Skip to main content
SpringerLink
Log in

The Role of Academic Engagement in Students’ Educational Development: Insights from Load Reduction Instruction and the 4M Academic Engagement Framework

  • Chapter
  • First Online:
Handbook of Research on Student Engagement

Abstract

School is difficult for many students. In part, this is because there are instructional burdens that impose a significant cognitive load on students as they try to learn. As cognitive load escalates, there is the risk of declining academic engagement which then reduces students’ learning and achievement. It is vital that teachers deliver instruction in a way that eases the cognitive load on students as they learn and supports students’ academic engagement, learning, and achievement. Load reduction instruction (LRI) is an instructional approach aimed at managing the cognitive demands experienced by students as they learn. This discussion explores how LRI can enhance students’ academic engagement and how these improvements in engagement assist academic outcomes such as achievement—thus, hypothesizing a mediating role for academic engagement. The discussion also introduces a novel engagement framework—the 4M Academic Engagement Framework—that hierarchically conceptualizes engagement in terms of students’ engagement with their broad educational development (the “mega” level) through to the granular operationalization of specific engagement variables (the “micro/measured” level). The theoretical and empirical connections between LRI and the academic engagement dimensions of the 4M Framework are then described, with a particular focus on how academic engagement mediates the link between LRI and achievement. Implications of these findings for engagement assessment and practice are discussed. Taken together, it is clear that academic engagement plays an important part in young people’s educational development and the instructional factors aimed at supporting that development.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Adams, G., & Engelmann, S. (1996). Research on direct instruction: 25 years beyond DISTAR. Educational Achievement Systems.

    Google Scholar 

  • Ainley, M. (2012). Students’ interest and engagement in classroom activities. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 283–302). Springer. https://doi.org/10.1007/978-1-4614-2018-7_13

    Chapter  Google Scholar 

  • Anderman, E. M. (2013). Middle school transitions. In J. Hattie & E. M. Anderman (Eds.), International guide to student achievement. Routledge.

    Google Scholar 

  • Anderman, E. M., & Mueller, C. E. (2010). Middle school transitions and adolescent development. In J. L. Meece & J. S. Eccles (Eds.), Handbook of research on schools, schooling, and human development. Routledge.

    Google Scholar 

  • Anderman, E. M., & Patrick, H. (2012). Achievement goal theory, conceptualization of ability/intelligence, and classroom climate. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 173–191). Springer. https://doi.org/10.1007/978-1-4614-2018-7_8

    Chapter  Google Scholar 

  • Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the student engagement instrument. Journal of School Psychology, 44, 427–445. https://doi.org/10.1016/j.jsp.2006.04.002

    Article  Google Scholar 

  • Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130, 224–237. https://doi.org/10.1037/0096-3445.130.2.224

    Article  Google Scholar 

  • Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of Educational Research, 70, 181–214. https://doi.org/10.3102/00346543070002181

    Article  Google Scholar 

  • Australian Academy of Science. (2020). Learning outcomes for online versus in-class education. Australian Academy of Science. https://www.science.org.au/covid19/learning-outcomes-online-vs-inclass-education

    Google Scholar 

  • Basso, D., & Belardinelli, M. O. (2006). The role of the feedforward paradigm in cognitive psychology. Cognitive Processing, 7, 73–88. https://doi.org/10.1007/s10339-006-0034-1

    Article  PubMed  Google Scholar 

  • Berg, J. L., & Wehby, J. (2013). Preteaching strategies to improve student learning in content area classes. Intervention in School and Clinic, 49, 14–20. https://doi.org/10.1177/1053451213480029

    Article  Google Scholar 

  • Bergdahl, N., Nouri, J., & Fors, U. (2020). Disengagement, engagement and digital skills in technology-enhanced learning. Education and Information Technologies, 25(2), 957–983.

    Article  Google Scholar 

  • Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: From the general to the applied. Journal of Computing in Higher Education, 26(1), 87–122.

    Article  Google Scholar 

  • Black, P., & Wiliam, D. (2004). The formative purpose: Assessment must first promote learning. Yearbook of the National Society for the Study of Education, 103, 20–50. https://doi.org/10.1111/j.1744-7984.2004.tb00047.x

    Article  Google Scholar 

  • Brophy, J., & Good, T. (1986). Teacher behavior and student achievement. In M. C. Wittrock (Ed.), Handbook of research on teaching (3rd ed.). McMillan.

    Google Scholar 

  • Burns, E. C., Martin, A. J., & Collie, R. J. (2018). Adaptability, personal best (PB) goals setting, and gains in students’ academic outcomes: A longitudinal examination from a social cognitive perspective. Contemporary Educational Psychology, 53, 57–72. https://doi.org/10.1016/j.cedpsych.2018.02.001

    Article  Google Scholar 

  • Burns, E. C., Martin, A. J., & Collie, R. J. (2019). Understanding the role of personal best (PB) goal setting in students’ declining engagement: A latent growth model. Journal of Educational Psychology, 111, 557–572. https://doi.org/10.1037/edu0000291

  • Burns, E. C., Martin, A. J., & Collie, R. J. (2021). A future time perspective understanding of high school students’ engagement and disengagement: A longitudinal investigation. Journal of School Psychology, 84, 109–123. https://doi.org/10.1016/j.jsp.2020.12.003

    Article  PubMed  Google Scholar 

  • Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105.

    Article  PubMed  Google Scholar 

  • Chadwick, D., Tindall-Ford, S., Agostinho, S., & Paas, F. (2015). Using cognitive load compliant instructions to support working memory for anxious students. 8th Cognitive Load Theory Conference: CO, USA.

    Google Scholar 

  • Christenson, S., Reschly, A., & Wylie, C. (Eds.). (2012). Handbook of research on student engagement. Springer. https://doi.org/10.1007/978-1-4614-2018-7

    Book  Google Scholar 

  • Christenson, S. L., Reschly, A. L., Appleton, J. J., Berman, S., Spanjers, D., & Varro, P. (2008). Best practices in fostering student engagement. In A. Thomas & J. Grimes (Eds.), Best practices in school psychology (5th ed.). National Association of School Psychologists.

    Google Scholar 

  • Christenson, S. L., Ysseldyke, J. E., & Thurlow, M. L. (1989). Critical instructional factors for students with mild handicaps: An integrative review. Remedial and Special Education, 10(5), 21–31.

    Article  Google Scholar 

  • Cleary, T. J., & Zimmerman, B. J. (2012). A cyclical self-regulatory account of student engagement: Theoretical foundations and applications. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 237–257). Springer. https://doi.org/10.1007/978-1-4614-2018-7_11

    Chapter  Google Scholar 

  • Covington, M. V. (2000). Goal theory, motivation, and school achievement: An integrative review. Annual Review of Psychology, 51, 171–200. https://doi.org/10.1146/annurev.psych.51.1.171

    Article  PubMed  Google Scholar 

  • Credé, M., Roch, S. G., & Kieszczynka, U. M. (2010). Class attendance in college: A meta-analytic review of the relationship of class attendance with grades and student characteristics. Review of Educational Research, 80, 272–295. https://doi.org/10.3102/0034654310362998

    Article  Google Scholar 

  • Cromley, J. G., & Byrnes, J. P. (2012). Instruction and cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 3, 545–553. https://doi.org/10.1002/wcs.1192

    Article  PubMed  Google Scholar 

  • de Bilde, J., Vansteenkiste, M., & Lens, W. (2011). Understanding the association between future time perspective and self-regulated learning through the lens of self-determination theory. Learning and Instruction, 21, 332–344. https://doi.org/10.1016/j.learninstruc.2010.03.002

    Article  Google Scholar 

  • Escueta, M., Quan, V., Nickow, A., & Oreopoulos, P. (2017). Education technology: An evidence-based review (No. w23744). National Bureau of Economic Research.

    Book  Google Scholar 

  • Evans, P., & Martin, A. J. (2022). Load reduction instruction: Multilevel effects on motivation, engagement, and achievement in mathematics. Submitted for publication. https://doi.org/10.4324/9780429283895-2

  • Feldon, D. F., Franco, J., Chao, J., Peugh, J., & Maahs-Fladung, C. (2018). Self-efficacy change associated with a cognitive load-based intervention in an undergraduate biology course. Learning and Instruction, 56, 64–72. https://doi.org/10.1016/j.learninstruc.2018.04.007

    Article  Google Scholar 

  • Finn, J. D., & Zimmer, K. S. (2012). Student engagement: What is it? Why does it matter? In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 97–131). Springer. https://doi.org/10.1007/978-1-4614-2018-7_5

    Chapter  Google Scholar 

  • Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109. https://doi.org/10.3102/00346543074001059

    Article  Google Scholar 

  • Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 763–782). Springer.

    Chapter  Google Scholar 

  • Ginns, P. (2005). Meta-analysis of the modality effect. Learning and Instruction, 15, 313–331. https://doi.org/10.1016/j.learninstruc.2005.07.001

    Article  Google Scholar 

  • Ginns, P. (2006). Integrating information: A meta-analysis of the spatial contiguity and temporal contiguity effects. Learning and Instruction, 16, 511–525. https://doi.org/10.1016/j.learninstruc.2006.10.001

    Article  Google Scholar 

  • Goldstein, H. (2003). Multilevel statistical models (3rd ed.). Hodder Arnold.

    Google Scholar 

  • Graham, C., & Hill, M. (2003). Negotiating the transition to secondary school. Scottish Council for Research in Education.

    Google Scholar 

  • Green, J., Liem, G. A. D., Martin, A. J., Colmar, S., Marsh, H. W., & McInerney, D. (2012). Academic motivation, self-concept, engagement, and performance in high school: Key processes from a longitudinal perspective. Journal of Adolescence, 35, 1111–1122. https://doi.org/10.1016/j.adolescence.2012.02.016

    Article  PubMed  Google Scholar 

  • Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.

    Google Scholar 

  • Hattie, J. (2012). Visible learning for teachers. Routledge. https://doi.org/10.4324/9780203181522

    Book  Google Scholar 

  • Hughes, M. D., Regan, K. S., & Evmenova, A. (2019). A computer-based graphic organizer with embedded self-regulated learning strategies to support student writing. Intervention in School and Clinic, 55(1), 13–22. https://doi.org/10.1177/1053451219833026

    Article  Google Scholar 

  • Hunter, M. (1984). Knowing, teaching and supervising. In P. Hosford (Ed.), Using what we know about teaching (pp. 169–192). ASCD.

    Google Scholar 

  • Jang, H., Reeve, J., & Deci, E. L. (2010). Engaging students in learning activities: It is not autonomy support or structure but autonomy support and structure. Journal of Educational Psychology, 102(3), 588–600. https://doi.org/10.1037/a0019682

    Article  Google Scholar 

  • Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539. https://doi.org/10.1007/s10648-007-9054-3

    Article  Google Scholar 

  • Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23–31. https://doi.org/10.1207/S15326985EP3801_4

    Article  Google Scholar 

  • Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93, 579–588. https://doi.org/10.1037/0022-0663.93.3.579

    Article  Google Scholar 

  • Kalyuga, S., Rikers, R., & Paas, F. (2012). Educational implications of expertise reversal effects in learning and performance of complex cognitive and sensorimotor skills. Educational Psychology Review, 24(2), 313–337. https://doi.org/10.1007/s10648-012-9195-x

    Article  Google Scholar 

  • Kauffman, D. F., & Husman, J. (2004). Effects of time perspective on student motivation: Introduction to a special issue. Educational Psychology Review, 16, 1–7. https://doi.org/10.1023/B:EDPR.0000012342.37854.58

    Article  Google Scholar 

  • Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and the multitasker. Teaching and Teacher Education, 67, 135–142.

    Article  Google Scholar 

  • Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41, 75–86. https://doi.org/10.1207/s15326985ep4102_1

    Article  Google Scholar 

  • Lambert, J., Kalyuga, S., & Capan, L. A. (2009). Student perceptions and cognitive load: What can they tell us about e-learning Web 2.0 course design? E-Learning and Digital Media, 6, 150–163. https://doi.org/10.2304/elea.2009.6.2.150

    Article  Google Scholar 

  • Lawson, M. A., & Lawson, H. A. (2013). New conceptual frameworks for student engagement research, policy, and practice. Review of Educational Research, 83(3), 432–479.

    Article  Google Scholar 

  • Lee, H. S., & Anderson, J. R. (2013). Student learning: What has instruction got to do with it? Annual Review of Psychology, 64, 445–469. https://doi.org/10.1146/annurev-psych-113011-143833

    Article  PubMed  Google Scholar 

  • Liem, G. A., & Martin, A. J. (2012). The motivation and engagement scale: Theoretical framework, psychometric properties, and applied yields. Australian Psychologist, 47, 3–13. https://doi.org/10.1111/j.1742-9544.2011.00049.x

    Article  Google Scholar 

  • Locke, E. A., & Latham, G. P. (2002). Building practically useful theory of goal setting and task motivation. American Psychologist, 57, 705–717. https://doi.org/10.1037/0003-066X.57.9.705

    Article  PubMed  Google Scholar 

  • Lysakowski, R. S., & Walberg, H. J. (1982). Instructional effects of cues, participation, and corrective feedback: A quantitative synthesis. American Educational Research Journal, 19, 559–572. https://doi.org/10.3102/00028312019004559

    Article  Google Scholar 

  • Marsh, H. W. (2002). A multidimensional physical self-concept: A construct validity approach to theory, measurement, and research. Psychology: The Journal of the Hellenic Psychological Society, 9, 459–493.

    Google Scholar 

  • Marsh, H. W., Lüdtke, O., Nagengast, B., Trautwein, U., Morin, A. J., Abduljabbar, A. S., & Köller, O. (2012). Classroom climate and contextual effects: Conceptual and methodological issues in the evaluation of group-level effects. Educational Psychologist, 47, 106–124. https://doi.org/10.1080/00461520.2012.670488

    Article  Google Scholar 

  • Marsh, H. W., Lüdtke, O., Robitzsch, A., Trautwein, U., Asparouhov, T., Muthén, B., & Nagengast, B. (2009). Doubly-latent models of school contextual effects: Integrating multilevel and structural equation approaches to control measurement and sampling error. Multivariate Behavioral Research, 44, 764–802. https://doi.org/10.1080/00273170903333665

    Article  PubMed  Google Scholar 

  • Marsh, H. W., Martin, A. J., & Hau, K. T. (2006). A multiple method perspective on self-concept research in educational psychology: A construct validity approach. In M. Eid & E. Diener (Eds.), Handbook of multimethod measurement in psychology. American Psychological Association Press.

    Google Scholar 

  • Martin, A. J. (2007). Examining a multidimensional model of student motivation and engagement using a construct validation approach. British Journal of Educational Psychology, 77, 413–440. https://doi.org/10.1348/000709906X118036

    Article  PubMed  Google Scholar 

  • Martin, A. J. (2009). Motivation and engagement across the academic lifespan: A developmental construct validity study of elementary school, high school, and university/college students. Educational and Psychological Measurement, 69, 794–824. https://doi.org/10.1177/0013164409332214

    Article  Google Scholar 

  • Martin, A. J. (2010). Building classroom success: Eliminating academic fear and failure. Continuum.

    Google Scholar 

  • Martin, A. J. (2012). Motivation and engagement: Conceptual, operational and empirical clarity. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement. Springer. https://doi.org/10.1007/978-1-4614-2018-7_14

    Chapter  Google Scholar 

  • Martin, A. J. (2015). Teaching academically at-risk students in middle school: The roles of explicit instruction and guided discovery learning. In S. Groundwater-Smith & N. Mockler (Eds.), Big fish, little fish: Teaching and learning in the middle years. Cambridge University Press.

    Google Scholar 

  • Martin, A. J. (2016). Using load reduction instruction (LRI) to boost motivation and engagement. British Psychological Society.

    Google Scholar 

  • Martin, A. J., Burns, E. C., Collie, R. J., Cutmore, M., Macleod, S., & Donlevy, V. (2021a). The role of engagement in immigrant students’ academic resilience. Submitted for publication.

    Google Scholar 

  • Martin, A. J., & Elliot, A. J. (2016). The role of personal best (PB) and dichotomous achievement goals in students’ academic motivation and engagement: A longitudinal investigation. Educational Psychology, 36, 1285–1302. https://doi.org/10.1080/01443410.2015.1093606

    Article  Google Scholar 

  • Martin, A. J., & Evans, P. (2018). Load Reduction Instruction: Exploring a framework that assesses explicit instruction through to independent learning. Teaching and Teacher Education, 73, 203–214. https://doi.org/10.1016/j.tate.2018.03.018

    Article  Google Scholar 

  • Martin, A. J., & Evans, P. (2019). Load reduction instruction: Sequencing explicit instruction and guided discovery to enhance students’ motivation, engagement, learning, and achievement. In S. Tindall-Ford, S. Agostinho, & J. Sweller (Eds.), Advances in cognitive load theory: Rethinking teaching. Routledge.

    Google Scholar 

  • Martin, A. J., & Evans, P. (2021). Load reduction instruction policy. In K.-A. Allen, A. Reupert, & L. Oades (Eds.), Building better schools with evidence-based policy: Adaptable policy for teachers and school leaders. Routledge.

    Google Scholar 

  • Martin, A. J., Ginns, P., Burns, E., Kennett, R., Munro-Smith, V., & Pearson, J. (2021b). Assessing and validating instructional cognitive load in the context of students’ psychological challenge and threat orientations: A multi-level latent profile analysis of students and classrooms. Submitted for publication.

    Google Scholar 

  • Martin, A. J., Ginns, P., Burns, E., Kennett, R., & Pearson, J. (2020b). Load reduction instruction in science and students’ science engagement and science achievement. Journal of Educational Psychology, 113(6), 1126–1142. https://doi.org/10.1037/edu0000552

    Article  Google Scholar 

  • Martin, A. J., & Jackson, S. A. (2008). Brief approaches to assessing task absorption and enhanced subjective experience: Examining ‘Short’ and ‘Core’ flow in diverse performance domains. Motivation and Emotion, 32, 141–157. https://doi.org/10.1007/s11031-008-9094-0

    Article  Google Scholar 

  • Martin, A. J., & Liem, G. A. (2010). Academic Personal Bests (PBs), engagement, and achievement: A cross-lagged panel analysis. Learning and Individual Differences, 20, 265–270. https://doi.org/10.1016/j.lindif.2010.01.001

    Article  Google Scholar 

  • Martin, A. J., & Marsh, H. W. (2003). Fear of failure: Friend or foe? Australian Psychologist, 38, 31–38. https://doi.org/10.1080/00050060310001706997

    Article  Google Scholar 

  • Martin, A. J., Sperling, R., & Newton, K. (Eds.). (2020a). Handbook of educational psychology and students with special needs. Routledge. https://doi.org/10.4324/9781315100654

    Book  Google Scholar 

  • Martin, A. J., Way, J., Bobis, J., & Anderson, J. (2015). Exploring the ups and downs of mathematics engagement in the middle years of school. Journal of Early Adolescence, 35, 199–244. https://doi.org/10.1177/0272431614529365

    Article  Google Scholar 

  • Marzano, R. J. (2003). What works in schools. ASCD.

    Google Scholar 

  • Marzano, R. J. (2011). Art and science of teaching/The perils and promises of discovery learning. Educational Leadership, 69, 86–87.

    Google Scholar 

  • Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59, 14–19. https://doi.org/10.1037/0003-066X.59.1.14

    Article  PubMed  Google Scholar 

  • Mayer, R. E., & Moreno, R. (2010). Techniques that reduce extraneous cognitive load and manage intrinsic cognitive load during multimedia learning. In J. L. Plass, R. Moreno, & R. Brunken (Eds.), Cognitive load theory (pp. 131–152). Cambridge University Press.

    Chapter  Google Scholar 

  • McGuinness, C., & Fulton, C. (2019). Digital literacy in higher education: A case study of student engagement with e-tutorials using blended learning. Journal of Information Technology Education: Innovations in Practice, 18, 1–28.

    Google Scholar 

  • McInerney, D. M., Yeung, A. S., & McInerney, V. (2001). Cross-cultural validation of the Inventory of School Motivation (ISM): Motivation orientations of Navajo and Anglo students. Journal of Applied Measurement, 2, 135–153.

    PubMed  Google Scholar 

  • Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. U.S. Department of Education, Office of Planning, Evaluation, and Policy Development.

    Google Scholar 

  • Midgley, C., Maehr, M., Hicks, L., Roesser, R., Urdan, T., Anderman, E., Kaplan, A., Arunkumar, R., & Middleton, M. (1997). Patterns of adaptive learning (PALS). University of Michigan. https://doi.org/10.1037/t19870-000

    Book  Google Scholar 

  • Moreno, R. (2010). Cognitive load theory: More food for thought. Instructional Science, 38, 135–141. https://doi.org/10.1007/s11251-009-9122-9

    Article  Google Scholar 

  • Moreno, R., & Mayer, R. E. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19, 309–326. https://doi.org/10.1007/s10648-007-9047-2

    Article  Google Scholar 

  • Nagro, S. A., Fraser, D. W., & Hooks, S. D. (2019). Lesson planning with engagement in mind: Proactive classroom management strategies for curriculum instruction. Intervention in School and Clinic, 54, 131–140. https://doi.org/10.1177/1053451218767905

    Article  Google Scholar 

  • Nakamura, J., & Csikszentmihalyi, M. (2009). Flow theory and research. In C. R. Snyder & S. J. Lopez (Eds.), Oxford handbook of positive psychology (pp. 195–206). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195187243.013.0018

    Chapter  Google Scholar 

  • Nandagopal, K., & Ericsson, K. A. (2012). Enhancing students’ performance in traditional education: Implications from the expert performance approach and deliberate practice. In K. R. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook (pp. 257–293). American Psychological Association. https://doi.org/10.1037/13273-010

    Chapter  Google Scholar 

  • Nguyen, T. (2015). The effectiveness of online learning: Beyond no significant difference and future horizons. MERLOT Journal of Online Learning and Teaching, 11(2), 309–319.

    Google Scholar 

  • OECD. (2015). Students, computers and learning: Making the connection. OECD Publishing.

    Book  Google Scholar 

  • Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1–4. https://doi.org/10.1207/S15326985EP3801_1

    Article  Google Scholar 

  • Patall, E. A., Pituch, K. A., Steingut, R. R., Vasquez, A. C., Yates, N., & Kennedy, A. A. (2019). Agency and high school science students’ motivation, engagement, and classroom support experiences. Journal of Applied Developmental Psychology, 62, 77–92.

    Article  Google Scholar 

  • Pekrun, R., & Linnenbrink-Garcia, L. (2012). Academic emotions and student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 259–282). Springer. https://doi.org/10.1007/978-1-4614-2018-7_12

    Chapter  Google Scholar 

  • Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). National Center for Research to Improve Postsecondary Teaching and Learning.

    Google Scholar 

  • Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86. https://doi.org/10.1016/S0959-4752(01)00016-0

    Article  Google Scholar 

  • Purdie, N., & Ellis, L. (2005). A review of the empirical evidence identifying effective interventions and teaching practices for students with learning difficulties in Years 4, 5, and 6. Australian Council for Educational Research.

    Google Scholar 

  • Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage.

    Google Scholar 

  • Reeve, J. (2012). A self-determination theory perspective on student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 149–172). Springer. https://doi.org/10.1007/978-1-4614-2018-7_7

    Chapter  Google Scholar 

  • Reeve, J. (2013). How students create motivationally supportive learning environments for themselves: The concept of agentic engagement. Journal of Educational Psychology, 105(3), 579–595.

    Article  Google Scholar 

  • Reeve, J., & Shin, S. H. (2020). How teachers can support students’ agentic engagement. Theory Into Practice, 59(2), 150–161.

    Article  Google Scholar 

  • Renkl, A. (2014). Toward an instructionally oriented theory of example-based learning. Cognitive Science, 38, 1–37. https://doi.org/10.1111/cogs.12086

    Article  PubMed  Google Scholar 

  • Renkl, A., & Atkinson, R. K. (2010). Learning from worked-out examples and problem solving. In J. L. Plass, R. Moreno, & R. Brunken (Eds.), Cognitive load theory (pp. 91–108). Cambridge University Press. https://doi.org/10.1017/CBO9780511844744.007

    Chapter  Google Scholar 

  • Reschly, A. L., & Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evolution and future directions of the engagement construct. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 3–19). Springer.

    Chapter  Google Scholar 

  • Rosenshine, B. V. (1995). Advances in research on instruction. The Journal of Educational Research, 88, 262–268. https://doi.org/10.1080/00220671.1995.9941309

    Article  Google Scholar 

  • Rosenshine, B. V. (2009). The empirical support for direct instruction. In S. Tobias & T. M. Duffy (Eds.), Constructivist instruction: Success or failure? Routledge.

    Google Scholar 

  • Rosenshine, B. V., & Stevens, R. (1986). Teaching functions. In M. C. Wittrock (Ed.), Handbook of research on teaching (3rd ed., pp. 376–391). McMillan.

    Google Scholar 

  • Schunk, D. H., & Mullen, C. A. (2012). Self-efficacy as an engaged learner. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 219–235). Springer. https://doi.org/10.1007/978-1-4614-2018-7_10

    Chapter  Google Scholar 

  • Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78, 153–189. https://doi.org/10.3102/0034654307313795

    Article  Google Scholar 

  • Sierens, E., Vansteenkiste, M., Goossens, L., Soenens, B., & Dochy, F. (2009). The synergistic relationship of perceived autonomy support and structure in the prediction of self-regulated learning. British Journal of Educational Psychology, 79, 57–68. https://doi.org/10.1348/000709908X304398

    Article  PubMed  Google Scholar 

  • Skinner, Ε. Α., & Pitzer, J. R. (2012). Developmental dynamics of student engagement, coping, and everyday resilience. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 21–44). Springer.

    Chapter  Google Scholar 

  • Slavin, R. E. (1995). A model of effective instruction. The Educational Forum, 59(2), 166–176.

    Article  Google Scholar 

  • Swann, W. (2013). The impact of applied cognitive learning theory on engagement with e-learning courseware. Journal of Learning Design, 6, 61–74. https://doi.org/10.5204/jld.v6i1.119

    Article  Google Scholar 

  • Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9–31. https://doi.org/10.1023/B:TRUC.0000021808.72598.4d

    Article  Google Scholar 

  • Sweller, J. (2012). Human cognitive architecture: Why some instructional procedures work and others do not. In K. R. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook (pp. 295–325). American Psychological Association. https://doi.org/10.1037/13273-011

    Chapter  Google Scholar 

  • Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer. https://doi.org/10.1007/978-1-4419-8126-4

    Book  Google Scholar 

  • Van Merriënboer, J. J. (1992). Training complex cognitive skills: A four-component instructional design model for technical training. Educational Technology Research and Development, 40, 23–43. https://doi.org/10.1007/BF02297047

    Article  Google Scholar 

  • Voelkl, K. E. (2012). School identification. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 193–218). Springer. https://doi.org/10.1007/978-1-4614-2018-7_9

    Chapter  Google Scholar 

  • Wiliam, D. (2011). Embedded formative assessment. Solution Tree Press.

    Google Scholar 

  • Winne, P. H., & Nesbit, J. C. (2010). The psychology of academic achievement. Annual Review of Psychology, 61, 653–678. https://doi.org/10.1146/annurev.psych.093008.100348

    Article  PubMed  Google Scholar 

  • Zimmerman, B. J. (2002). Achieving self-regulation: The trial and triumph of adolescence. In F. Pajares & T. Urdan (Eds.), Academic motivation of adolescents. Information Age Publishing.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Education, University of New South Wales, Sydney, NSW, Australia

    Andrew J. Martin

Authors
  1. Andrew J. Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew J. Martin .

Editor information

Editors and Affiliations

  1. University of Georgia, Athens, GA, USA

    Amy L. Reschly

  2. University of Minnesota, Minneapolis, MN, USA

    Sandra L. Christenson

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Martin, A.J. (2022). The Role of Academic Engagement in Students’ Educational Development: Insights from Load Reduction Instruction and the 4M Academic Engagement Framework. In: Reschly, A.L., Christenson, S.L. (eds) Handbook of Research on Student Engagement. Springer, Cham. https://doi.org/10.1007/978-3-031-07853-8_23

Download citation

Publish with us

Policies and ethics

Search

Navigation