By Professor Andrew Martin MAPS, Faculty of Education and Social Work, University of Sydney (from 2014: School of Education, University of New South Wales)

Many factors and processes operate in the classroom to affect academic learning. These factors and processes can be broadly categorised into two groups: will and skill (Covington, 1992). ‘Will’ refers to student motivation, while ‘skill’ refers to the knowledge and competencies centrally relevant to performing academic tasks. Research and theory generally confirm that will precedes skill; that is, motivation represents the inner drive and activity that provides the impetus, energy and direction required to develop and sustain one’s knowledge and competence.

In addition to this inner drive, research and theory show that external influences play an important role in the development of will and skill. Major external influences include parents/caregivers, peers, counsellors/psychologists and teachers. Of these, much research suggests it is the teacher (via instruction) who perhaps plays the greatest role in directly impacting both academic will and academic skill.

Educational psychology – supported and informed by cognate domains such as cognitive, school and social psychology – contributes research, theory and practice that draws together and explains factors and processes relevant to the development of academic will and skill. Harnessing seminal, contemporary and evolving ideas in educational psychology, this article explores motivation and instruction in today’s classroom with a view to better understanding how to enhance students’ academic will and skill, two factors central to their learning.

Learning and academic will: The role of student motivation

Academic motivation is defined here as students’ energy, drive and inclination to learn and achieve. Dominant theories of academic motivation tend to couch it in cognitive and affective terms. Some models of motivation also include behaviour (e.g., persistence, attendance) – however, many researchers now agree that engagement (rather than motivation) is more relevant to behaviour.

In recent years there has been some frustration among practitioners, researchers and theorists that the field of motivation is diffuse and in need of integration. This fragmentation has been seen as a major barrier for practitioners trying to apply ideas and advice, and has led to calls for more unifying models of academic motivation (Pintrich, 2000).

The Motivation and Engagement Wheel

One unifying model, very much based on data from Australian students, takes the form of the Motivation and Engagement Wheel (Figure 1; Martin, 2007, 2009, 2010). The Wheel comprises eleven factors, separated into adaptive and maladaptive cognitive-affective and behavioural dimensions. These factors emanate from major theories of achievement motivation and are grouped under four clusters: (1) adaptive motivation; (2) adaptive engagement; (3) maladaptive motivation; and (4) maladaptive engagement. This article focuses on motivation, or the ‘will’ part of the Wheel (a discussion of engagement can be found elsewhere; Martin, 2010).

‘Adaptive motivation’ is comprised of three adaptive behaviours: self-belief is students’ belief and confidence in their ability to understand or to do well in their schoolwork, to meet challenges they face, and to perform to the best of their ability; learning focus is being focused on learning, solving problems, effort and developing skills, more than being focused on competition, ability and comparisons with others; and, valuing school is how much students believe what they learn at school is useful, important, and relevant to them or to the world in general. 

Maladaptive motivation consists of three maladaptive cognitions and affects in students: anxiety, which involves feeling nervous and uneasy when thinking about schoolwork and assignments/exams, and worrying about not doing well in schoolwork, assignments/exams; failure avoidance (or fear of failure) refers to students’ tendency to do their schoolwork for the primary purpose of avoiding doing poorly or avoiding being seen to do poorly; and, uncertain control reflects students’ uncertainty about how to do well or how to avoid doing poorly.

Motivation and academically at-risk students

The bulk of motivation research and theory tends to be focused on ‘mainstream’ or ‘regular’ students who experience no major academic risk, although motivation can still be a challenge. Not surprisingly, however, motivation can be quite a significant barrier for academically at-risk students (e.g., those with learning difficulties and learning disabilities). For example, a history of underachievement negatively impacts self-belief and a valuing of school; it also increases anxiety, elevates fear of failure, and reduces a sense of control. In some cases, this sense of control is so challenged that at-risk students completely disengage (see Sideridis, 2009, for a review). Importantly, however, research demonstrates some motivational congruencies between academically at-risk students and students not at risk – hence, it is possible for academically at-risk students to have positive motivational profiles (Martin, 2012, in press).

Motivation research and intervention

Importantly, the motivation factors identified in the Wheel are significantly linked to students’ academic engagement, interest in school, enjoyment of schoolwork, effort, self-regulation, class participation, academic resilience, attendance, study patterns, and homework and assignment completion. Moreover, it is very much via these connections that motivation (‘will’) leads to knowledge acquisition, skill development and competence (‘skill’; Martin, 2007, 2009, 2010; Pintrich, 2000).

Intervention research conducted over the past two decades demonstrates that each of these specific motivation factors can be enhanced and sustained. For the three adaptive motivation factors, research has demonstrated that students: (1) can be taught how to think more positively and constructively about themselves as students (self-belief); (2) can be more focused on improvement and personal progress than on competition and comparisons with others (learning focus); and (3) can be shown the relevance of schoolwork to their lives and the short-term and lifelong yields of education (valuing school).

In terms of the three maladaptive motivation factors, there is a long line of cognitive and behavioural approaches to reducing anxiety; there are well-established practices for helping students develop the courage to constructively and pro-actively respond to possible mistakes and failure (failure avoidance; fear of failure); and students can be taught how to focus on factors within their control (e.g., effort) and to reduce their focus on things outside of their control (e.g., good or bad luck) (uncertain control).

Having optimised students’ academic will, instruction can be more effectively targeted at developing students’ skill, as discussed below.

Learning and academic skill: The role of instruction

In recent years there has been something of a tussle between heavily constructivist (and post-modernist) approaches to instruction and more explicit and direct approaches to instruction. Interpretations of the former have led to very student-centred learning, discovery and enquiry-based approaches, with the teacher seen more as a facilitator of learning. The latter (explicit) approach tends to be more teacher-centred, focused on explicit and structured instruction (including some deliberate practice and drill), with the teacher seen more as an activator and director (rather than facilitator) of learning.

In a recent review of instruction and its links to achievement, Liem and Martin (2013) found that effect sizes were larger for explicit instruction than for inquiry-based and discovery learning. Importantly, however, rather than dismissing discovery-based approaches, they suggested the difference in effect sizes may be because many educators introduce discovery-based approaches too early in the learning process. In contrast, when students have been well guided, supported and led by the teacher in initial learning (explicit instruction), the student is sufficiently skilled and knowledgeable to engage in meaningful and informed discovery-based learning. Educational and cognitive psychologies have contributed much to understanding explicit instruction as the front-end of learning and skill development, which lays a solid foundation for subsequent meaningful and supported discovery-based learning.

Explicit instruction

Explicit instruction refers to a mode of teacher-led instruction that involves: (1) reducing the difficulty of a task during initial learning; (2) various approaches to instructional support and scaffolding; (3) ample practice; (4) appropriate provision of instructional feedback; and (5) monitored independent practice (Adams & Engelmann, 1996; Rosenshine, 2009). Following this sequence of instruction and demonstrated learning, the teacher may move from being an explicit and direct activator to being a facilitator of discovery- and exploratory-based approaches.

Many of these strategies are based on principles of human cognitive architecture relevant to working and long-term memory. From a cognitive load perspective, for example, learning very much relies on building long-term memory and effectively managing working memory to facilitate this (Mayer & Moreno, 2010; Sweller, 2012). The fact that working memory is limited presents an enormous challenge to educators as it is this part of the cognitive architecture that must be fully accommodated when teaching new material (Winne & Nesbit, 2010). Because long-term memory has no such limitations, a teacher’s task is to develop instruction and instructional material that optimally assists working memory to process information that can be transferred to long-term memory (Sweller, 2012; Winne & Nesbit, 2010). Explicit instruction emphasises the centrality of the teacher structuring material and activities to assist working memory by reducing ambiguity, enhancing clarity, building in sequencing, harnessing scaffolds, and promoting deliberate and guided practice to help automate some learning processes to reduce the burden on working memory (Kirschner, Sweller, & Clark, 2006).

Discovery (and similar) learning

Discovery learning has a potentially important place in the learning process. Liem and Martin (2013) suggest that after sufficient direct input, guided practice and independent demonstration of learning, there is then a place for guided discovery learning. Having moved beyond novice status, learners now have the skills and knowledge to engage in more meaningful and richer discovery learning. That is, having acquired the requisite knowledge and skills in long-term memory, there is no longer the load on working memory to acquire this knowledge and skill. Working memory can then be directed to applying the acquired knowledge and skill in potentially novel and creative ways. It may be, then, that some of the low to moderate effect sizes associated with problem-based, inquiry-based and discovery learning are a result of these learning practices being implemented too early in the learning process. Further research is needed here, but some work has confirmed that once learners become expert, they benefit more from problem solving approaches than from structured and explicit approaches to learning (Kalyuga, Chandler, Tuovinen, & Sweller, 2001).

It therefore seems that the effectiveness of explicit and constructivist teaching and learning are intertwined such that the effectiveness of one depends on the successful implementation of the other. As summarised in Liem and Martin (2013): “constructivist approaches are better assisted by direct and structured input from the teacher that systematically and unambiguously builds the knowledge and skills needed to subsequently engage in meaningful discovery, problem-based, and enquiry-based learning. If we may, the horse must be well and truly before the cart when it comes to effective instruction and learning” (p. 368).

Explicit instruction and academically at-risk students

Given most classrooms include students at academic risk (e.g., students with learning difficulties or disabilities), evidence must not only support the effectiveness of explicit instruction for the majority of students, but also for those who struggle academically. Encouragingly, it is possible that academically at-risk students may gain particular benefit from explicit instruction. It has been claimed that such students can have difficulty understanding or identifying many of the subtleties of instructional material and the ‘hidden structure’ of learning (Ewing, 2011). By making all elements of learning explicit, less is hidden and more becomes accessible to these students.

Indeed, in their discussion of the myths associated with explicit instruction, Adams and Engelmann (1996) emphasise that direct instruction is appropriate for low and high performers and also for low-level and more advanced tasks.  In fact, some researchers argue that low and high performers are not markedly qualitatively different. For example, there are relatively few mistakes or tendencies unique to low performers that high performers are not at risk of making. Instead, variation seems to be in the degree and amount of a particular instructional approach that is appropriate for low and high performers. Thus, the main variation in instruction would be the pace of presentation and the relative weight given to the core steps in explicit instruction. Thus, while high and low performers both receive explicit instruction, high performers might move onto discovery-based approaches sooner than low performers.

From skill to will: From learning to motivation

Of course, the process does not end with skill and knowledge acquisition. Research shows there is a cycle that operates such that learning (‘skill’) fosters subsequent motivation (‘will’) (Martin, 2007, 2009, 2010; Pintrich, 2000), as demonstrated in Figure 2. For example, self-belief is likely to be enhanced (or sustained) when students acquire academic knowledge and academic skill. Similarly, students who are learning tend to value school and school subjects more than students who are not learning. Moreover, when students feel on top of their learning, they tend to be less anxious and have a greater sense of control. In all these cases, learning has enhanced students’ academic motivation.

In addition, because explicit instruction is a key to learning important skills and academic subject matter, it also has an important contribution to make to students’ motivation. In essence, we must not underestimate the motivating properties of clear, structured and well-shepherded instruction, effortful academic application, and deliberate and guided practice. Further, when the required skill level has been attained, we must also not underestimate the motivating properties of well-supported discovery learning.


The learning process can be characterised as one in which students move from ‘will’ to ‘skill’. Educational and cognitive psychologies have contributed much to our understanding of how students learn and how to move them towards independent discoverers via teacher-led explicit and structured instruction. When students have academic will and skill, their educational journey is much more enjoyable and successful.

The author would like to thank the Australian Research Council for funding the research program informing this article and Professor John Sweller for comments on an earlier draft.

The author can be contacted at and from January 2014 at


  • Adams, G., & Engelmann, S. (1996). Research on Direct Instruction: 25 years beyond DISTAR. Seattle, WA: Educational Achievement Systems.
  • Covington, M.V. (1992). Making the grade: A self-worth perspective on motivation and school reform. Cambridge: Cambridge University Press.
  • Ewing, B. (2011). Direct instruction in mathematics: Issues for schools with high Indigenous enrolments: A literature review. Australian Journal of Teacher Education, 36, 64-91.
  • Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London: Routledge.
  • 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.
  • Kirschner, P., Sweller, J., & Clark, R. (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.
  • Liem, G. A. D., & Martin, A. J. (2013). Direct instruction and academic achievement. In J. Hattie & E. Anderman (Eds.), International Guide to Student Achievement. Oxford: Routledge.
  • 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.
  • 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.
  • Martin, A. J. (2010). Building classroom success: Eliminating academic fear and failure. New York: Continuum.
  • Martin, A. J. (2012). The role of Personal Best (PB) goals in the achievement and behavioral engagement of students with ADHD and students without ADHD. Contemporary Educational Psychology, 37, 91-105.
  • Martin, A. J. (in press). Academic buoyancy and academic outcomes: Towards a further understanding of students with ADHD, students without ADHD, and academic buoyancy itself. British Journal of Educational Psychology.
  • 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. Cambridge: Cambridge University Press.
  • Pintrich, P. R. (2000). Educational psychology at the millennium: A look back and a look forward. Educational Psychologist, 35, 221-226.
  • Rosenshine, B. V. (2009). The empirical support for direct instruction. In S. Tobias & T. M. Duffy (Eds.), Constructivist instruction: Success or failure? New York: Routledge.
  • Sideridis, G. D. (2009). Motivation and learning disabilities: Past, present, and future. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of school motivation. New York: Routledge.
  • 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. Washington: American Psychological Association
  • Winne, P. H., & Nesbit, J. C. (2010). The psychology of academic achievement. Annual Review of Psychology, 61, 653-678.

InPsych December 2013