Chemistry Learning Using Multiple Representations: A Systematic Literature Review

The abstractness of the chemistry concept can be understood easily through chemistry learning using multiple representations. This article used the Systematic Literature Review (SLR) method to review eleven articles published from 2012 to 2021 and focused on chemistry learning using various representations. The articles are systematically obtained from the online article database ERIC, Scopus, and SINTA. The purpose of a review is to give information to teachers and researchers in chemistry education about the definition of multiple representations, the influence of multiple representations on chemistry learning outcomes, and how to implement various representations in chemistry learning models or strategies. The review results showed that the definition of numerous representations referred to both three levels of chemical representation and the tetrahedral representation of chemistry. Also, it referred to the use of various media. The influence of multiple representations on chemistry learning outcomes included improving concept understanding, improving performance, reducing mental effort, improving self-efficacy, making better cognitive structures, improving mental models, and reducing misconceptions. Multiple representations have also been implemented in several learning models or strategies such as Inquiry, Inquiry 5E, Guided Inquiry, Problem Solving, Thinking, Aloud Pair Problem Solving (TAPPS), Problem Posing (PP), Cognitive Dissonance, and Multiple Representation Based Learning (MRL)

Picture this: the value of multiple visual representations for student learning of quantum concepts in general chemistry

Mental models for scientific learning are often defined as, "cognitive tools situated between experiments and theories" (Duschl & Grandy, 2012). In learning, these cognitive tools are used to not only take in new information, but to help problem solve in new contexts. Nancy Nersessian (2008) describes a mental model as being [loosely] characterized as a representation of a system with interactive parts with representations of those interactions. Models can be qualitative, quantitative, and/or simulative (mental, physical, computational)" (p. 63). If conceptual parts used by the students in science education are inaccurate, then the resulting model will not be useful. Students in college general chemistry courses are presented with multiple abstract topics and often struggle to fit these parts into complete models. This is especially true for topics that are founded on quantum concepts, such as atomic structure and molecular bonding taught in college general chemistry. The objectives of this study were focused on how students use visual tools introduced during instruction to reason with atomic and molecular structure, what misconceptions may be associated with these visual tools, and how visual modeling skills may be taught to support students' use of visual tools for reasoning. The research questions for this study follow from Gilbert's (2008) theory that experts use multiple representations when reasoning and modeling a system, and Kozma and Russell's (2005) theory of representational competence levels. This study finds that as students developed greater command of their understanding of abstract quantum concepts, they spontaneously provided additional representations to describe their more sophisticated models of atomic and molecular structure during interviews. This suggests that when visual modeling with multiple representations is taught, along with the limitations of the representations, it can assist students in the development of models for reasoning about abstract topics such as atomic and molecular structure. There is further gain if students’ difficulties with these representations are targeted through the use additional instruction such as a workbook that requires the students to exercise their visual modeling skills

Mental Representations in Musical Processing and their Role in Action-Perception Loops

Music is created in the listener as it is perceived and interpreted - its meaning derived from our unique sense of it; likely driving the range of interpersonal differences found in music processing. Person-specific mental representations of music are thought to unfold on multiple levels as we listen, spanning from an entire piece of music to regularities detected across notes. As we track incoming auditory information, predictions are generated at different levels for different musical aspects, leading to specific percepts and behavioral outputs, illustrating a tight coupling of cognition, perception and action. This coupling, together with a prominent role of prediction in music processing, fits well with recently described ideas about the role of predictive processing in cognitive function, which appears to be especially suitable to account for the role of mental models in musical perception and action. Investigating the cerebral correlates of constructive music imagination offers an experimentally tractable approach to clarifying how mental models of music are represented in the brain. I suggest here that mental representations underlying imagery are multimodal, informed and modulated by the body and its in- and outputs, while perception and action are informed and modulated by predictions based on mental models

Learning Representations of Social Media Users

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.Comment: PhD thesi

Processing and learning from multiple sources: a comparative case study of students with dyslexia working in a multiple source multimedia context

This study investigated how four 10th-grade students with dyslexia processed and integrated information across web pages and representations when learning in a multiple source multimedia context. Eye movement data showed that participants' processing of the materials varied with respect to their initial exploration of the web pages, their overall processing time, and the linearity of their processing patterns, with post-learning interviews indicating the deliberate, strategic considerations underlying each participant's processing pattern. Eye movement data in terms of fixation duration and percentage of regressions also corroborated the findings of formal, diagnostic assessments. Finally, it was found that participants differed with respect to how much factual information they learned from working with the materials and how well they were able to integrate information across the web pages and representations, with results suggesting particular problems with learning factual information and, at the same time,constructing a coherent mental representation of the issue, as well as with drawing on textual information in the integration process. This study brings together two research areas that essentially have been kept apart in theory and research, that is, dyslexia and multimedia learning, and it provides unique information about the role of individual differences in multiple source multimedia contexts

Learning Representations of Social Media Users

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.Comment: PhD thesi

Gradual progression from sensory to task-related processing in cerebral cortex

Somewhere along the cortical hierarchy, behaviorally relevant information is distilled from raw sensory inputs. We examined how this transformation progresses along multiple levels of the hierarchy by comparing neural representations in visual, temporal, parietal, and frontal cortices in monkeys categorizing across three visual domains (shape, motion direction, and color). Representations in visual areas middle temporal (MT) and V4 were tightly linked to external sensory inputs. In contrast, lateral prefrontal cortex (PFC) largely represented the abstracted behavioral relevance of stimuli (task rule, motion category, and color category). Intermediate-level areas, including posterior inferotemporal (PIT), lateral intraparietal (LIP), and frontal eye fields (FEF), exhibited mixed representations. While the distribution of sensory information across areas aligned well with classical functional divisions (MT carried stronger motion information, and V4 and PIT carried stronger color and shape information), categorical abstraction did not, suggesting these areas may participate in different networks for stimulus-driven and cognitive functions. Paralleling these representational differences, the dimensionality of neural population activity decreased progressively from sensory to intermediate to frontal cortex. This shows how raw sensory representations are transformed into behaviorally relevant abstractions and suggests that the dimensionality of neural activity in higher cortical regions may be specific to their current task. Keywords: categorization; cognition; prefrontal cortex; posterior parietal cortex; dimensionalityNational Institute of Mental Health (U.S.) (Grant 5R37MH087027

Prefrontal Cortex Networks Shift from External to Internal Modes during Learning

As we learn about items in our environment, their neural representations become increasingly enriched with our acquired knowledge. But there is little understanding of how network dynamics and neural processing related to external information changes as it becomes laden with “internal” memories. We sampled spiking and local field potential activity simultaneously from multiple sites in the lateral prefrontal cortex (PFC) and the hippocampus (HPC)—regions critical for sensory associations—of monkeys performing an object paired-associate learning task. We found that in the PFC, evoked potentials to, and neural information about, external sensory stimulation decreased while induced beta-band (~11–27 Hz) oscillatory power and synchrony associated with “top-down” or internal processing increased. By contrast, the HPC showed little evidence of learning-related changes in either spiking activity or network dynamics. The results suggest that during associative learning, PFC networks shift their resources from external to internal processing.National Institute of Mental Health (U.S.) (Grant R37MH087027)National Institute of Mental Health (U.S.) (Grant F32-MH081507

Examining the cognitive costs of counterfactual language comprehension: Evidence from ERPs

Recent empirical research suggests that understanding a counterfactual event (e.g. ‘If Josie had revised, she would have passed her exams’) activates mental representations of both the factual and counterfactual versions of events. However, it remains unclear when readers switch between these models during comprehension, and whether representing multiple ‘worlds’ is cognitively effortful. This paper reports two ERP studies where participants read contexts that set up a factual or counterfactual scenario, followed by a second sentence describing a consequence of this event. Critically, this sentence included a noun that was either consistent or inconsistent with the preceding context, and either included a modal verb to indicate reference to the counterfactual-world or not (thus referring to the factual-world). Experiment 2 used adapted versions of the materials used in Experiment 1 to examine the degree to which representing multiple versions of a counterfactual situation makes heavy demands on cognitive resources by measuring individuals’ working memory capacity. Results showed that when reference to the counterfactual-world was maintained by the ongoing discourse, readers correctly interpreted events according to the counterfactual-world (i.e. showed larger N400 for inconsistent than consistent words). In contrast, when cues referred back to the factual-world, readers showed no difference between consistent and inconsistent critical words, suggesting that they simultaneously compared information against both possible worlds. These results support previous dual-representation accounts for counterfactuals, and provide new evidence that linguistic cues can guide the reader in selecting which world model to evaluate incoming information against. Crucially, we reveal evidence that maintaining and updating a hypothetical model over time relies upon the availability of cognitive resources