Advertisement

Unraveling the Research on Deeper Learning: A Review of the Literature

  • Stylianos Sergis
  • Demetrios Sampson
Chapter

Abstract

Deeper learning (DL) has emerged at the spotlight of educational policies around the world and has gained significant attention from various stakeholders in education (teachers, school leaders, curricula designers, policy makers). This is the result of DL being associated to core competences of the current and future workplaces such as problem-solving, critical thinking, self-regulated learning, and effective collaboration, which are considered as essential for building innovative solutions to wicked global challenges. However, despite this well-acknowledged trend research related to modeling, cultivating and assessing Deeper Learning competences is still at a shaping stage. This is also reflected in the rather limited advancements in the use of digital educational technologies to support the assessment and measurement of DL. In this context, the contribution of this chapter is to perform a systematic literature review of the current state on existing works for modeling DL competences, teaching approaches applied to cultivate them as well as, methods and instruments proposed for assessing and measuring DL.

Keywords

Deeper learning Modeling deeper learning Deeper learning competences Assessment of deeper learning Measuring deeper learning Teaching strategies for deeper learning 

Notes

Acknowledgement

The work presented in this paper has been partially funded by (a) the European Commission in the context of the “STORIES—Stories of Tomorrow: Students Visions on the Future of Space Exploration” project (Grant Agreement no. 731872) under Horizon 2020 Program, H2020-ICT-22-2016-2017 “Information and Communication Technologies: Technologies for Learning and Skills” and (b) the Greek General Secretariat for Research and Technology, under the Matching Funds 2014–2016 for the EU project “Inspiring Science: Large Scale Experimentation Scenarios to Mainstream eLearning in Science, Mathematics and Technology in Primary and Secondary Schools” (Project Number: 325123). This document does not represent the opinion of neither the European Commission nor the Greek General Secretariat for Research and Technology, and the European Commission and the Greek General Secretariat for Research and Technology are not responsible for any use that might be made of its content.

References

  1. Abbott, I., Townsend, A., Johnston-Wilder, S., & Reynolds, L. (2009). Literature review: Deep learning with technology in 14-to 19-year-old learners. Coventry: Institute of Education.Google Scholar
  2. Abraham, R. R., Vinod, P., Kamath, M. G., Asha, K., & Ramnarayan, K. (2008). Learning approaches of undergraduate medical students to physiology in a non-PBL-and partially PBL-oriented curriculum. Advances in Physiology Education, 32(1), 35–37.CrossRefGoogle Scholar
  3. Adiga, S., & Adiga, U. (2010). Problem based learning-An approach to learning pharmacology in medical school. Biomedical Research, 21(1), 43–46.Google Scholar
  4. Agarwal, P. K., McDaniel, M. A., Thomas, R. C., McDermott, K. B., & Roediger, H. L., III. (2011). Quizzing promotes deeper acquisition in middle school science: Transfer of quizzed content to summative exams. Washington, DC: Society for Research on Educational Effectiveness.Google Scholar
  5. Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542–550.CrossRefGoogle Scholar
  6. Akyol, Z., & Garrison, D. R. (2011). Understanding cognitive presence in an online and blended community of inquiry: Assessing outcomes and processes for deep approaches to learning. British Journal of Educational Technology, 42(2), 233–250.CrossRefGoogle Scholar
  7. Altamirano, N., & Jaurez, J. (2013). Student built games in economic courses: Applying the game design methodology as another approach to deeper learning. Journal of Research in Innovative Teaching, 6(1), 115–131.Google Scholar
  8. Antonenko, P. D., Jahanzad, F., & Greenwood, C. (2014). Fostering collaborative problem solving and 21st century skills using the DEEPER scaffolding framework. Journal of College Science Teaching, 43(6), 79–88.Google Scholar
  9. Arbaugh, J. B., Cleveland-Innes, M., Diaz, S. R., Garrison, D. R., Ice, P., Richardson, J., & Swan, K. P. (2008). Developing a community of inquiry instrument: Testing a measure of the Community of Inquiry framework using a multi-institutional sample. The Internet and Higher Education, 11(3–4), 133–136.CrossRefGoogle Scholar
  10. Asikainen, H., & Gijbels, D. (2017). Do students develop towards more deep approaches to learning during studies? A systematic review on the development of students’ deep and surface approaches to learning in higher education. Educational Psychology Review, 29, 205–234.CrossRefGoogle Scholar
  11. Association of American Colleges and Universities. (2009). Assessing learning outcomes: Lessons from AAC&U’s VALUE project. Washington, DC: Association of American Colleges and Universities.Google Scholar
  12. Belk, M., Papatheocharous, E., Germanakos, P., & Samaras, G. (2013). Modeling users on the world wide web based on cognitive factors, navigation behavior and clustering techniques. Journal of Systems and Software, 86(12), 2995–3012.CrossRefGoogle Scholar
  13. Biggs, J., Kember, D., & Leung, Y. (2001). The revised two-factor study process questionnaire: R-SPQ-2F. British Journal of Educational Psychology, 71, 133–149.CrossRefGoogle Scholar
  14. Biggs, J. B. (1987). Students approaches to learning and studying. Hawthorn, VIC: Australian Council for Educational Research.Google Scholar
  15. Bishay, P. L. (2016). “FEApps”: Boosting students’ enthusiasm for coding and app designing, with a deeper learning experience in engineering fundamentals. Computer Applications in Engineering Education, 24(3), 456–463.CrossRefGoogle Scholar
  16. Bouwmeester, R. A., De Kleijn, R. A., & Van Rijen, H. V. (2016). Peer-instructed seminar attendance is associated with improved preparation, deeper learning and higher exam scores: A survey study. BMC Medical Education, 16(1), 200.CrossRefGoogle Scholar
  17. Cacioppo, J., Petty, R., Feinstein, J., & Jarvis, W. (1996). Dispositional differences in cognitive motivation: The life and times of individuals varying in need for cognition. Psychological Bulletin, 119, 197–253.CrossRefGoogle Scholar
  18. Clark, M., McKague, M., Ramsden, V. R., & McKay, S. (2015). Deeper learning through service: Evaluation of an interprofessional community service-learning program for pharmacy and medicine students. Journal of Research in Interprofessional Practice and Education, 5(1), 1–25.CrossRefGoogle Scholar
  19. Conley, D. (2014). A new era for educational assessment. Students at the Center: Deeper learning research series. Boston, MA: Jobs for the Future.Google Scholar
  20. Conley, D., & Darling-Hammond, L. (2013). Creating systems of assessment for deeper learning. Stanford, CA: Stanford Center for Opportunity Policy in Education.Google Scholar
  21. D’mello, S. K., & Kory, J. (2015). A review and meta-analysis of multimodal affect detection systems. ACM Computing Surveys, 47(3), 43-1–43-36.Google Scholar
  22. Dede, C. (2014). The role of digital technologies in deeper learning. Students at the Center: Deeper learning research series. Boston, MA: Jobs for the Future.Google Scholar
  23. Deloitte. (2016). The new organization: Different by design. New York, NY: Deloitte University.Google Scholar
  24. Digital Learning Compass (2017). Distance education enrollment report 2017. Retrieved from https://goo.gl/dsw2GPGoogle Scholar
  25. Dolmans, D. H., Loyens, S. M., Marcq, H., & Gijbels, D. (2016). Deep and surface learning in problem-based learning: A review of the literature. Advances in Health Sciences Education, 21(5), 1087–1112.CrossRefGoogle Scholar
  26. Entwistle NJ. (2001). ASSIST. Retrieved from http://www.etl.tla.ed.ac.uk/questionnaires/ASSIST.pdfGoogle Scholar
  27. Erkens, G., & Janssen, J. (2008). Automatic coding of online collaboration protocols. International Journal of Computer-Supported Collaborative Learning, 3, 447–470.CrossRefGoogle Scholar
  28. Esparza, D. R. (2016). A first-year implementation of Mindquest21, A project-based paradigm shift to deeper learning: A program evaluation. PhD Thesis.Google Scholar
  29. Fullan, M., Hill, P., Rincon-Gallardo, S. (2017). Deep learning: Shaking the foundations.. Deep learning series, 3. New Pedagogies for Deep Learning: A Global PartnershipGoogle Scholar
  30. Fullan, M., McEahen, J., & Quinn, J. (2016). New pedagogies for deep learning. New Pedagogies for Deeper Learning global report.Google Scholar
  31. Getting Smart (2014). Assessing deeper learning: A survey of performance assessment and mastery-tracking tools.Google Scholar
  32. Grant, A., Kinnersley, P., & Field, M. (2012). Learning contexts at Two UK medical schools: A comparative study using mixed methods. BMC Research Notes, 5(1), 153.CrossRefGoogle Scholar
  33. Greenhill, V., & Martin, J. (2014). OECD test for schools: Implementation toolkit. Menlo Park, CA: EdLeader21 and Hewlett-Flora Foundation. Retrieved from http://www.oecd.org/pisa/aboutpisa/EdLeader21_OECD_TFS_Toolkit.pdfGoogle Scholar
  34. Grover, S., & Pea, R. (2016). Designing a blended, middle school computer science course for deeper learning: A design-based research approach. Singapore: International Society of the Learning Sciences.Google Scholar
  35. Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199–237.CrossRefGoogle Scholar
  36. Gurpinar, E., Kulac, E., Tetik, C., Akdogan, I., & Mamakli, S. (2013). Do learning approaches of medical students affect their satisfaction with problem-based learning? Advances in Physiology Education, 37(1), 85–88.CrossRefGoogle Scholar
  37. Hatala, M., Beheshitha, S. S., & Gasevic, D. (2016). Associations between students’ approaches to learning and learning analytics visualizations. In Proceedings of the Learning Analytics & Knowledge Conference (pp. 3–10). New York, NY: ACM.Google Scholar
  38. Heller, R., & Wolfe, R. (2015). Effective schools for deeper learning: An exploratory study. Students at the Center: Deeper learning research series. Boston, MA: Jobs for the Future.Google Scholar
  39. Herman, J., la Torre, D., Epstein, S., & Wang, J. (2016). Benchmarks for deeper learning on next generation tests: A study of PISA. National Center for Research. Los Angeles, CA: University of California.Google Scholar
  40. Herman, J., La Torre Matrundola, D., & Wang, J. (2015). On the road to assessing deeper learning: What direction do test blueprints provide? National Center for Research. Los Angeles, CA: University of California.Google Scholar
  41. Herman, J., & Linn, R. (2013). On the road to assessing deeper learning: The status of smarter balanced and PARCC assessment consortia. National Center for Research. Los Angeles, CA: University of California.Google Scholar
  42. Huberman, M., Bitter, C., Anthony, J., & O’Day, J. (2014). The shape of deeper learning: Strategies, structures, and cultures in deeper learning network high schools. Washington, DC: American Institute of Research.Google Scholar
  43. Jarvis, W., Sadeque, S., & O’Brien, I. M. (2016). An exploration of dis-confirmation of deeper learning expectations using choice theory. Procedia-Social and Behavioral Sciences, 228, 662–667.CrossRefGoogle Scholar
  44. Kirby, J. R., Cain, K., & White, B. (2012). Deeper learning in reading comprehension. In Enhancing the quality of learning: Dispositions, instruction, and learning processes (pp. 315–338). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  45. Laird, T. F., Seifert, T. A., Pascarella, E. T., Mayhew, M. J., & Blaich, C. F. (2011). Deeply effecting first-year students’ thinking: The effects of deep approaches to learning on three outcomes. In Proceedings of the Annual Conference of the Association for the Study of Higher Education.Google Scholar
  46. Laird, T. F. N., Shoup, R., Kuh, G. D., & Schwarz, M. J. (2008). The effects of discipline on deep approaches to student learning and college outcomes. Research in Higher Education, 49(6), 469–494.CrossRefGoogle Scholar
  47. Lathram, B., Lenz, B., & Ark, T.V. (2016) Preparing students for a project-based world. Getting Smart/Buck Institute for Education.Google Scholar
  48. Lloyd, J. E. (2014). Inverting a non-major’s biology class: Using video lectures, online resources, and a student response system to facilitate deeper learning. Journal of Teaching and Learning with Technology, 3(2), 31–39.CrossRefGoogle Scholar
  49. McParland, M., Noble, L. M., & Livingston, G. (2004). The effectiveness of problem-based learning compared to traditional teaching in undergraduate psychiatry. Medical Education, 38(8), 859–867.Google Scholar
  50. Mehta, J., & Fine, S. (2015). The why, what, where, and how of deeper learning in American secondary schools. Students at the Center, Deeper learning research series. Boston, MA: Jobs for the Future.Google Scholar
  51. Mok, C. K., Dodd, B., & Whitehill, T. L. (2009). Speech-language pathology students’ approaches to learning in a problem-based learning curriculum. International Journal of Speech-Language Pathology, 11(6), 472–481.CrossRefGoogle Scholar
  52. Murrant, C. L., Dyck, D. J., Kirkland, J. B., Newton, G. S., Ritchie, K. L., Tishinsky, J. M., … Richardson, N. S. (2015). A large, first-year, introductory, multi-sectional biological concepts of health course designed to develop skills and enhance deeper learning. The Canadian Journal of Higher Education, 45(4), 42–62.Google Scholar
  53. Nijhuis, J. F., Segers, M. S., & Gijselaers, W. H. (2005). Influence of redesigning a learning environment on student perceptions and learning strategies. Learning Environments Research, 8(1), 67–93.Google Scholar
  54. OECD. (2012). PISA 2012 results: Creative problem solving students’ skills in tackling real-life problems. Paris: OECD. Retrieved from http://www.oecd.org/pisa/keyfindings/PISA-2012-results-volume-V.pdfGoogle Scholar
  55. OECD. (2017). The PISA 2015 collaborative problem-solving framework. Paris: OECD. Retrieved from https://www.oecd.org/pisa/pisaproducts/Draft%20PISA%202015%20Collaborative%20Problem%20Solving%20Framework%20.pdfCrossRefGoogle Scholar
  56. Offir, B., Lev, Y., & Bezalel, R. (2008). Surface and deep learning processes in distance education: Synchronous versus asynchronous systems. Computers & Education, 51(3), 1172–1183.CrossRefGoogle Scholar
  57. Online Learning Consortium. (2015). Online report card – Tracking online education in the United States. Newburyport, MA: Online Learning Consortium. Retrieved from http://onlinelearningconsortium.org/read/online-report-card-tracking-online-education-united-states-2015Google Scholar
  58. Pappas, I. O., Giannakos, M. N., Jaccheri, L., & Sampson, D. G. (2017). Assessing student behavior in computer science education with an fsQCA approach: The role of gains and barriers. ACM Transactions on Computing Education (TOCE), 17(2), 369.Google Scholar
  59. Pellegrino, J. W., & Hilton, M. L. (2012). Committee on defining deeper learning and 21st century skills. In Education for life and work: Developing transferable knowledge and skills in the 21st century. Washington, DC: The National Academies Press.Google Scholar
  60. Peltier, C., & Vannest, K. J. (2017). A meta-analysis of schema instruction on the problem-solving performance of elementary school students. Review of Educational Research, 87, 899.CrossRefGoogle Scholar
  61. Pirnay-Dummer, P., Ifenthaler, D., & Spector, J. M. (2010). Highly integrated model assessment technology and tools. Educational Technology Research & Development, 58(1), 3–18.CrossRefGoogle Scholar
  62. Quellmalz, E. S., Davenport, J. L., Timms, M. J., DeBoer, G. E., Jordan, K. A., Huang, C. W., & Buckley, B. C. (2013). Next-generation environments for assessing and promoting complex science learning. Journal of Educational Psychology, 105(4), 1100–1114.CrossRefGoogle Scholar
  63. Reid, W. A., Evans, P., & Duvall, E. (2012). Medical students’ approaches to learning over a full degree programme. Medical Education Online, 17(1), 17205.CrossRefGoogle Scholar
  64. Rufer, R., & Adams, R. H. (2013). Deep learning through reusable learning objects in an MBA program. Journal of Educational Technology Systems, 42(2), 107–120.CrossRefGoogle Scholar
  65. Selcuk, G. S. (2010). The effects of problem-based learning on pre-service teachers achievement, approaches and attitudes towards learning physics. International Journal of Physical Sciences, 5(6), 711–723.Google Scholar
  66. Sergis, S., & Sampson, D. (2016). Learning object recommendations for teachers based on elicited ICT competence profiles. IEEE Transactions on Learning Technologies, 9(1), 67–80.CrossRefGoogle Scholar
  67. Sergis, S., Sampson, D., & Giannakos, M. (2018). Supporting school leadership decision making with holistic school analytics: Bringing the qualitative-quantitative divide using fuzzy-set qualitative comparative analysis. Computers in Human Behavior, 89, 355.CrossRefGoogle Scholar
  68. Serife, A. K. (2011). The effect of computer supported problem based learning on students approaches to learning. Current Issues in Education, 14(1), 1–19.Google Scholar
  69. Spector, J. M., & Koszalka, T. A. (2004). The DEEP methodology for assessing learning in complex domains. (Final report to the National Science Foundation Evaluative Research and Evaluation Capacity Building). Syracuse, NY: Syracuse University.Google Scholar
  70. Stull, A. T., & Mayer, R. E. (2007). Learning by doing versus learning by viewing: Three experimental comparisons of learner-generated versus author-provided graphic organizers. Journal of Educational Psychology, 99(4), 808.CrossRefGoogle Scholar
  71. Surr, W., & Redding, S. (2017). Competency-based Education: Staying shallow or going deeper. Washington, DC: Great Lakes and Midwest Regional Deeper Learning Initiative, American Institutes for Research. Retrieved from https://ccrscenter.org/sites/default/files/CBE_GoingDeep.pdfGoogle Scholar
  72. The William and Flora Hewlett Foundation. (2012). Deeper learning strategic plan summary education program. Menlo Park, CA: The William and Flora Hewlett Foundation. Retrieved from https://hewlett.org/wp-content/uploads/2016/09/Education_Deeper_Learning_Strategy.pdfGoogle Scholar
  73. Tiwari, A., Chan, S., Wong, E., Wong, D., Chui, C., Wong, A., & Patil, N. (2006). The effect of problem-based learning on students’ approaches to learning in the context of clinical nursing education. Nurse Education Today, 26(5), 430–438.CrossRefGoogle Scholar
  74. Turvey, K. (2006). Towards deeper learning through creativity within online communities in primary education. Computers & Education, 46(3), 309–321.CrossRefGoogle Scholar
  75. Van der Ark, T., & Schneider, C. (2013). How digital learning contributes to deeper learning. Getting Smart.Google Scholar
  76. Van der Ark, T., & Schneider, C. (2014). Deeper learning for every student every day. Getting Smart.Google Scholar
  77. Vanthournout, G., Coertjens, L., Gijbels, D., Donche, V., & Van Petegem, P. (2013). Assessing students’ development in learning approaches according to initial learning profiles: A person-oriented perspective. Studies in Educational Evaluation, 39(1), 33–40.CrossRefGoogle Scholar
  78. Vanthournout, G., Doche, V., Gijbels, D., & Van Petegem, P. (2014). (Dis)similarities in research on learning approaches and learning patterns. In D. Gijbels, V. Doche, J. Richardson, & J. D. Vermunt (Eds.), Learning patterns in higher education: Dimensions and research perspectives (pp. 11–32). London: Routledge.Google Scholar
  79. Vos, N., Van Der Meijden, H., & Denessen, E. (2011). Effects of constructing versus playing an educational game on student motivation and deep learning strategy use. Computers & Education, 56(1), 127–137.CrossRefGoogle Scholar
  80. Vuchic, V. (2011). Deeper learning and e-learning: A review of promising programs and emerging technologies in the field of online teacher professional development. Menlo Park, CA: William & Flora Foundation.Google Scholar
  81. Wijnen, M., Loyens, S. M., Smeets, G., Kroeze, M., & van der Molen, H. (2016). Comparing problem-based learning students to students in a lecture-based curriculum: Learning strategies and the relation with self-study time. European Journal of Psychology of Education, 32, 431–447.CrossRefGoogle Scholar
  82. Wong, D. K. P., & Lam, D. O. B. (2007). Problem-based learning in social work: A study of student learning outcomes. Research on Social Work Practice, 17(1), 55–65.CrossRefGoogle Scholar
  83. Wu, C. H., Huang, Y. M., & Hwang, J. P. (2015). Review of affective computing in education/learning: Trends and challenges. British Journal of Educational Technology, 47, 1304–1323.CrossRefGoogle Scholar
  84. Yeager, D. S., Henderson, M. D., Paunesku, D., Walton, G. M., D’Mello, S., Spitzer, B. J., & Duckworth, A. L. (2014). Boring but important: A self-transcendent purpose for learning fosters academic self-regulation. Journal of Personality and Social Psychology, 107(4), 559.CrossRefGoogle Scholar
  85. Zervas, P., & Sampson, D. (2018). Supporting reflective lesson planning based on inquiry learning analytics for facilitating students’ problem solving competence: The inspiring science education tools. In R. Huang, N.-S. Chen, & Kinshuk (Eds.), Authentic learning through advances in technologies. Singapore: Springer.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Stylianos Sergis
    • 1
  • Demetrios Sampson
    • 1
    • 2
  1. 1.Department of Digital SystemsUniversity of PiraeusPiraeusGreece
  2. 2.School of EducationCurtin UniversityPerthAustralia

Personalised recommendations