Constructing Mental Models during Learning from Science Text

Mikkilä-Erdmann, Mirjamaija; Penttinen, Marjaana; Anto, Erkki; Olkinuora, Erkki

doi:10.1007/978-0-387-76898-4_4

Cited by 33 publications

(39 citation statements)

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“…At the same time, other research studies have failed to find positive effects for refutational texts either on retention (Kendeou, Muis, & Fulton, 2011;Kendeou & van den Broek, 2007;Mason et al, 2008;Mikkilä-Erdmann, 2001) or on learning of the scientific content (Alvermann & Hague, 1989;Broughton, Sinatra, & Reynolds, 2010;Hynd & Guzzetti, 1998;Kendeou et al, 2011;Kendeou & van den Broek, 2007;Mikkilä-Erdmann, Penttinen, Anto, & Olkinuora, 2008;Palmer, 2003). There are additional concerns that refutational texts might introduce misconceptions to readers who do not already hold them (Hynd et al, 1997).…”

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confidence: 85%

The influences of text and reader characteristics on learning from refutations in science texts.

Braasch

Goldman

Wiley

2013

Journal of Educational Psychology

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Three experiments examined conceptual change from reading refutational texts and how such learning interacted with prior knowledge organization. Prior to reading, 3 groups of learners were identified on the basis of their prior knowledge of the targeted concept: 2 groups held misconceptions; 1 group was generally accurate. Experiment 1 tested learning from a text that contrasted a misconception and the correct conception of the phenomenon of airflow against learning from a text that repeated the correct scientific description twice. The 2 reader groups learned from both types of texts about equally. Experiment 2 contrasted a more traditional refutational text to the "repetition" text. Learning was better with the refutational than the repetition text for both misconception groups on both measures. Experiment 3 demonstrated that learners who held largely accurate conceptions prior to reading texts that presented misconceptions preserved their largely accurate performance. Overall, the results suggest that the inclusion of an explicit refutation of the misconception is critical for instigating knowledge revision when readers possess inaccurate prior conceptions.

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confidence: 85%

The influences of text and reader characteristics on learning from refutations in science texts.

Braasch

Goldman

Wiley

2013

Journal of Educational Psychology

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“…However, previous studies have reported difficulties in understanding photosynthesis, and misconceptions concerning this phenomenon seem to be quite common (see, e.g., Ahopelto, Mikkilä-Erdmann, Anto, & Penttinen, 2011;Mikkilä-Erdmann, 2001Roth, 1990;Vosniadou, Vamvakoussi, & Skopeliti, 2008). Students' prior knowledge might act as a filter that prevents the construction of new knowledge-their everyday notions of food as a requirement for life might lead them to think, for example, that plants "eat" water and nutrients from the soil via roots (Mikkilä-Erdmann, 2001;Mikkilä-Erdmann et al, 2008;Mintzes & Wandersee, 2005;Roth, 1990;Vosniadou, Vamvakoussi, & Skopeliti, 2008). The problem with misconceptions is that they seem to be very resistant to change (Chi, 2005).…”

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confidence: 96%

“…Learning about topics from the natural world requires that the learner goes beyond knowledge acquisition and overcomes significant conceptual and affective obstacles (Sinatra & Mason, 2008). In addition to previous knowledge, the nature of text might also affect the learning of difficult scientific concepts (Mikkilä-Erdmann, Penttinen, Anto, & Olkinuora, 2008). Thus, understanding complex scientific phenomena seems to often require conceptual change, by which we mean the process whereby learners reorganize their cognitive structures according to scientific notions and abandon their misconceptions derived from everyday life (Duit & Treagust, 2003;Vosniadou, 1994;Vosniadou, Ioannides, Dimitrakopoulou, & Papadimetriou, 2001).…”

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confidence: 99%

The Role of Regulation and Processing Strategies in Understanding Science Text Among University Students

Vilppu

Mikkilä-Erdmann

Ahopelto

2013

Scandinavian Journal of Educational Research

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The aim of the study was to investigate the role of regulation and processing strategies in understanding science text. A total of 91 student teachers answered open-ended questions concerning photosynthesis before and after reading either a traditional or a refutational science text. After this, they also answered parts of the Inventory of Learning Styles. Understanding of photosynthesis increased after the text reading among all the students. However, deep and independent learners reached a better understanding than reproductive and support-dependent students. The refutational text seemed to promote understanding more than the traditional text. The study supports the assumption that personal characteristics play a role in learning complex scientific concepts. Further, refutational texts seem to facilitate high quality learning.The focus of this study is to integrate three research traditions: learning strategies, conceptual change, and text comprehension research, at university level among future elementary school teachers. Studying in teacher education requires working with and combining a huge amount of information from different domains and planning one's studies carefully in order to maintain the fast pace. It is also essential to monitor and evaluate ones learning processes in order to detect possible difficulties in understanding. It could be said that effective studying provides effective learning strategies, that is, deep processing of the learning materials and self-regulating one's learning (Vermunt

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“…Therefore, we argue that it is essential to identify economic, fast, reliable, and valid techniques to elicit and analyze these cognitive structures (see Ifenthaler 2008Ifenthaler , 2010c. Methodologies include standardized questionnaires and interviews, think-aloud protocols (e.g., Ericsson and Simon 1993), the assessment of log files or click streams (e.g., Chung and Baker 2003;Dummer and Ifenthaler 2005), eye-tracking measures (e.g., Mikkilä-Erdmann et al 2008), and Pathfinder networks (Schvaneveldt 1990;Durso and Coggins 1990), as well as mind tools (e.g., Jonassen and Cho 2008;Spector et al 2006). Accordingly, the possibilities for externalizing cognitive structure are limited to a few sets of sign and symbol systems (Seel 1999b)-characterized as graphical-and language-based approaches (Ifenthaler 2010c).…”

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confidence: 99%

Identifying cross-domain distinguishing features of cognitive structure

Ifenthaler

2011

Education Tech Research Dev

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Our research aims to identify domain-specific similarities and differences of externalized cognitive structures. Cognitive structure, also known as knowledge structure or structural knowledge, is conceived as the manner in which an individual organizes the relationships of concepts in memory. By diagnosing these structures precisely, even partially, the educator comes closer to influencing them through instructional settings and materials. Our assessment and analysis of cognitive structures is realized within the HIMATT tool, which automatically generates four quantitative indicators for the structural entities of written text or causal maps. In our study, participants worked on the subject domains biology, history, and mathematics. Results clearly indicate different structural and semantic features across the three subject domains. Additionally, we found that written texts and causal maps seem to represent different structure and content across the three subject domains when compared to an expert's representation. We conclude with a general discussion, instructional implications and suggestions for future research.

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Constructing Mental Models during Learning from Science Text

Cited by 33 publications

References 40 publications

The influences of text and reader characteristics on learning from refutations in science texts.

The influences of text and reader characteristics on learning from refutations in science texts.

The Role of Regulation and Processing Strategies in Understanding Science Text Among University Students

Identifying cross-domain distinguishing features of cognitive structure

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