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Cognitive Factors Research Thrust

The research in this thrust aimed at understanding cognitive learning—changes in knowledge—that result from instructional events.  Consistent with the KLI Framework, the work triangulated on a set of events around learning: learning events, instructional events, and assessment events. The hypotheses of the Cognitive Factors Thrust concern how instructional procedures (e.g., decisions about the learner’s task, materials, practice, feedback) promote robust learning and effective instruction.  Just a few of the most notable research results of the Cognitive Factors thrust are summarized below.

Writing helps learning to read and modifies the brain’s reading network. In one exemplary study, Guan et al. (2011) compared the effects of an online writing tutor that included character handwriting with an instructional tutor that included reading only.  They found that when students write Chinese characters they are learning to read, their later performance on reading tasks is improved substantially over a read-only condition. The practical implication is that an integration of character handwriting and Pinyin typing promotes learning to read Chinese in a second language learning context.  The scientific implications of this line of research are also interesting.  Writing may focus attention on the orthography of the character and it may create a cross-modal (sensory motor and perceptual) representation of the character. A neuroimaging study by Cao, Perfetti and others (Cao, Tao, Liu, Perfetti & Booth, 2013) found that when subjects view characters they have learned to write, activation is higher in spatial memory and sensory-motor areas of the brain. Furthermore, activation is higher in left temporal areas associated with character-meaning, suggesting that writing supports the acquisition of an orthographic form that becomes linked to its meaning.

Visual Complexity of Languages Affects Learning.  Exciting new research in this line of work has explored how the visual complexity of orthographies varies across writing systems. Prior research had shown that complexity strongly influences the initial stage of reading development: the perceptual learning of grapheme forms. LearnLab research (Chang, Plaut, & Perfetti,  2016) demonstrated how visual complexity can be a factor that leads to grapheme learning difficulty across writing systems.  This work connects with the Computational Modeling and Data Mining thrust in that a computational learning model was developed.  131 identical neural networks were trained to learn the structure of a different orthography and demonstrated a strong, positive association between network learning difficulty and multiple dimensions of grapheme complexity. We also tested the model’s performance against grapheme complexity effects on behavioral same/different judgments. Although the model was broadly consistent with human performance in how processing difficulty depended on the complexity of the tested orthography, as well as its relationship to viewers’ first-language orthography, discrepancies provide insight into important limitations of the model.

Children Learn Scientific Inquiry Best When GIven Direct Instructional Support. The paper by David Klahr and colleagues in the August 2011 issue of Science (Klahr, Zimmerman, & Jirout 2011) presented a taxonomy for classifying different types of research on scientific thinking from the perspective of cognitive development and associated attempts to teach science.  The article summarized the literature on the early—unschooled—development of scientific thinking, and focused on recent research on how best to teach science to children from preschool to middle school. It summarizes research from Klahr’s team demonstrating that scientific inquiry skills are better learned by children via more direction instruction that provides explanations than less direct socratic or discovery instruction (e.g., Klahr & Chen, 2003; Siler & Klahr, 2015).

Unified Model for Learning a Second Language. Research by Brian MacWhinney and his team on a unified model of language learning (MacWhinney,  2012) has led to the development of language tutors in English, Spanish, Mandarin, Cantonese, and Japanese. Their Second Language Acquisition website provides language tutoring systems, online measurement, experimentation, and analytic tools for second language learning  data. Several of their tutors are in use in classrooms around the world.  His Pinyin tutor has gained wide acceptance in the Chinese teaching community with 48 schools and universities enrolling more than 1300 learners. The team has also developed and disseminated online cognitive assessment tools in eight languages available on both desktops and tablets/phones for predicting individual differences in language learning including digit span, flanker, and number-letter tasks.