The Role of Cognitive Effort in Decision Performance Using Data Representations :;a Cognitive Fit Perspective

A major goal of Decision Support (DSS) and Business Intelligence (BI) systems is to aid decision makers in their decision performance by reducing effort. One critical part of those systems is their data representation component of visually intensive applications such as dashboards and data visualization. The existing research led to a number of theoretical approaches that explain decision performance through data representation\u27s impact on users\u27 cognitive effort, with Cognitive Fit Theory (CFT) being the most influential theoretical lens. However, available CFT-based literature findings are inconclusive and there is a lack of research that actually attempts to measure cognitive effort, the mechanism underlying CFT and CFT-based literature. This research is the first one to directly measure cognitive effort in Cognitive Fit and Business Information Visualization context and the first one to evaluate both self-reported and physiological measures of cognitive effort. The research provides partial support for CFT by confirming that task characteristics and data representation do influence cognitive effort. This influence is pronounced for physiological measures of cognitive effort while it minimal for self-reported measure of cognitive effort. While cognitive effort was found to have an impact on decision time, this research suggests caution is assuming that task-representation fit is influencing decision accuracy. Furthermore, this level of impact varies between self-reported and physiological cognitive effort and is influenced by task complexity. Research provides extensive cognitive fit theory, business information visualization and cognitive effort literature review along with implications of the findings for both research and practic

The Role of Cognitive Effort in Decision Performance Using Data Representations :;a Cognitive Fit Perspective

A major goal of Decision Support (DSS) and Business Intelligence (BI) systems is to aid decision makers in their decision performance by reducing effort. One critical part of those systems is their data representation component of visually intensive applications such as dashboards and data visualization. The existing research led to a number of theoretical approaches that explain decision performance through data representation\u27s impact on users\u27 cognitive effort, with Cognitive Fit Theory (CFT) being the most influential theoretical lens. However, available CFT-based literature findings are inconclusive and there is a lack of research that actually attempts to measure cognitive effort, the mechanism underlying CFT and CFT-based literature. This research is the first one to directly measure cognitive effort in Cognitive Fit and Business Information Visualization context and the first one to evaluate both self-reported and physiological measures of cognitive effort. The research provides partial support for CFT by confirming that task characteristics and data representation do influence cognitive effort. This influence is pronounced for physiological measures of cognitive effort while it minimal for self-reported measure of cognitive effort. While cognitive effort was found to have an impact on decision time, this research suggests caution is assuming that task-representation fit is influencing decision accuracy. Furthermore, this level of impact varies between self-reported and physiological cognitive effort and is influenced by task complexity. Research provides extensive cognitive fit theory, business information visualization and cognitive effort literature review along with implications of the findings for both research and practic

The effect of teacher scaffolding and student comprehension monitoring on a multimedia/interactive videodisc science lesson for second graders

Imagery based computer instruction is predicted to have a major impact on educational curriculum in the next century. Yet research on the effectiveness of imagery technology for early elementary-age children is a relatively unexplored area. The purpose of this study was to examine age-appropriate uses of a multimedia/interactive videodisc (IVD) science lesson for second graders in two areas. First, the unique properties that these media offer as a stand-alone teaching tool were assessed. Second, the non-technological strategies of teacher scaffolding and comprehension monitoring as supplements to IVD programs were investigated. A learner controlled multimedia/IVD instructional program was specifically designed for this study. The learning objectives were to teach the scientific processes of classification and problem solving through observing, comparing, and contrasting two species of primates: apes and monkeys

Integrating Cognitive Learning Strategies into Physics Instruction : Developing students’ approaches to physics and learning

Introductory physics courses are obligatory for many disciplines outside of physics. As experienced by many students, they are notoriously difficult, often with high failure rates. Many students, whether they passed or failed a physics course, fail to acquire the required conceptual knowledge and skill to become able to model complex situations with physics principles. In some cases, this can be attributed to a lack of study time; in many cases, it can be attributed to inefficient learning strategies. The aim of this thesis was to find ways to create self-regulated physics students who use effective learning strategies, achieve a deep understanding of physics principles, and, ultimately, become able to solve conceptually challenging physics problems through the use of physics modeling. In this research project, we have identified and tried to fill some of the gaps in students’ knowledge that hinder them from becoming able to practice physics modeling. Research within cognitive science, educational psychology, and physics education has informed us about the structure of the knowledge students fail to learn. We matched proven, effective learning strategies to each aspect of this cognitive knowledge structure and we developed tools for scaffolding the process. In the first phase of the first paper, we investigated students’ memory for physics principles and basic facts shortly before the exam and experimentally tested the efficacy of retrieval practice of a novel hierarchical principle structure for improving their declarative memory. The results showed that many of the control group students had a severe lack in their memory for basic facts and principles and that seventy minutes of retrieval practice resulted in large gains for the experimental group. In the second phase, we implemented structured retrieval practice in lectures throughout the semester. The multiple regression model indicated that retrieval practice improved students’ results on the final exam, especially for the weaker students. In the second paper, we quasi-experimentally (study 1) and experimentally (study 2) tested the effects of doing retrieval practice before self-explanation on posttest problem-solving and conceptual scores. In sum, results indicated a medium-sized effect of doing retrieval practice on the problem-solving score. The results were inconclusive for the score on conceptual tests. We also investigated the knowledge students should seek to acquire when self-explaining worked examples in physics. The results from the two studies indicated that when explaining the physics model, students should seek to explicate the principles and their conditions of application, how the principle is set up, and how the physics model can lead to the goal of the problem; and when explaining the mathematical procedures, students should seek to explicate what is done in the particular procedural action, the goal of that action, and the conditions for its application. In the third paper, we built on the results and experiences from the first two papers and tried to integrate three learning strategies and three scaffolding tools into an introductory mechanics course. The three learning strategies were elaborative encoding for acquiring associative links within and between physics principles; retrieval practice for building strong memories of physics principles; and self-explanations for building effective declarative rules for problem-solving. The three tools were: A set of elaborative encoding-questions as a scaffold for elaborative encoding; the Hierarchical Principle Structure for Mechanics, which together with retrieval practice was meant for scaffolding students’ construction of a meaningful and hierarchical cognitive knowledge structure; and a problem-solution structure with emphasis on physics modeling for scaffolding self-explanation and for developing knowledge and skills in physics modeling. Using thematic analysis, we found that the two main encoding strategies—elaborative encoding and self-explanation—require substantial work for overcoming the existing barriers to student adoption and achieving effective implementation. We had more success with the integration of retrieval practice, the hierarchical principle structure, and the practice of physics modeling during problem-solving. The paper provided multiple suggestions for how to overcome barriers and better integrate these learning strategies and tools into the structure of physics courses. Together, these three papers contribute to the physics education research literature with increased knowledge of how we can support students’ conceptual learning, from simple cognitive learning processes like elaborative encoding to the complex practice of physics modeling; with new tools for scaffolding students’ conceptual learning in introductory physics, especially the Hierarchical Principle Structure for Mechanics and the problem-solution structure; and with insights into barriers to students’ adoption of effective learning strategies.Doktorgradsavhandlin

An investigation into the impact of human age on persuasion and advertising.

The elderly segment of this country\u27s population is growing rapidly. By the beginning of the next century, adults age 65 and over will fully comprise one-quarter of the population. It cannot be assumed that communication principles that apply to young adults are directly applicable to elderly adults. Age-related changes in learning, memory and information processing can have a dramatic effect on communicating with an aging population. The Elaboration Likelihood Model developed by Petty and Cacioppo provided the underlying persuasion theory for this study. Previous advertising and gerontological research provided additional theoretical basis for the research hypotheses. This study examined the impact of personal relevance and type of ad copy on the persuasion process for elderly as compared to young adults. More specifically, the research objectives were: (1) To examine information processing differences for persuasive communications between elderly and young adults under conditions of high and low personal relevance. (2) To study the persuasiveness and memorability of abstract and concrete ad copy for elderly and young adults. (3) To identify reasons for age-related deficits in memory. To accomplish these objectives, chronological age, personal relevance and type of ad copy factors were manipulated in a 2 x 2 x 2 between subjects design. There were two levels each of age (young/old adults), personal relevance (high/low), and type of ad copy (abstract/concrete). Two hundred, thirty-eight young and elderly subjects were studied. All subjects participated in an experimental session in which they examined a sample magazine containing stimulus advertisements and then completed a questionnaire. Results of the study showed that young adults appear to have better aided recall and recognition memory for advertisements than older adults. There were differential effects for abstract and concrete ad copy. For both age groups, concrete was remembered better than abstract ad copy. Yet, elderly adults exposed to concrete ad copy appeared to have higher levels of purchase intent in the high personal relevance condition than in the low personal relevance condition. Overall, elderly adults had higher levels of purchase intent than young adults, but poorer memory performance. Finally, memory deficits among elderly adults appeared to stem from problems recalling information from memory, not due to encoding information into memory. The implications of the study findings for advertising are many. These relate to the type of copy used in print advertisements, media selection for reaching the elderly adult, and advertisement effectiveness measurement, to name a few. It is clear that advertisers need to approach elderly adults differently than they do young adults. Future research directions are presented

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

Would disfluency by any other name still be disfluent? Examining the boundary conditions of the disfluency effect

When exposed to words presented under perceptually disfluent conditions (e.g., words written in Haettenschweil font), participants have difficulty recognizing the words. Those same words, though, may be better remembered than words presented in standard type font. This counterintuitive finding is referred to as the disfluency effect. Evidence for this disfluency effect, however, has been mixed. Using a recognition memory task, I examined five variables that may contribute to the inconsistent findings: type of judgments of learning (JOLs), encoding instructions, type of disfluency manipulation, encoding duration, and retention interval between study and test. Experiment 1 employed a masking manipulation and examined the influence of type of JOLs (item-by-item JOLs vs. aggregate JOLs) along with encoding instructions (incidental vs. intentional). Experiments 2 and 3 explored the locus of the disfluency effect by examining perceptual disfluency manipulations that tap different loci during word recognition: low-level blurring (pre-lexical) and cursive handwriting (lexical). Experiment 4 examined the role of encoding duration on the disfluency effect. Experiment 5 examined whether list design (blocked vs. mixed) moderates the disfluency effect. Experiment 6 examined whether the benefits of disfluency extend over longer durations (24 hours). Results across the six experiments indicated that the disfluency effect is modulated by testing expectancy, type of disfluency manipulation, and encoding duration. A disfluency effect was observed only under incidental instructions with a sufficiently long encoding duration. Further, I found that a pre-lexical manipulation (i.e., blurring) did not produce a disfluency effect, but a lexical perceptual disfluency manipulation (i.e., cursive) did. This cursive disfluency effect was moderated by legibility: easy-to-read cursive words tended to be better remembered than hard-to-read cursive words. This finding was bolstered by a meta-analysis. Taken together, these results challenge extant accounts of the disfluency effect. The research comprising this dissertation furthers the theoretical understanding of the disfluency effect as well at its practical implications

Examining the Testing Effect using the Dual-Process Signal Detection Model

Taking a test can lead to enhanced long-term retention compared to not practicing the information or simply restudying, a finding known as the testing effect (Roediger, Agarwal, Kang, & Marsh, 2010). The current study examined whether the dual-process signal detection (DPSD) model (Yonelinas, 1994) offers an approach for investigating the testing effect across two experiments. Experiment 1 investigated if the DPSD model could be used to examine the testing effect, and it also examined a factor (i.e., the number of practice sessions) that influences the magnitude of the testing effect. Experiment 2 investigated whether making the final test dependent on recollection would influence the magnitude of the testing effect and the parameter estimates of recollection and familiarity. The results of these experiments demonstrated that when practice testing enhanced later memory, it also influenced the processes underlying the recognition memory judgments in a manner consistent with the DPSD model. Practice testing (in comparison to restudying) increased familiarity in both experiments and increased both familiarity and recollection when three practice tests were used. However, when comparing old versus similar lure items on the recollection-dependent final test format, no significant differences between practice testing and restudying were found. Overall, this study demonstrated that the DPSD model can be used to examine the testing effect. The DPSD model may provide a useful approach for future research investigating the testing effect in terms of the conditions under which the effect occurs, factors that influence the effect, and theoretical explanations for the effect

The Effect of Age, Syntax Complexity, and Cognitive Ability on the Rate of Semantic Illusions

Semantic illusions are recognition errors that occur when an individual fails to notice that information contradicts their prior knowledge (Barton & Sanford, 1993; Erickson & Mattson, 1981). For example, after hearing the question, “If a plane crashes while flying over state lines, where should the survivors be buried?” many start to consider the legality or appropriateness of the scenario despite knowing “survivors” should not be buried. Having more knowledge does not necessarily prevent individuals from overlooking illusory information/misinformation. Older adults tend to have greater crystallized intelligence than young adults, yet these age groups appear to detect illusory information at equivalent rates (Umanath & Marsh, 2012; Umanath, 2014). However, there is also evidence that older adults experience more semantic illusions than young adults in general (Umanath et al., 2012). Previous research demonstrates that the rate of semantic illusions is sensitive to specific language structure manipulations, such as syntax structure or word placement that facilitate overlooking the illusory information (Bredart and Modolo, 1988; Büttner, 2007; Wang, Hagoort, & Yang, 2009). Furthermore, there is evidence that disrupting processing fluency by increasing the difficulty of reading enables more frequent detection of illusory information (Song, 2009). Although this effect has been demonstrated using easy- versus difficult-to-read font, increasing syntax complexity also increases reading difficulty and requires more effort for comprehension (e.g., Kemtes & Kemper, 1997; Stromswold et al., 1996). The current study used a combined experimental-correlational approach to investigate the effects of age, language structure, and cognitive ability on the rate of semantic illusions experienced in response to general knowledge questions. The experimental approach compared the rate of semantic illusions between young and older adult age groups for illusory information embedded in sentences with either simple or complex syntax structures. The correlational approach examined the best cognitive predictors of increased detection of illusory information among composite scores for crystallized intelligence, fluid intelligence, and rationality. The sample of 203 participants, including 114 young adults ( M = 24.98, SD = 4.06) and 89 older adults ( M = 65.63; SD = 4.93), was administered a semantic illusion task, general knowledge check, and reading comprehension task, along with a battery of cognitive measures assessing fluid intelligence, crystallized intelligence, and rational thinking (Comprehensive Assessment of Rational Thinking [CART]; Stanovich, 2016). The semantic illusion task included general knowledge questions that either contained the correct information (target item), e.g., “How many animals of each kind did Noah bring on the ark?” or incorrect information (illusion item), e.g., How many animals of each kind did Moses bring on the ark?”. The sentence structure of the general knowledge questions varied across syntax complexity condition, such that participants experienced target items and illusion items in both simple (right-branching) versus complex (left-branching, middle-branching) syntax structures. Scoring procedures assessed frequencies for: (a) correct responses on target items (target score), (b) successful detection of illusory information (detection score), and (c) failures to detect illusory information (illusion score). The results of the experimental portion of the study confirmed an interaction of age and syntax for detection scores. Older adults detected illusory information more frequently than young adults, and complex versus simple syntax increased this advantage for the older adult age group. Alternatively, the pattern of results for illusion scores, or overlooking the illusory information, produced a main effect of age with older adults experiencing more semantic illusions than young adults regardless of syntax condition. Although counterintuitive, older adults had a higher baseline of prior knowledge, and therefore had more opportunities than young adults to detect and overlook the illusory information at higher rates. The correlational portion was largely data-driven, and investigated which cognitive composites for fluid intelligence, crystallized intelligence, and rationality best predicted detection scores. Results demonstrated varying patterns between age groups, such that young adult detection scores were most accurately predicted by the rationality composite scores. However, older adult detection scores were best predicted by crystallized intelligence. Although both crystallized intelligence and rationality are positively associated with detection of illusory information (Hannon & Daneman, 2001; Mata et al., 2014), a mediation analysis revealed a potential underlying cause to the age-differences in the outcomes. A bootstrap mediation analysis indicated the effect of age group on detection scores was fully mediated by crystallized intelligence. More specifically, older adults had more prior knowledge than young adults to such a disparity, variation in detection scores between age groups can be fully accounted for by differences in crystallized intelligence between young and older adults. Overall, increased syntax complexity facilitates detection of illusory information compared to simple syntax. Furthermore, increased crystallized intelligence is associated with more frequent detection of illusory information. Yet, with less prior knowledge, performance on rational thinking problems is the better predictor of detecting illusory information