Deep Motivation
© Francois Victor Tochon

We Rarely Use the Results of Research on Human Motivation

Three psychological and motivational theories legitimate an open project-based approach in learning and assessment. A whole branch of alternative assessment has been created on this model. Three psychological theories illuminate why this approach is so powerful to motivate students: (1) self-efficacy theory (Bandura, 1986), (2) attribution theory (Weiner, 1986), and (3) self-determination theory (Deci, Vallerand, Pelletier & Ryan, 1991). According to all three motivation theories, open projects increase the dynamics of learning tremendously because they benefit from students’ intrinsic motivation. Self-efficacy requires that learners set their course of action, practice cognitive and metacognitive strategies, with successful peers to model action. Thus, they realize they can achieve their goals. Students need to be in control of the determining factors of their success. The same issue applies to assessment if you want to assess proficiency (NOT controlled learning). Attribution theory indicates that learning must be a source of pleasure to be effective. Self-accomplishment is a major motivating goal. Prolonged, autonomous effort is the key to the success of expert professionals, rather than help or chance. The source of achievement is internal, not external. Controlled teaching and learning thus deprive the students from their main source of energy and motivation. Moreover, teachers who adopt controlled approaches feel they work against the grain, in some way, that is against the inner will of their students. Students need team relationships, autonomy, and self-directed competence. Promoting self-determination, effort-enhancing attributions and a sense of self-efficacy are crucial to propose deeper approaches to language teaching and learning. What do we do to build on these findings on motivation research?

When our programs, methods and learning environments control details of accomplishment in a way that goes against intrinsic motivation, we contradict its natural drive. Fear of failing stimulates extrinsic motivation to get good marks. In contrast, deep learning requires contextualized, holistic experiences, in which the identity narrative can expand with new life meanings. Deep understanding characterizes deep learning (Akbar Hessami & Sillitoe, 1990). The focus is on what is signified, linking prior information to everyday experience. Deep processing involves re-conceptualizing how reality is viewed (Entwistle, 2000). In contrast, controlled environments produce surface learning based upon extrinsic motivation. In this respect, alternative forms of assessment are more powerful than traditional forms of norm-referenced evaluation. Criterion reference can handle complexity, thoroughness, treat open and creative questions, and address proficiency. The evaluation criteria can be explored together, discussed, negotiated, reworked, and can serve as guides in the accomplishment of projects, establishing the scale of achievement. The negotiated criteria can form the basis for instructional agreements. Such contracts, even informal, help plan, structure, and personalize the learning process (Šliogerienė, 2006). These agreements can be oral, audio recorded, e-mailed or handwritten, internet-based, pre-formatted or assembled in the form of tables and criterial rubrics. Within this format, students face a series of free choices, NOT a closed and constraint environment.

Excerpt from:

Tochon, F. V. (2010). A Deep Approach to Language Multimedia and Evaluation: For a more Colorful Future. Invited Keynote Speech. Proceedings of the Fourteenth international conference of APAMALL and ROCMELIA(pp.73-92). Kaohsiung, Taiwan: National Kaohsiung Normal University.

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