scholar 

  • Pellegrino, J. W., Chudowsky, N., & Glaser, R. (Eds.). (2001). Knowing what students know: The science and design of educational assessment. Washington, DC: National Academy Press.

    Google Scholar 

  • Piaget, J. (1950). La construction du réel chez l’enfant. Neuchatel: Delachaux et Niestlé S.A.

    Google Scholar 

  • Pirnay-Dummer, P., & Ifenthaler, D. (2011). Reading guided by automated graphical representations: How model-based text visualizations facilitate learning in reading comprehension tasks. Instructional Science, 39(6), 901–919. https://doi.org/10.1007/s11251-010-9153-2.

  • Plake, B. S. (1993). Teacher assessment literacy: Teachers’ competencies in the educational assessment of students. Mid-Western Educational Researcher, 6(2), 21–27.

    Google Scholar 

  • Romero, C., & Ventura, S. (2015). Learning analytics: From research to practice. In J. A. Larusson & B. White (Eds.), Technology, knowledge and learning. https://doi.org/10.1007/s10758-015-9244-x.

    Google Scholar 

  • Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18, 119–144.

    Article  Google Scholar 

  • Sadler, D. R. (2010). Beyond feedback: Developing student capability in complex appraisal. Assessment & Evaluation in Higher Education, 35(5), 535–550.

    Article  Google Scholar 

  • Scriven, M. (1967). The methodology of evaluation. Washington, DC: American Educational Research Association.

    Google Scholar 

  • Shermis, M. D., & Hamner, B. (2013). Contrasting state-of-the-art automated scoring of essays. In M. D. Shermis & J. Burstein (Eds.), Handbook of automated essay evaluation (pp. 213–246). New York: Routledge.

    Google Scholar 

  • Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189.

    Article  Google Scholar 

  • Shute, V. J. (2011). Stealth assessment in computer-based games to support learning. In S. Tobias & J. D. Fletcher (Eds.), Computer games and instruction (pp. 503–524). Charlotte: Information Age Publishers.

    Google Scholar 

  • Shute, V. J., Wang, L., Greiff, S., Zhao, W., & Moore, G. (2016). Measuring problem solving skills via stealth assessment in an engaging video game. Computers in Human Behavior, 63, 106–117. https://doi.org/10.1016/j.chb.2016.05.047.

    Article  Google Scholar 

  • Spector, J. M. (2009). Adventures and advances in instructional design theory and practice. In L. Moller, J. B. Huett, & D. M. Harvey (Eds.), Learning and instructional technologies for the 21st century (pp. 1–14). New York: Springer.

    Google Scholar 

  • Spector, J. M., Ifenthaler, D., Sampson, D. G., Yang, L., Mukama, E., Warusavitarana, A.,⋯ Gibson, D. C. (2016). Technology enhanced formative assessment for 21st century learning. Educational Technology & Society, 19(3), 58–71.

    Google Scholar 

  • Stiggins, R. J. (1995). Assessment literacy for the 21st century. Phi Delta Kappan, 77(3), 238–245.

    Google Scholar 

  • Tatsuoka, K. (2009). Cognitive assessment: An introduction to the rule-space method. New York: Routledge.

    Google Scholar 

  • Wagner, W., & Wagner, S. U. (1985). Presenting questions, processing responses, and providing feedback in CAI. Journal of Instructional Development, 8(4), 2–8.

    Article  Google Scholar 

  • Wiliam, D. (2011). What is assessment for learning? Studies in Educational Evaluation, 37(1), 3–14.

    Article  Google Scholar 

  • Wüstenberg, S., Greiff, S., Molnar, G., & Funke, J. (2014). Cross-national gender differences in complex problem solving and their determinants. Learning and Individual Differences, 29, 18–29. https://doi.org/10.1016/j.lindif.2013.10.006.

    Article  Google Scholar 

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Correspondence to Dirk Ifenthaler , Samuel Greiff or David Gibson .

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Ifenthaler, D., Greiff, S., Gibson, D. (2018). Making Use of Data for Assessments: Harnessing Analytics and Data Science. In: Voogt, J., Knezek, G., Christensen, R., Lai, KW. (eds) Second Handbook of Information Technology in Primary and Secondary Education . Springer International Handbooks of Education. Springer, Cham. https://doi.org/10.1007/978-3-319-53803-7_41-1

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  • DOI: https://doi.org/10.1007/978-3-319-53803-7_41-1

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