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Metacognition and Motivation Research Thrust

Metacognition and motivation are two broad classes of learner factors that influence (a) what instructional events a learner participates in and (b) how a learner engages in those events. The Metacognition and Motivation (M&M) thrust focused on the influence of these factors on robust learning. Based on promising prior research, the thrust focused on particular metacognitive skills (self-assessment, proper help use, off-task behavior, self-explanation), particular motivational variables (achievement goals, self-efficacy, interest) and particular affective variables (engaged concentration, confusion, frustration, boredom).

Metacognition and Learning.  A major line of work on metacognition explored students metacognitive awareness of benefits of self-directed effort (trying without help) versus getting help and metacognitive strategies for deciding to try or to seek help. This work was explored in the context of online tutoring where fine-grained longitudinal process data is available on students’ help-seeking choices and the contexts in which they make them.  Overlapping with goals of the Computational Modeling and Data Mining thrust, this research has involved the data-driven creation of a computational model of good and bad help-seeking — early publications of this model have been quite influential (e.g., Aleven, Mclaren, Roll, & Koedinger, 2006, cited by 247).  Later work has demonstrated success of providing adaptive tutoring of metacognitive strategies in producing lasting gains in the targeted help-seeking strategies (Roll, Aleven, McLaren, & Koedinger, 2011, cited by 140; Aleven, Roll, & Koedinger, 2012). In particular, rural high-school students used a Cognitive Tutor augmented with a metacognitive “help-seeking tutor” in geometry classes for two months.  In the month following, they demonstrated better help-seeking skills while working with a different Cognitive Tutor unit even though the help-seeking tutor support have been removed. These students internalized principles of good help-seeking and applied them spontaneously in new learning without any on-going metacognitive support.

Zepeda et al. (2015) found that metacognitive instruction can lead to better self-regulated learning outcomes during adolescence, a period in which students’ academic achievement and motivation often decline.  Students who received the metacognitive instruction and training (i.e., a 6-hr intervention designed to teach the declarative and procedural components of planning, monitoring, and evaluation) were less biased when making metacognitive judgments and performed better on a novel self-guided learning activity than students in business-as-usual control, which received more problem-solving practice in place of the metacognitive training. In addition, the intervention had positive impacts on motivation including student’s task value, self-efficacy, adoption of mastery goals, and beliefs about intelligence.

More generally, M&M research and development of metacognitive support within online learning has been highly influential, as reflected in review pieces of our own and of others that draw substantially on LearnLab work (e.g., Azevedo & Aleven, 2013;  Koedinger, Aleven, Roll, & Baker, 2009; Goldberg, & Spain, 2014).

Motivation and Learning. An important recent example of M&M research on motivational factors, including game formats for instruction, found that a CTAT-built tutor for equation solving produced better learning than did DragonBox 12+, a popular, commercial algebra game (Long & Aleven, 2014).  This work also demonstrated that a common level selection screen in games, which allows students to select levels, including ones previously practiced, is suboptimal for learning.

A general approach of this and other M&M studies is to identify elements in games that contribute to engagement and test if these elements can be incorporated in an existing learning environment to enhance learning and motivation. Another example that produced a surprising result started with an observation of a contrast between the way feedback is used in games versus tutors. Tutors help students avoid floundering by giving immediate feedback on problem solving, whereas games allow for floundering – within reasonable and sometimes invisible constraints – to promote a sense of challenge and discovery. Easterday, Aleven, Scheines, & Carver (2011) conducted a study to compare the effect of a more “tutor-like” feedback mechanism against a more “game-like” policy. Surprisingly, they found that the tutor-like feedback policy (i.e., more direct feedback aimed at reducing floundering) led not only to greater learning, but was also associated with greater interest in the subject matter. These studies provide cautionary notes against importing game elements into learning environments without careful evaluation.

Belenky and Nokes-Malach (2012) demonstrated a strong relationship between student motivation and knowledge transfer. They measured student achievement goal orientations and then gave students either invention or tell-and-practice activities for learning statistics concepts.  They found that students’ who were initially high in mastery-approach orientation were more likely to transfer in a test of preparation for future learning regardless of what type of learning activity they were given. For those who entered the activity initially low in mastery-approach orientation, there was a particular benefit in learning with invention activities on transfer. This result suggests how mastery-approach goals may serve as a mechanism of transfer that facilitates constructive cognitive processes and helps connect later learning episodes with relevant earlier learning.

An exploration of the motivation effects of adding instructional explanations to worked examples (Richey & Nokes-Malach, 2013) found general benefits for withholding explanations, but particularly for students with higher scores on a motivational scale of achievement goal orientations.  The paper links robust learning—a key construct of the KLI framework—to instructional strategies in math and science. This paper in the Learning and Instruction journal is one of a number that extend exposure of the KLI framework from its origin in Cognitive Science to a wider Education researcher audience (e.g., Frishkoff, Perfetti, & Collins-Thompson, 2011; Koedinger & McLaughlin, 2014; Koedinger & Wiese, 2015; Nokes-Malach, & Mestre, 2013; Perfetti & Stafura, 2014).

Bernacki et al. (2016) demonstrated that a brief writing intervention can improve student’s motivation in a middle school science class. Students in the experimental condition showed higher endorsement of mastery goals and reported greater situational interest in science topics after the intervention compared to students who summarized the lessons.