Student teachers' prior knowledge as prerequisite to learn how to assess pupils' learning strategies

Teaching and Teacher Education xxx (2018) 1e15

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Student teachers' prior knowledge as prerequisite to learn how to assess pupils' learning strategies Inga Glogger-Frey*, Marcus Deutscher, Alexander Renkl Department of Educational and Developmental Psychology, Institute of Psychology, University of Freiburg, Engelbergerstr. 41, 79085 Freiburg, Germany

h i g h l i g h t s
Student teachers' knowledge about learning strategies was misconceived and in pieces.
Tailored short training prepared participants to assess strategies in student work.
Correct conceptual knowledge on learning strategies predicted strategy assessment.
Even misconceptual knowledge served as resource for learning to assess strategies.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 February 2017 Received in revised form 23 January 2018 Accepted 24 January 2018 Available online xxx

This study analyzed whether student teachers exhibit insufficient prior knowledge concerning learning strategies and whether different contexts lead to variations in activated knowledge. Furthermore, we investigated whether pieces of prior-knowledge and their structured-ness were associated with the assessment of pupils' learning strategies. In a within-subjects experiment (ABABAB design; N ¼ 47 student teachers), questions about learning-strategy application referred to either (A) pupil-focused or (B) teacher-focused contexts. After a short training intervention, student teachers assessed strategies in pupils’ work. Different prior knowledge was elicited depending on contexts, suggesting “knowledge-inpieces”. Prior-knowledge pieces, even if insufficient, served as a prerequisite to learn strategy assessment. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Learning-strategy assessment Teacher education Prior knowledge Pedagogical knowledge-in-pieces Assessment literacy

To make teacher education effective, we need information about student teachers' knowledge prerequisites for future tasks (Ell, Hill, & Grudnoff, 2012). This study aimed to identify prior knowledge about comprehension-oriented learning (COr) strategies as a prerequisite for the future task of assessing learning strategies in pupils' work. Previous research on teacher assessment has rarely addressed this area of teachers' knowledge, yet it is extremely relevant to teachers' responsibility to support pupils' learning (Askell-Williams, Lawson, & Skrzypiec, 2012). Teachers should be capable of assessing and supporting their pupil's strategy application (Askell-Williams et al., 2012; Kiewra & Gubbels, 1997; LohseBossenz, Kunina-Habenicht, & Kunter, 2013). However, they exhibit deficits in this area (Hamman, Berthelot, Saia, & Crowley,

2000; cf.; Dignath, Buettner, & Langfeldt, 2008; Dignath-van Ewijk & van der Werf, 2012; Durkin, 1978; Moely et al., 1992). In this study, we assumed that student teachers hold insufficient prior knowledge about COr strategies (Clift, Ghatala, Naus, & Poole, 1990; Klug, Bruder, Kelava, Spiel, & Schmitz, 2013). However, we did not simply expect it to be “correct, but insufficient”. Rather, we assumed that it is fragmented and in part incorrect, hampering learning from standard instruction. Thus, the aim of this study was first to test whether student teachers demonstrate such inaccurate and fragmented knowledge. This assumption was tested by asking them about learning strategies in relation to different contexts in a within-subject design. Second, we asked whether several features of such prior knowledge predicted how well student teachers could later assess pupils' learning strategies.

* Corresponding author. E-mail addresses: [email protected] (I. Glogger-Frey), [email protected] (M. Deutscher), [email protected] (A. Renkl). https://doi.org/10.1016/j.tate.2018.01.012 0742-051X/© 2018 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Glogger-Frey, I., et al., Student teachers' prior knowledge as prerequisite to learn how to assess pupils' learning strategies, Teaching and Teacher Education (2018), https://doi.org/10.1016/j.tate.2018.01.012

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1. Learning strategies: what should student teachers ideally know about it? This section introduces the scientifically accepted (“correct”) concepts of learning strategies, thus providing a basis to decide whether empirically-identified knowledge is scientifically acceptable. The following minimal definition of learning strategies proved to be helpful (Glogger-Frey, Ampatziadis, Ohst, & Renkl, in press; Ohst, Fondu, Glogger, Nückles, & Renkl, 2014; Ohst, Glogger, Nückles, & Renkl, 2015): A learning strategy is a mental activity, regulated by the learner, and directly related to understanding subject matter. The definition helps, for example, to differentiate teaching strategies from learning strategies as the former are not regulated by the learner, but by the teacher. By referring to strategies directly related to understanding, we focus on the center of a prominent model of self-regulated learning by Boekaerts (1999), namely on comprehension-oriented learning strategies. Inspired by Boekaerts' model, we discern this COr strategy core, and layers of selfregulated learning around the core. The second, middle layer represents the strategies that support the use of comprehensionoriented learning strategies (support strategies as in Dansereau, 1985; Muelas & Navarro, 2015). The outer layer represents regulation of the self, such as the choice of (personal) goals. It is rather distant from the actual cognitive processes leading to understanding. Hence, when teachers assess their pupils' learning strategies they should, first of all, evaluate strategies from the core. These COr strategies are central, as they directly build understanding and knowledge (Dunlosky & Bjork, 2014; Nelson & Narens, 1994). They correspond to cognitive and metacognitive strategies that all have different important functions. Essential cognitive learning strategies are elaboration and organization. The main function of organization is to identify interrelations and hierarchies within the new learning contents (e.g., identifying main ideas). The main function of elaboration is to integrate new learning contents into prior knowledge or experiences (e.g., by thinking of an example of a newly learned concept). Important metacognitive learning strategies (directly related to comprehension-building) are the planning and monitoring of cognitive strategies and one's own comprehension. Monitoring comprehension identifies gaps in one's understanding, so that this gap can be filled. Teachers should know about these strategies' specific functions and be able to distinguish them from each other (Lohse-Bossenz et al., 2013). The second-layer strategies that we call support strategies (e.g., Dansereau, 1985; Ohst et al., 2014, 2015) are not directly related to building understanding, but they support the use of COr strategies. Resource strategies such as time management or motivational strategies (Muelas & Navarro, 2015), or learning in a collaborative setting provide such support. For example, by managing time, the pupils provide themselves with resources (time slots) for applying COr strategies. However, if pupils are not capable to effectively apply COr strategies (e.g., think of an own example that makes the learning contents more meaningful for them), applying support strategies will not help comprehension-oriented, self-regulated learning. That is why we assigned a critical role to knowledge about COr learning strategies. 2. Expected prior knowledge about learning strategies How do future teachers acquire knowledge on “how to learn”? They acquire it already during their school experiences (Smith, diSessa, & Roschelle, 1993; Kali, Goodyear, & Markauskaite, 2011). They then learn about it in subject matter education courses (e.g., mathematics) and educational courses during teacher education, and struggle with interconnecting these knowledge sources (Ball,

2000; Harr, Eichler, & Renkl, 2014; Leinhardt & Smith, 1985; Seidel, Blomberg, & Renkl, 2013). Hints from colleagues and experience with their own children might supplement this fragmented knowledge. These assumptions are supported by the finding that practicing teachers refer to their own school experiences, experiences with their own children, and hints from colleagues in order to assess and make decisions about their teaching (Hargreaves, 2000; Stark, 2005). Teachers rarely systematically compare, align, or integrate their knowledge from different sources (e.g., Harr et al., 2014). This lack of knowledge integration results in “knowledgein-pieces” (e.g., Kali et al., 2011). 2.1. Fragmented prior knowledge: knowledge-in-pieces Prior knowledge that is fragmented and partly incompatible with scientific concepts is increasingly conceptualized as “knowl€ € edge-in-pieces” (diSessa, 1993; Ozdemir & Clark, 2007; Ozdemir, 2013) also with regard to teacher knowledge (Ashe & Bibi, 2011; Harlow, Bianchini, Swanson, & Dwyer, 2013; Hopper, Sanford, & Bonsor-Kurki, 2012; Kali et al., 2011; Ohst et al., 2014, 2015; Orrill & Brown, 2012). The individual pieces of knowledge-in-pieces are atomic knowledge elements that originate from different everyday sources. These pieces are not embedded in an overarching knowledge system, but remain relatively isolated (diSessa, 2002). Their activation and retrieval is context-dependent (Ashe & Bibi, December 2011; diSessa, 2002). Due to this characteristic, people may retrieve different, possibly incoherent knowledge pieces in different contexts (situations) without being aware of the inconsistencies (diSessa, 1993). In fact, initial research suggests knowledge-in-pieces in teachers' pedagogical knowledge: a) knowledge is activated contextdependently: Teachers were sometimes unable to rely on knowledge available in other situations (e.g., Carraher, Carraher, & Schliemann, 1985; Kali et al., 2011; Orrill & Brown, 2012). b) Knowledge is inconsistent: teachers’ thoughts about one specific teaching situation contradicted those about another situation (Eley, 2006; Kane, Sandretto, & Heath, 2002; Postareff, Katajavuori, Lindblom-Yl€ anne, & Trigwell, 2008). We are unaware of any studies specifically investigating knowledge-in-pieces regarding the domain of learning strategies. Clift et al. (1990) provided deep insight into teachers’ knowledge about learning strategies, but they did not focus on different contexts or inconsistencies across answers. Student teachers might reveal inconsistent pieces of knowledge in this domain in different contexts. For example, they might talk about teaching strategies when actually asked about learning strategies. This particular case is likely as teachers focus intensively on their teaching rather than on pupil thinking (Yeh & Santagata, 2015; in terms of a curriculum script:; Putnam, 1987; and in terms of teacher explanations:; Chi, Siler, Jeong, Yamauchi, & Hausmann, 2001; Herppich, Wittwer, Nückles, & Renkl, 2013). However, the same student teachers might refer to learning strategies when asked within a different context, for example, one that is clearly focused on pupils. 2.2. Incorrect prior knowledge: misconceived pieces For incorrect prior knowledge, we adopt a theoretical stance combining assumptions of knowledge-as-theory (e.g., Chi, 2008; Vosniadou, 2008) and knowledge-as-elements (Clark, 2006; € diSessa, 1988) perspectives (they share similarities, see Ozdemir & Clark, 2007). We assume that prior-knowledge pieces can be misconceived (called “incorrect” above) or (scientifically) correct. Misconceived knowledge is incompatible with normative knowledge (Chi, 2008; Vosniadou, 2008). A special case of misconceived knowledge is miscategorization (Chi, 2008; Davis, 2004).

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Miscategorization occurs when novices categorize concepts in different ways than experts (scientists). For example, a novice could think she knows that a coral is a plant rather than an animal. Findings by Clift et al. (1990, p. 260) can be interpreted as miscategorizations: the teachers mentioned teacher-regulated activities and processes belonging to a task, such as decoding words while reading, when asked for learning strategies. That is, they categorized basic information processing or non-self-regulated strategies as learning strategies (showing “lateral miscategorization”, Chi, 2008). Both cases are out of line with our minimal definition (learning strategies are regulated by learners; they are not basic information processing, but directly related to building understanding).

3. Indicator for knowledge-in-pieces The question whether student teachers' knowledge reveals characteristics of knowledge-in-pieces can be investigated by providing systematically varied contexts and asking learners about the same concept each time (e.g., diSessa, Gillespie, & Esterly, 2004). If the concept is explained differently across contexts, knowledge is activated context-specific, indicating knowledge-inpieces. Comparing two characteristics of activated knowledge across contexts can reveal differences: (1) Contents, that is, the amount of correct and misconceived pieces; (2) Structured-ness, that is, the coherence (conclusiveness, interconnectivity) of knowledge (cf. Kintsch & van Dijk, 1978; de Jong & FergusonHessler, 1996). Structured-ness is an important characteristic of knowledge which separates expert knowledge from novice knowledge (Chi, 2006; Putnam & Borko, 2000; Seidel et al., 2013; Sternberg, 1981). Differences regarding contents and structure of knowledge across contexts would indicate knowledge-in-pieces.

4. Knowledge-in-pieces hampers learning When student teachers had little knowledge about learning strategies, they could be simply taught correct knowledge. However, if they possess knowledge-in-pieces including pieces incompatible with scientific conceptions (i.e., misconcepts), simply communicating the scientifically correct knowledge can be ineffective (Tillema & Knol, 1997). Prior knowledge-in-pieces adheres to the various contexts in which the pieces originated. Hence, a lesson explaining learning strategies could merely add another context with another body of knowledge pieces. In addition, misconcepts can be very plausible in everyday life and thus be resistant to change (Sinatra, Brem, & Evans, 2008). Accordingly, we found that student teachers in their fifth semester or above did not differ from those in their first semester regarding misconceived knowledge (Glogger-Frey et al., in press; cf.; Vosniadou, 2008). That is, even when students were instructed about learning and teaching during several semesters, they kept their misconcepts. Assigning objects or processes to a category allows us to infer important characteristics of the object or process. Thus, in the case of miscategorizations, erroneous categorical inferences can be triggered, hampering further learning (Chi, 2008). For example, when teachers who categorize teaching strategies as learning strategies are instructed about evaluating pupils’ learning strategies, they might infer that the teacher maintains control over the strategy use (Askell-Williams et al., 2012). Thus, they might miss the importance of letting pupils find their own examples and instead infer that they are obliged to provide pupils with examples.

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5. Content and structured-ness of knowledge as a prerequisite for learning to assess pupils' learning strategies “Correct” conceptual knowledge contents, such as knowing what learning strategies are, what kinds of strategies can be distinguished (cf. Weinstein & Mayer, 1986, pp. 315e327) and what the functions of these strategies are (Glogger, Schwonke, Holz€ apfel, Nückles, & Renkl, 2012; Leutner, Leopold, & Den Elzen-Rump, 2007; McMaster & van den Broek, 2014), should be an important prerequisite for learning to competently assess pupils' learning strategies (Klug et al., 2013). We base this assumption on the conceptual model on teachers’ assessment competence (Herppich et al., this issue; Koeppen, Hartig, Klieme, & Leutner, 2008; cf. Xu & Brown, 2016,1). Accordingly, we define assessment competence as a cognitive disposition composed of knowledge structures. This disposition enables teachers to perform adequately in assessment situations. In these situations, several steps of an assessment process are executed (e.g., gathering data, analyzing data, etc.). Given adequate knowledge structures, teachers should execute each of these steps very capably, for example, the step of noticing (van Es & Sherin, 2002) and assessing learning strategies in pupil work. Correct and coherent conceptual knowledge should enable student teachers to apply such knowledge (Borko, 2004; Bromme, 2001; Putnam & Borko, 2000). In the present study, the pupil work consisted of authentic extracts from learning journals. In learning journals, pupils can write down reflections about preceding lessons as a follow-up course work so that learning strategies or the lack of applying them become “visible” (Glogger et al., 2012; Nückles, Hübner, & Renkl, 2009). Thus, teachers can assess the strategies formatively. For example, a teacher can see that a pupil noticed an own problem in understanding and wrote down a question to clarify this issues in the next lesson (metacognitive strategy, see further examples in Appendix A). While “correct” and coherent knowledge pieces should clearly be a prerequisite for learning-strategy assessment, misconceived pieces can lead to erroneous conclusions and hamper high-quality assessments (cf. Ell et al., 2012). However, there are instructional methods addressing misconceived knowledge-in-pieces that can reduce or even alleviate such negative effects. 6. Instruction “treating” misconcepts and knowledge-inpieces Special instructional methods can avoid negative effects of knowledge-in-pieces including misconceived pieces in further learning. Two special methods are the following: Refutational texts address a misconcept and contrast it with the scientifically correct concept (Sinatra & Broughton, 2011). More specifically, a misconcept believed to be commonly-held is introduced in words such as “many people think …”. Then the refutational statement states that the misconcept is, in fact, false and the scientifically accepted concept is presented as a contrast (e.g., Braasch, Goldman, & Wiley, 2013; Guzzetti, 2000; Tippett, 2010). The second method is a recategorization scheme similar to Kalyuga's categorial scheme (Kalyuga, 2013). It helps learners to re-organize their prior knowledge and sort inconsistent pieces of knowledge into better fitting categories (diSessa & Wagner, 2005; Ohst et al., 2014, 2015; Ozdemir, 2013). For example, categories commonly confused such as teaching strategies (cf. Chi et al., 2001; Herppich et al., 2013; Ohst

1 A related construct is assessment literacy. This term is much more common outside of Central Europe and can be defined as teachers' “understandings of the fundamental assessment concepts and procedures” (Popham, 2011, p. 265). More details and a demarcation of the constructs are given in Herppich et al. (this issue).

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et al., 2014, 2015) are contrasted with the category “comprehension-oriented learning strategies”. Learners can use the categories to sort their knowledge pieces. Both methods were combined in an introductory phase of a short training session carried out in the present study. 7. Research questions The present study aimed to contribute to teacher education by providing insights in student teachers' prior knowledge about learning strategies and in its role as a prerequisite to learning to assess learning strategies. More specifically, we measured several characteristics of student teachers’ prior knowledge, tested whether their knowledge is activated in a context-sensitive manner, and whether the prior-knowledge characteristics are predictive for assessment performance to address the following research questions: (Q1) Description of Prior Knowledge Contents: What priorknowledge contents about comprehension-oriented learning strategies do student teachers exhibit? (Q2) Knowledge-in-Pieces-Question: Do different situational contexts lead to substantial variations in student-teachers’ activated prior knowledgedindicating knowledge-in-pieces? (Q3) Knowledge-Performance Relation: Are different priorknowledge characteristics (namely, correct and misconceived contents, coherence) related to assessment performance using authentic pupil work. We expected “correct” and coherent knowledge pieces (units of conceptual knowledge) to relate positively to learning-strategy assessment. We had no expectations regarding the direction of the effect concerning misconcepts as they can hamper learning, but the special instruction used in this study might have attenuated detrimental effects.

8. Method

context was described in a single sentence and comprehension was always the learning goal (e.g., “A pupil wants to understand a biological process (e.g., cell division)”). Please see Table 1 for further examples of the context questions and the full instructions. Below the situation description, the same question was posed each time: “Do you see the application of or the potential to apply a comprehension-oriented learning strategy in the present context?” Participants were forced to choose yes or no. They were then asked to justify their answer in writing (cf. Table 1). If they answered yes, they were also asked to explain which strategies. The justification was coded and analyzed as described in the paragraph “Prior knowledge characteristics: content and structure”. While the context differed, COr strategies could always be mentioned as correct answers. If student teachers had fragmented knowledge-in-pieces, they should activate different knowledge pieces across the two types of contexts. To answer the Knowledge-Performance-Relation question (Q3), participants worked on a performance test requiring the assessment of learning strategies. Prior to that, they had completed a short training session on strategy assessment (cf. Fig. 1). 8.3. Demographic questionnaire A demographic questionnaire assessed gender, age, number of semesters in teacher education, combination of majors, teaching experience (no or yes with indication of weeks), self-judgement about learning-strategies knowledge (11-point scale; 0: very low to 100: very high), and experience assessing learning strategies. Participants filled in the questionnaire before answering the learning-strategy question in different contexts. 8.4. Prior knowledge characteristics: content and structured-ness The prior knowledge represented in answers to different contexts could differ in contents, that is, in the number of correct pieces or misconceived pieces, as well as in structured-ness (de Jong & Ferguson-Hessler, 1996). We developed the following instruments to measure these two knowledge characteristics.

8.1. Participants We recruited 47 student teachers (sex: 36 female, 11 male; Mage ¼ 23.2, SD ¼ 3.5, range from 17 to 35 years) from several teacher-education classes at one University in Germany [details removed for blind review]. These students were in different semesters (median: 5 semesters, range 1e14 semesters). They were studying diverse combinations of majors such as languages, natural sciences or sport sciences, and being trained to teach at the secondary level (highest track in the German school system, “Gymnasium”). About half of them (53.2%) reported having had teaching experience (overall M ¼ 8 weeks, SD ¼ 10.64; range 0e48 weeks). About a fifth (19.1%) indicated having had experience assessing learning strategies in the context of university seminars or lectures. 8.2. Design To answer the Knowledge-in-Pieces-Question (Q2), we conducted a within-subjects experiment by providing participants with systematically varied contexts (short descriptions of learning situations) and asked them whether COr learning strategies could be applied. Specifically, we varied whether the context focused on (A) a pupil (e.g., “A pupil tries …”) or on (B) a teacher (e.g., “A teacher has the pupils …”). All participants worked with all contexts in the same order. The contexts A and B were provided in alternated fashion, back-to-back, but each on a separate page of a computer-based questionnaire (Design: ABABAB, see Fig. 1). Each

8.4.1. Content of student teachers' knowledge: coding scheme We coded the answers to the experimentally varied contexts with regard to learning strategies and misconcepts. An existing coding scheme (Glogger-Frey et al., in press) was adapted to fit the present data. The existing categories were deduced from theories on comprehension-oriented learning strategies (Boekaerts, 1999; Dansereau, 1985; Weinstein & Mayer, 1986, pp. 315e327), complemented by categories defined bottom-up. The concrete categories in this study were adapted bottom-up, based on the data in a qualitative process (see Chi, 1997). The final category scheme is presented in Table 2. The main categories with which the analyses were executed summarize (a) comprehension-oriented learning strategies (e.g., elaboration), (b) support strategies (Dansereau, 1985) not directly related to comprehension-building, and (c) misconcepts (e.g., teaching strategies were mentioned as learning strategies). The main category Comprehension-oriented learning strategies was defined as mental activities that are regulated by the learner and directly related to understanding the subject matter (see Table 2 for examples). The second main category was named Support, not comprehension-oriented learning strategies. They were still defined as mental processes and regulated by the learner. However, they are not directly related to understanding subject matter, but instead to creating a context that fosters learning (resource strategies) or to procedural learning (support strategies as in Dansereau, 1985). For example, Creating a pleasant atmosphere can be regarded as a

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Demographics

Answering Questions to Varying Contexts:

Q 3: KnowledgePerformance Relation

Q 2: Knowledge-In-Pieces Question

A Pupil-focused B Teacher-focused

all data

Data pupil-focused

A Pupil-focused

vs.. v.s

B…

Data teacher-focused

A… B… Short Training Intro Refutational text + Re-categorization scheme

Modules on 1) Elaboration 2) Organization 3) Metacognitive monitoring and planning

Assessment Performance Test Fig. 1. Design, procedure and research questions (Q). To answer Q2, we aggregated data from all three pupil-focused contexts as well as from all teacher-focused contexts, respectively. These two data sets were compared (within participants). To answer Q3, data from all context questions were aggregated and related to data from the assessment performance test in regression models.

Table 1 Full Instructions to the Prior Knowledge Questions and two Example Contexts of the ABABAB Design. Introduction.

Question part. This was the same for every context

We are interested in your understanding of learning strategies. Explain your understanding to us, that is, please answer in full sentences to the following questions. In case you feel like you need to repeat yourself, please do so and write out your answer again. Do you see the application or the possibility of applying a comprehension-oriented learning strategy in the present context? Yes No

If yes, explain which and why. If no, explain why not. A (pupil-focused) Example Contexts. Each context was presented on a separate page together A pupil would like to understand a new calculation rule. S/he has the newly learned rule and several worked calculation problems at hand. with the question part B (teacher-focused) A teacher has pupils read a historical text individually first, then discuss it in small groups, so that pupils understand it better.

support strategy (Dansereau, 1985; Friedrich & Mandl, 1997) that can indirectly support comprehension-oriented learning by enhancing the learning situation's conditions. The main category Misconcepts was defined as not entirely mental or not regulated by the learner and not directly related to understanding subject matter. We decided that if an answer offered

no semantic hints that the participant understood learning strategies as mental processes, the person's prior knowledge did not yet clearly represent the core characteristic of learning strategies. If a participant mentioned an activity clearly regulated by the teacher, we coded a Teaching strategy (e.g., “showing weaker pupils how they can approach physical problems”). Miscategorizing a teaching

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Table 2 Category scheme. Sub-categories

Description

Examples

Comprehension-Oriented Learning Strategies Mental þ regulated by learner þ directly related to understanding and learning the subject matter Elaboration

Organization Metacognition Generation Knowledge-Application Strategies

deepening and working out learning content through illustrations and by building connections to prior knowledge Summarizing and structuring, getting an overview Planning and monitoring of the learning process and comprehension, remedial strategies Draw conclusions, create new knowledge, abstract sth. Applying knowledge

“explain contents to oneself with examples”, “deepening understanding by visualizing“ “Arranging in a scheme”, “Writing a summary” “Look up unknown words” „He/she can check his/her understanding so far by recapitulating the text in sections” “create comparisons”, “develop one's own approach” “Learning by application of knowledge”, “transfer to new situations”

Support (and not Comprehension-Oriented Learning) Strategies Mental þ regulated by learner þ not directly related to understanding and learning the subject matter, but related to procedural learning or to creating a context fostering learning Verbal or written repetition of content, consolidation of “learning contents through repetition”, associations “through rehearsal of the process”, “the rehearsal … helps the pupil to imprint the topic” Mnemotic Strategies Strategies supporting storage of exact wording of “mnemo strategies”, “mentally deposit the phases of cell-division on a memory learning content route” Practicing Strategies concerning the compilation & automatization “become skilled at solving mathematical problems” of procedural knowledge Learning in Social Context To deal with the learning content within a dyad or a “compare thoughts with others”, “To explain something to others” group Resource Strategies: Time Contextual factors supportive of the application and “planning units of learning”, “turn off the internet connection to control the learning regulation of COr learning strategies environment and avoid sources of distraction ” Management, Motivation, Study Environment Strategies Bundle Method enabling the application of several COr“Mind mapping”, “Keeping a learning diary” strategies (no mental functions were mentioned)

Rehearsal

Misconcepts Not entirely mental, or not regulated by learner þ not directly related to understanding and learning subject matter Teaching Strategies

Tools Multi-Sensory Learning Other

Strategies the teacher employs in class in order to foster “Showing weaker pupils how they can approach physical problems”, “Having pupils learning (not regulated by learner) role-play” “I would give pupils a short text from the textbook covering the basics of the biological process” Means helping or systemizing learning, but no cognitive “Using index cards”, “a video could help” activity is mentioned (not mental) Combining several physical senses “Learning through as much senses as possible”, “visually and auditory” Statements that failed to answer the question or were too “The strategy is both to learn the phases of cell division and understand them. This vague to assign them to a category ensures long-term knowledge and understanding rather than mere memorization.“ “To understand cell division, several steps are necessary that must be interconnected, because it is a quite complex process“

strategy as a learning strategy can be damaging. To notice and foster learning strategies, it is essential to see learning strategies as activities that are ultimately initiated and regulated by the learners themselves (Askell-Williams et al., 2012). Parts of answers that were very vague and did not contain a cue to their classification to one of the strategy or misconcept categories were assigned to the category Other. Such answers often referred to the importance of comprehension or self-regulated learning as a whole. They thus did not address our question whether learning strategies were applicable in that situation (for examples, please see Table 2). Two raters coded 20% of the answers. Interrater reliability was satisfactory (Krippendorff's Alpha ¼ .872; Hayes & Krippendorff, 2007). 8.4.2. Structured-ness of student teachers' knowledge: Coherence The structured-ness of answers to each context was rated on a 6-point scale (1 ¼ very incoherent to 6 ¼ very coherent). We considered two criteria for this rating. The criteria were theoretically grounded in the literature on learning from texts (Lee, 2002).

2 Krippendorff's Alpha values are interpreted equivalent to other reliability measures. It avoids weaknesses of existing measures and is suitable for a broad use to measure rater agreement.

A text's cohesion is expressed by the relationship between (two) consecutive propositions or sentences (Foltz, Kintsch, & Landauer, 1998; Kintsch & van Dijk, 1978; van Dijk, 1992). Hence, we took the cohesion of participants' answers as an indicator for the coherence of the body of knowledge pieces underlying one answer. The first criterion for knowledge coherence was whether consecutive propositions and sentences in an answer (to one context) were closely related or interconnected, be it through conjunctions or semantically. The second criterion was the semantic conclusiveness, the comprehensibility of the propositions. Raters considered both criteria for their very inferential rating.

8.5. Training treating knowledge-in-pieces with misconceived pieces In the present study, all participants underwent a tried and tested short training session treating knowledge-in-pieces including misconceived pieces so that deleterious effects of such prior knowledge should be eliminated. First, the importance of assessing and fostering pupils' learning strategies was explained. A definition was provided: Comprehension-oriented learning strategies are mental processes that directly refer to learning. They are initiated by the learners themselves and help to actively build

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knowledge and (deep) understanding. The learning goal was explained as differentiating learning strategies from strategies that are often confused with learning strategies. Then, the special instructional methods refutational statements and re-categorization scheme were applied in combination (Ohst et al., 2014, 2015). Refutational statements took up a misconcept (a misconceived knowledge piece) stating that it “is often considered a learning strategy, BUT is not a learning strategy”. Usually, a refutational statement is followed by simply presenting the scientifically accepted concept as a contrast. Instead, we introduced the misconcept as an alternative category of a re-categorization scheme. For example, a refutational statement introducing the alternative category “teaching-strategy” in contrast to the category “learning strategy” was: “Some people consider teaching strategies to be learning strategies. HOWEVER: comprehension-oriented learning strategies are initiated by the learners themselves. Teaching strategies are methods used to control the learners' learning processes. They are initiated by the teacher and are not mental processes.” (cf. Ohst et al., 2015; emphasis in original). As can be seen in the example, the above definition was used to briefly explain which defining criteria of a learning strategy the misconcept fails to meet, that is, to distinguish the categories. The misconcepts were chosen on the basis of analyses of student-teachers’ prior knowledge in Glogger-Frey et al. (in press, e.g. teaching strategies, tools for learning; cf. categories in Table 2). An overview of the different categories together with examples completed the introductory phase (13 min). Finally, student teachers were asked to summarize in text boxes what the most important thing was that they had learned. By implementing the two instructional methods in the introductory phase, prior knowledge (knowledge-in-pieces) was re-organized, learners could be made aware of and re-categorize misconcepts. A clearer, more accurate and better structured knowledge representation about learning strategies was initiated. After this introductory phase, the student teachers learned within an interactive learning environment with audio and animation how to assess COr learning strategies (ca. 19 min). Following the learning strategy model described in our introduction, it contained a module for elaboration, organization, and metacognitive monitoring and planning. The learning environment defined each COr strategy with supporting visual input (partly animated) and presents excerpts from learning journals with examples of each COr strategy. Towards the end of the modules, the tasks required that learners choose the best-fitting explanation on why a strategy can be identified in extracts. Elaborated feedback was provided. The learning environment was developed according to the principles of example-based learning and multi-media learning (Mayer, 2001; Renkl, 2014). Glogger et al. (2013) provide a detailed description of the learning environment. 8.6. Learning-strategy assessment: performance test An assessment-performance test asked student teachers to explain which COr strategies could be identified in 6 pieces of pupil work (Cronbach's a ¼ 67). The pupil work consisted of excerpts from learning journals. In writing the learning journals, high school pupils were instructed to reflect about learning contents through writing and to apply learning strategies while writing (Nückles et al., 2009). Learning contents were about probability (subject: mathematics). Pupils received one lesson (45 min) that covered the writing of a journal. They then wrote the journals as homework, using a learning-strategy-prompts card. The card showed four steps and their prompts: (1) metacognitive monitoring, (2) elaboration, (3) organization, and (4) again metacognitive monitoring (please see details in Glogger et al., 2012; example: “Organize: what are the most important concepts, formulas and rules?”, “build connections

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…: Can you think of your own example?”). The six excerpts were chosen for the performance test to contain one or more COr strategies (10 strategies in total), or none (i.e., “distractor excerpt”). As Appendix A makes clear, student teachers had to decide whether they identified any learning strategies in the excerpts, and which ones they identified. They then had to explain their answers (Appendix A contains translations of two performance test items). We measured the quality of the assessment explanations via a selfdeveloped coding scheme (Criteria: capacity: are all critical aspects incorporated [strategies noticed, strategies' characteristics or functions named, and corresponding references to extract made]?; consistency and closure: are the aspects consistently interconnected, are explanations conclusive, no precipitous conclusions?, Biggs & Collis, 1982). Twenty-two points were attainable (up to two points per well-explained strategy or good explanation as to why the distractor is not a strategy). Two independent raters coded 20% of the explanations (not adjusted ICC ¼ .97). As the interrater reliability was very good, a single rater coded the remaining 80% of the explanations. 8.7. Procedure The entire study was computer-based. All participants signed an agreement to participate in this study and they agreed that their anonymized data could be used. As depicted in Fig. 1, the student teachers started by answering the demographics questionnaire. They then answered the learning strategy question in the varying contexts (design: ABABAB). Next, they partook in a short training session addressing knowledge-in-pieces including misconceived pieces without a time limit (31.69 min on average, SD ¼ 4.60 min). Finally, student teachers worked on the learning-strategy assessment test. Overall, the duration of the study averaged 82.51 min (SD ¼ 26.59 min). Participants received 15 Euros and a link to a full version of the computer-based tool for training and supporting learning-strategy assessment (see Glogger et al., 2013). 8.8. Data analyses To answer the Knowledge-in-pieces-Question (Q2) we compared the data obtained from the pupil-focused contexts with the data from the teacher-focused contexts (see Fig. 1 for an overview). That is, we calculated mean values of all prior knowledge measures regarding the three answers to pupil-focused contexts and, separately, regarding the three teacher-focused contexts. As this was a within-subject comparison, we conducted the pairedsamples t-test to compare the two means of each of the knowledge-characteristic measures: the number of COr strategies, number of support strategies, number of misconcepts, and coherence. To test whether teaching experience moderates the effects, we looked for any significant interactions between the contexts (as within-subjects factor in a GLM) and teaching experience (as moderator). To answer the Knowledge-Performance-relation question (Q3), we conducted regression analyses, predicting (learning-strategy-) assessment performance by the knowledge measures. We applied a hierarchical regression to proceed theoretically. We controlled for effects of teaching experience by including this variable in all models. We followed the hierarchy described below and included variables only if they enhanced the predictiveness of the model significantly (i.e., significant change in R2). The knowledge about COr strategies should be the central basis on which to assess COr strategies in pupil work. The structured-ness of knowledge (coherence) should be the next important characteristic of knowledge. Thus we first calculated a model with the number of COr strategies, then added structured-ness (see Table 3 for an

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Table 3 Overview of regression models predicting assessment performance.

Model 1 Constant COr strategies Teaching Experience Model 2 Constant COr strategies Teaching Experience Coherence Model 2a Constant COr strategies Teaching Experience Misconcepts Model 3 Constant COr strategies Teaching Experience Misconcepts Support strategies

R2

Significance of change

.17

.015

.22

.25

.25

B

SE B

b

6.53 0.44 0.08

0.77 0.24 0.05

.27 .23

10.97

2.81

0.08 1.28

0.05 0.78

.23 .28

4.83 0.51 0.08 0.63

1.11 0.24 0.05 0.30

.32* .24 .28*

4.59 0.49 0.09 0.61 0.21

1.22 0.25 0.05 0.31 0.44

.30 .26 .27 .07

.109

.044

.634

Note. N ¼ 47. *p < .05.

overview). Misconceived pieces of prior knowledge could well explain some part of assessment performance even when it is unclear whether they have a negative effect. This was entered in the next step. Knowledge about support strategies should not help nor harm during the task of assessing COr strategies in pupil work. We entered support strategies in the last step. 9. Results A significance level of .05 was used for all analyses. We used d as an effect-size measure with values between .20 and .50 classified as small, between .50 and .80 as medium, and values > .80 as large (Cohen, 1988). The explained variance R2 served as the effect size of the regression models (.01 was classified as small, .09 as medium, .25 as large). 9.1. What knowledge contents about comprehension-oriented learning strategies do student teachers exhibit (Description of Prior Knowledge Contents, Q1)? We investigated what knowledge student teachers demonstrate when asked which comprehension-oriented learning strategies are applicable in several contexts (Q1). A third (30.4%) of the codings of teachers’ answers referred to COr learning strategies. A quarter of codings (25.6%) were support strategies, another quarter (25.4%) were misconcepts, and 18.6% were classified as “Other”. 9.1.1. Example answers To give some insight into how student teachers answered, we present several examples along with the assigned codes. We selected examples from the most important and interesting main categories COr strategies and misconcepts. Short examples of each sub-category of all main categories are given in Table 2. In the following, Participant 1, in referring to the context of understanding a mathematical rule and given some worked problems (see Appendix A), describes a COr strategy, coded as generation strategy: (1) “Yes. You can re-work through the worked problems and try to understand how such problems are solved. You can try to generate connections between the newly learned rule and the problems' solution style.”

Participant 2 explains another COr strategy, namely an elaboration strategy, in detail (context: understanding the process of cell division, see Design section): (2) “If a pupil writes a learning diary, he/she is encouraged to activate his/her prior knowledge. Apparently, he/she is already motivated to understand the process of cell division. Now, the question is how he/she can best do this. By answering the question as to which knowledge should be relied upon, he/she will attain deeper understanding by realizing in general what a cell is made up of. After that, he/she can speculate (anticipate) how the cell division might proceed, what that process might benefit etc.“ The following set of examples contrasts answers in the same context, namely understanding a historical text in groups. Participant 3a briefly mentions an elaboration, organization and metacognitive strategy. The other two examples 3b and 3c show that the student teachers think they are talking about a learning strategy when they are actually discussing a teaching strategy (group work). To understand our interpretation, it is important to remember that participants first ticked the “Yes” box, meaning that they perceived the possibility of applying a learning strategy in this context. The answer is their explanation of their “Yes” answer. (3a) “Important points and topics are frequently addressed in conversation with other pupils. Pupils can express the read text in their own words and explain it to their classmates. Problems with comprehension can be clarified.” (3b) “Yes, group work can also be a learning strategy.“ (3c) “All this makes sense only when the teacher supervises the discussion and ensures that the discussion does not veer off course. The teacher should make sure that all pupils understood the text correctly. Generally speaking, it is not a bad idea to let pupils discuss a text, because that can help different perspectives to develop.” The Participant 3c took group work as the learning strategy and explained in greater detail how it would be effective. No hint is provided that the participant thinks a learning strategy as being regulated by the pupil (instead of the teacher). Thus, 3c was coded as the misconcept “teaching strategy”.

9.2. Does the situational context lead to substantial variations in student-teachers’ activated knowledgedindicating knowledge-inpieces (Q2)? Means and standard deviations of learning-strategy-knowledge variables in both conditions are found in Table 4. We detected large variations in the activated knowledge depending on the context. In pupil-focused contexts, participants mentioned significantly more

Table 4 Means (standard deviations) of learning-strategy-knowledge variables in both contexts.

Coherence COr strategies Support strategies Misconcepts

Pupil-focused M (SD)

Teacher-focused M (SD)

3.06 0.65 0.18 0.33

2.80 0.26 0.33 1.00

(1.01) (0.52) (0.27) (0.31)

(0.84) (0.30) (0.20) (0.40)

Note. N ¼ 47. COr ¼ comprehension-oriented. Scale for coherence ranged from 1 (very incoherent) to 6 (very coherent). The other, empirically found ranges were: COr strategies 0e2, support strategies 0e1, misconcepts 0e1.7.

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COr strategies, t(46) ¼ 5.42, p < .001, d ¼ 0.90 (large effect). This effect was moderated by teaching experience (main effect contexts: F(1,45) ¼ 8.24, p ¼ .006, d ¼ .51, medium effect; interaction effect: F(1,45) ¼ 6.56, p ¼ .014, d ¼ .45, small effect). The more teaching experience a student teacher had, the more COr strategies were mentioned in the pupil-focused contexts. This was not the case in the teacher-focused contexts, where participants mentioned fewer COr strategies regardless of their teaching experience (no participant mentioned more than one COr strategy on average). In this case, the interaction can be interpreted as follows: Only in pupilfocused contexts were all prior knowledge pieces about COr strategies activated in the more experienced participants, but overall knowledge was activated context-sensitively in all student teachers. Participants also mentioned fewer misconcepts in pupilfocused contexts than in teacher-focused contexts t(46) ¼ 10.58, p < .001, d ¼ 1.87 (large effect). We observed no interaction effect from teaching experience (p ¼ .132). Participants mentioned fewer support strategies in pupilfocused contexts, t(46) ¼ 3.37, p ¼ .002, d ¼ 0.66 (medium effect). The interaction with teaching experience was significant, but minor, revealing that the main effect of contexts is a bit more pronounced in student teachers with no or little teaching experience (main effect contexts: F(1,45) ¼ 16.76, p < .001, d ¼ .80, large effect; interaction effect: F(1,45) ¼ 4.73, p ¼ .035, d ¼ .28, small effect). Coherence tended to be higher in pupil-focused contexts, t(46) ¼ 1.92, p ¼ .058, d ¼ 0.27 (small effect). We detected no interaction effect in conjunction with teaching experience (p ¼ .561). For exploratory purposes, we analyzed whether the knowledge measures in the pupil-focused contexts correlated with the teacher-focused contexts. No or only weak correlations were additional evidence of knowledge-in-pieces because activated knowledge in one context would be (relatively) unrelated to activated knowledge in another. We observed, in fact, at the most moderate or even no correlation between the measures (COr: r ¼ .36, support: r ¼ .09, and misconcepts: r ¼ .26). 9.3. Are the different knowledge characteristics related to assessment performance using authentic pupil products (Q3)? Table 3 offers an overview of calculated models, variance explained, and regression coefficients. Prior knowledge about COr strategies and teaching experience (Model 1) explained a significant amount of variance in assessment performance (R2 ¼ .17). Introducing the coherence of prior knowledge in the second step (Model 2) did not lead to a significantly enhanced model (in terms of a non-significant change in explained variance R2). We thus proceeded with comparing Model 1 with Model 2b, introducing misconceived knowledge (the number of misconcepts). This step led to a significantly enhanced model (p ¼ .044). Introducing support strategies in the last step did not improve the model (Model 3). Thus the model containing COr strategies as well as misconcepts (together with teaching experience) predicted assessment performance best, R2 ¼ .25, F(3,43) ¼ 4.72, p ¼ .006, explaining 25% of the variance in assessment performance (large effect). Correct conceptual knowledge about COr strategies (b* ¼ .32, p ¼ .036) and, surprisingly, misconceived pieces in prior knowledge (b* ¼ .28, p ¼ .044) predicted assessment performance significantly, given a short training session addressing prior knowledge-in-pieces (including misconceived pieces). To get an impression of what student teachers' assessment performance looked like, we provide examples in the following: Participant 1 identified an elaboration (i.a.) in item 2 (Appendix A). The explained evaluation reads: “This is an elaboration because the

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pupil is thinking of and working with his own example for the learning contents which [the elaboration] has the function of facilitating understanding and enriching and intensifying the rather abstract knowledge so far.” The quality of this assessment explanation was high (2 points). Participant 2 also identified elaboration (i.a., also organization). The following explanation does not clearly differentiate elaboration and organization: “First of all, the pupil develops an example. She arranges it more clearly by using different colors, a sketch and paragraphs. She organizes her knowledge and, at the same time, elaborates upon it by developing her own example.” Concerning elaboration, this second participant informs us that developing the pupil's own example is an elaboration. How or with which function is not explained further. Therefore, this explanation is lower in quality than the first example (1 point instead of 2). Participant 3 provides a high-quality assessment explanation on a metacognitive strategy (to item 6: “Somehow I cannot understand all that. I cannot get it into my mind.”): “The Monitoring becomes apparent in the evaluation of one's own understanding as insufficient. The reason for it does not seem to be well-thought-out and consists in the pupil's blaming herself, but the exact cause remains unclear.” This explanation is high in quality because it is consistent and conclusive, and relates the noticed strategy with the corresponding part in the extract. 10. Discussion Aim of this study was to make a contribution to teacher education by providing insights in student teachers’ prior knowledge about learning strategies. Additionally, we investigated whether such knowledge is a basis for acquiring assessment competence regarding learning strategies. At least a third of student-teachers’ prior knowledge was about COr strategies. However, our results still indicate the assumed deficits: a quarter of activated prior knowledge pieces were misconceived, and answers to questions embedded in systematically varied contexts revealed profound variations in the activated knowledge, suggesting knowledge-in-pieces. Pieces representing comprehension-oriented learning strategies and misconceived pieces predicted assessment performance using authentic pupil work. This study provides empirical evidence of knowledge-in-pieces by using an experimental “within” approach. Although the experimentally varied questions were asked back-to-back, without a delay, and COr strategies could have been the correct answer to each question, significantly different prior knowledge was evident in the student teachers' answers. Teaching experience partly moderated the differences, but not in a manner that would consistently lead to fewer differencesdmeaning less contextsensitivity of knowledgedhad the student teachers possessed more experience,. That is, we noted that the knowledge of all student teachers revealed context-sensitivity, a characteristic typical of knowledge-in-pieces (diSessa, 1993). According to the knowledge-in-pieces theory, student teachers’ pieces of knowledge have been generated in different situations in everyday experience, and have never been aligned and integrated into an overarching knowledge structure. Due to this lack of integration, student teachers activated differing knowledge pieces in different contexts (diSessa, 1993). Our observation that learning-strategy knowledge is in pieces concurs with findings in other domains of teacher education (Ball, 2000; Harr et al., 2014; Leinhardt & Smith, 1985; Orrill & Brown, 2012; Postareff et al., 2008; Seidel et al., 2013). Correct conceptual knowledge pieces about COr strategies were shown to be an important basis for learning strategies assessment. When teachers know COr strategies, they can better learn to assess learning strategies in their pupils’ work. This finding supports

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assumptions of theoretical models of assessment competence that assume knowledge (as measured in this study) as the basis for skillful action (Herppich et al., this issue; Xu & Brown, 2016). Conceptual knowledge about support strategies was not prioritized in this study. It would have been highly unlikely to identify support strategies in pupils’ learning journal extracts. Therefore, it is highly plausible that knowledge pieces with contents on support strategies did not serve as an important basis for assessing COr strategies (nor did they interfere with it). Knowledge coherence as measured in this study did not contribute to explaining how some student teachers were better able to assess learning strategies than others. This finding may be due to a drawback associated with our measure: the shorter answers are, the harder it is to rate the coherence. Future research should test a different measure. We were rather surprised by the relatively strong positive effect of misconceived knowledge on learning-strategies assessment. That misconceived knowledge pieces helped learning to assess learning strategies can be explained by the special instructional methods of the brief training session the student teachers received that used a refutational text and a re-categorization scheme. The effectiveness of refutational text can be explained, for example, by the concurrent activation of both the misconceived knowledge and the to-be-learned knowledge. Coactivation enables the learner to directly compare the knowledge pieces, ideally becoming aware of the conflicting information and then being able to integrate the new knowledge (Sinatra & Broughton, 2011; van den Broek & Kendeou, 2008). That the side-by-side comparison of misconceived and new knowledge can even assist learning is also supported by Oser and Spychiger's theory of “negative knowledge” €pflin, Hofer, & Aerni, 2012). Ac(Oser & Spychiger, 2005; Oser, Na cording to their theory, knowledge about wrong concepts (i.e., negative knowledge) can be useful if the incorrect knowledge is represented in connection to the corresponding correct knowledgedas is done with the refutational textdand best with knowledge about how to prevent applying the incorrect knowledgedas is done by providing alternative categories to differentiate the misconcepts from the learning strategies. In this case, negative knowledge helps to avoid errors by reminding the learner of potential error sources (Gartmeier, Bauer, Gruber, & Heid, 2010). The effectiveness of the re-categorization scheme can be further explained by mechanisms of re-organizing fragmented and incoherent knowledge and preventing learners from cognitive disorientation due to “too much (and conflicting) information at once” (cognitive overload, Mayer & Moreno, 2003). These processes explain why we did not find that misconceived prior knowledge hampered learning to assess learning strategies. The fact that misconceived pieces even benefited learning to assess learning strategies is in line with the resource perspective of € knowledge-in-pieces theory (diSessa, 1993; Ozdemir & Clark, 2007; € Ozdemir, 2013; Smith et al., 1993). The knowledge elements in knowledge-in-pieces theory, albeit context-sensitively activated, mal-categorized, or unorganized, are always regarded as a productive resource for learning. For example, imagine student teachers who know activities that facilitate learning, but conceive them as teacher-regulated. During instruction, they come to understand the point that learning strategies must be initiated and controlled by the pupil. They then understand that the activities they have known already can become learning strategies if pupils learn regulate them on their own. In this case, the knowledge pieces can indeed be useful when correctly re-categorized and associated with the newly learned, correct characteristics and contexts. In addition to misconceived pieces being resources, the nontrivial process of becoming aware of misconceived knowledge can

function as a motor for further learning (Glogger-Frey, Fleischer, Grüny, Kappich, & Renkl, 2015; Loibl, Roll, & Rummel, 2016; VanLehn, Siler, Murray, Yamauchi, & Baggett, 2003). Several findings suggest that successful learning requires that the learners reach an impasse (VanLehn et al., 2003). An impasse “occurs when a pupil realizes that he or she lacks a complete understanding of a specific piece of knowledge” (VanLehn et al., 2003, p. 220). Instructional explanations are more efficient if an impasse has been ~ a, reached or misunderstandings are made very explicit (Acun nchez, 2011; S García Rodicio, & Sa anchez & García-Rodicio, 2013; ~ a, 2009). The refutational stateS anchez, García-Rodicio, & Acun ments did indicate misunderstandings very explicitly, so that the following instructional explanations might have been processed more deeply in case participants possessed misconcepts. More specifically, participants might have learned more from the instructional explanations only if they possessed and activated their misconcepts by mentioning them in answers in varying contexts. That is, participants who wrote down their misconceived knowledge might have had more impasse experiences during the short training session, making the session more effective for them. 10.1. Implications for teacher education Overall, identified prior knowledge could be an important reason why strategy interventions are less effective when implemented by teachers rather than researchers (Dignath et al., 2008), and why teachers seldom instruct their pupils how to apply COr strategies (Durkin, 1978; Hamman et al., 2000; Moely et al., 1992). Therefore, we join the call to intensify instruction about learning strategies, their assessment, and training methods as part of teacher education (e.g., Archer & Isaacson, 1990; Hamman et al., 2000; Pressley & Allington, 2014). Our study findings about what knowledge student teachers bring to their teacher education courses can be used to tailor instruction to optimally foster learning strategies assessment. Our results suggest that such teacher education should adopt instructional methods from the conceptual change literature, such as refutational texts and re-categorization schemes to support the reorganization of knowledge (see e.g., Ohst et al., 2015; Slotta & Chi, 2006). In addition to the two aforementioned methods, knowledge-in-pieces can be comprehensively activated by providing different contexts, as occurs with the short descriptions of learning situations in this study. Broad activation of the different knowledge pieces facilitates their integration, alignment, and reorganization into a more coherent knowledge system (diSessa, 1988). As mentioned before, broad activation can also enhance the awareness of limits in understanding (Glogger-Frey et al., 2015; Loibl et al., 2016; VanLehn et al., 2003). A further reorganization method specifically for differentiating blurry or overly comprehensive concepts (Carey, 1991; Chi, 2008) or the opposite, that is, the coalescence of concepts, could apply contrasting cases. Contrasting cases are examples from the learning domain, designed to help learners notice information they might otherwise overlook. More specifically, they are designed so that key aspects of the to-belearned-material stand out. Comparing the cases supports the coalescence of concepts, contrasting the differentiation (for more detailed description, see, for example: Glogger-Frey et al., 2015 and Schwartz, Chase, Oppezzo, & Chin, 2011; Alfieri, Nokes-Malach, & Schunn, 2013; Bransford & Schwartz, 1999). 10.2. Limitations and future research We limited the focus of this study to COr learning strategies and developed different contexts referring to typical examples of miscategorization, namely learning strategies classified as teaching

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strategies. Although this is an important area, future research should broaden the examination and investigate other areas of selfregulated learning such as support strategies (Boekaerts, 1999). Similarly, future research could widen the spectrum of assessment. We provided student teachers with pupil data, that is, they had to do the “data analysis” in an assessment process (Herppich et al., this issue). It would also be interesting to investigate student teachers’ performance in choosing assessment goals as well as instruments, or in collecting data referring to self-regulated learning (Klug et al., 2013). Participants answered a question about the application of learning strategies in light of six contexts. The contexts were given in an alternated fashion (ABABAB), back-to-back. Starting with a pupil-centered context could have activated COr knowledge that was still partly activated when reading the next (a teacher-focused) context. This activation could have reduced the differences between contexts. Other effects of the order of contexts are possible. Thus, future research could give contexts in an order randomized across participants. Of course, further evidence is required on the theoretical assumption that teachers' knowledge transfers to the assessment of pupils’ data (confirming the model by Herppich et al., this issue). In the present study, student teachers underwent a training session, which provided an opportunity for future learning (Schwartz & Martin, 2004). Future research should also look at whether conceptual knowledge predicts assessment performance without such a learning opportunity. Besides, research on whether teachers can better assist pupils in improving their learning strategies (cf. Kersting, Givvin, Thompson, Santagata, & Stigler, 2012) when their assessment performance has improved is desirable. Enabling teachers to coach pupils in using learning strategies is a very important step toward turning pupils into self-regulated, life-long learners. 10.3. Conclusions This study provides evidence that student teachers bring knowledge-in-pieces in conjunction with a significant number of

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misconceived pieces to their teacher education course. Misconceived pieces can serve as resources for learning in the context of tailored instruction (instruction that supports the awareness of misconceived knowledge, re-categorization, and integration of isolated knowledge pieces). As we expected, “correct” conceptual knowledge forms a basis for student teachers' action in the sense of skillfully assessing pupils' learning strategies. This study demonstrates means of measuring suboptimal, fragmented knowledge (knowledge-in-pieces) which can be very useful in research that aims to promote theory about teachers’ professional knowledge. Overall, our results suggest that future teachers should undergo more training in strategy assessment (Askell-Williams et al., 2012), and that such training benefits from adopting instructional methods addressing knowledge-in-pieces, including misconceived pieces (see e.g., Ohst et al., 2014; Slotta & Chi, 2006). Acknowledgements We would like to thank Beatrice Fondue for assisting as a second coder and Carole Cürten for proof-reading and refining our English. Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.tate.2018.01.012. Appendix A Translation of two assessment-performance-test items These slides from the computer-based environment show excerpts from pupils’ learning journals containing COr learning strategies. The participants had to decide whether they identify learning strategies in the excerpts. If they answered yes, the participants had to write down which strategies they have identified (still on the first slide). Depending on the answer, the next slide asked for explanations of their answers as in (3) or (4), respectively.

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