Alternative assessment of strategy use with self-report instruments: a discussion

By M. Veenman. 2011

264 citations recorded by the source corpus.

Strategy use is a very broad term for controlled and consciously applied procedural knowledge, in contrast with skills that are automated to a more or lesser extent (Gagne et al. 1993). Both terms, however, are used interchangeably when referring to processes that direct and support problem solving and learning. There is a gray area in between controlled strategy use and the automated performance of skills (Veenman 2011). For instance, monitoring processes may be consciously applied during reading, but often run in the background until an error or anomaly is detected. The latter is the case with more experienced readers. In this special issue, many of the strategic processes described operate in this gray area. For instance, confidence judgments or inference processes are neither fully controlled, nor fully automated. Consequently, learners may not be fully aware of ongoing processes, which may affect the verbalization of these processes in self-reports. Strategic processes may be cognitive or metacognitive by nature. According to Nelson (1996), cognitive and metacognitive processes operate at different levels of the cognitive system that represent different functions. Higher-order metacognitive processes monitor and regulate lower-order cognitive processes that, in turn, shape behavior. Thus, drawing inferences is a cognitive activity, but the self-induced decision to initiate such activity is a metacognitive one (Veenman 2011). The authors of this special issue diverge in the perspective that they have chosen for studying strategic processes in reading. Schellings (this issue) discusses the role and assessment of learning strategies, which encompass both cognitive and metacognitive strategies, but her empirical work is entirely based on assessment methods for metacognition. Braten and Stromso (this issue) avoid classifying the inference processes in their study as either cognitive or metacognitive. Magliano et al. (this issue) clearly state that their RSAT does not provide a direct measure of metacognitive processes, but rather captures (cognitive) information-processing activities. On the other hand, they do acknowledge that metacognitive awareness is prerequisite to adequately Metacognition Learning (2011) 6:205–211 DOI 10.1007/s11409-011-9080-x

Subfields: Artificial Intelligence, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/2304447f5057df32c7920b01317c545b8e6f280e

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Citations in the corpus

Third wave of measurement in the self-regulated learning field: when measurement and intervention come hand in hand (2016, methodology): “When developing or using measurement + intervention instruments it is essential to consider their validity (Pintrich et al., 2000; Veenman, 2011).”
Metacognition in Psychology (2019, background): “…conception has shifted toward one where self-regulation is seen as less stable and more contextualized, thus requiring online measures that are more closely associated with the performance of specific learning or cognitive tasks (see, for example, Panadero, Klug, & Järvelä, 2016; Veenman, 2011).”
The Role of Direct Strategy Instruction and Indirect Activation of Self-Regulated Learning—Evidence from Classroom Observation Studies (2020, background): “…to large groups, and their analyses are efficient regarding time and costs, off-line self-reports can suffer from validity problems (see Veenman 2011, or Veenman and van Cleef 2019 for an overview): First, teachers have to reconstruct their earlier teaching behavior from memory, and…”
Measuring the complexity of upper primary school children’s self-regulated learning: A multi-component approach (2013, background): “The major advantage of these methods is that they give information about the learning activities at the moment they take place, making the information less vulnerable to students’ memory distortions (Veenman, 2011).”
Granularity matters: comparing different ways of measuring self-regulated learning (2019, background): “Specifically, learners who have insufficient declarative knowledge about self-regulatory strategies may incorrectly label the strategies they report using (Veenman 2011).”
Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses (2017, methodology): “There is a critique of self-reported data (Boekaerts & Corno, 2005; Veenman, 2011), as well as a defense for other types of self-reported data (Samuelstuen & Bråten, 2007).”
Revising the Metacognitive Awareness of Reading Strategies Inventory (MARSI) and testing for factorial invariance (2018, background): “…use strategies to a different extent in different contexts, even in academic reading, and context-free measures do not accurately reflect strategy use for all of those contexts (e.g., Bråten & Strømsø, 2011; Hadwin, Winne, Stockley, Nesbit, & Woszczyna, 2001; Pressley, 2000; Veenman, 2011).”
Assessing How Teachers Enhance Self-Regulated Learning: A Multiperspective Approach (2013, background): “…for both students’ and teachers’ questionnaires (Winne, 2010; Winne & Perry, 2000): Empirical studies on assessing students’ SRL by means of questionnaires have revealed only weak correlations with other types of assessments, and only low or no correlations with performance data (Veenman, 2011).”
The Relation Between Self-Regulated Learning and Academic Achievement Across Childhood and Adolescence: A Meta-Analysis (2015, background): “That is, students have to draw from fewer and more specific episodes to arrive at a typical or characteristic use of strategies in structured interviews, which should improve students’ recall accuracy (Veenman 2011).”