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One of the essential goals of science learning is to lead students to master scientific concepts or ideas and apply them to explain relevant everyday phenomena. Such mastery should help students to work with various representations. The objective of this study was to determine the effectiveness of the Learning Cycle Multiple Representation (LCMR) model in students' mastery of concepts in plant physiology. This research was done using a quasi-experimental pretest-posttest design involving 62 students as the respondents. The concept mastery instrument consisted of 11 essay tests. The instrument met the value of validity and reliability. The data analysis was done using the one-way analysis of covariant set at a significance level of 5%. The results show that the LCMR model significantly affected students' mastery levels. The students who learned using the LCMR model showed significantly better mastery of the tested concept than the ones who used the LC model. The mastery score for the student learned using the LCMR model was 80.67, significantly higher (p=0.00 <[alpha]) than that obtained from the LC model of 54.00. Apart from enhancing mastery in plant physiology, the LCMR method can be expanded for other topics, and its effectiveness can be evaluated in future studies.

Numerous studies have found that selective attention affects category learning. However, previous research did not distinguish between the contribution of focusing and filtering components of selective attention. This study addresses this issue by examining how components of selective attention affect category representation. Participants first learned a rule-plus-similarity category structure, and then were presented with category priming followed by categorization and recognition tests. Additionally, to evaluate the involvement of focusing and filtering, we fit models with different attentional mechanisms to the data. In Experiment 1, participants received rule-based category training, with specific emphasis on a single deterministic feature (D feature). Experiment 2 added a recognition test to examine participants' memory for features. Both experiments indicated that participants categorized items based solely on the D feature, showed greater memory for the D feature, were primed exclusively by the D feature without interference from probabilistic features (P features), and were better fit by models with focusing and at least one type of filtering mechanism. The results indicated that selective attention distorted category representation by highlighting the D feature and attenuating P features. To examine whether the distorted representation was specific to rule-based training, Experiment 3 introduced training, emphasizing all features. Under such training, participants were no longer primed by the D feature, they remembered all features well, and they were better fit by the model assuming only focusing but no filtering process. The results coupled with modeling provide novel evidence that while both focusing and filtering contribute to category representation, filtering can also result in representational distortion.

The evolution of concept mapping has benefitted from the robust theoretical basis provided by Ausubelian learning theory. However, for concept mapping to maintain its relevance and to keep pace with the evolutionary changes in the educational context, it is vital that educational researchers and classroom practitioners can augment this theoretical base with contemporary learning theories that will help to improve the application of concept mapping and increase the likelihood that the goal of meaningful learning will be achieved in practice. This involves shifting the focus of concept mapping from product to process and the role of the learner from 'being' to 'becoming.' The act of concept mapping needs to be viewed as a way of mastering learning rather than of mastering specific content. We propose the consideration of the explicit role of semantic waves as an improvement from simplistic knowledge representation towards the development of more complex knowledge modelling as a way of developing powerful knowledge structures.

This study aims to investigate the process of designing infographics through participants' knowledge construction in the assessment and evaluation process of IB Educator Certificate (IBEC) educational technologies education. The study, in which the case study method was used, was conducted with 12 participants who attended the educational technologies and material design course in IBEC education. 7 infographic assignments were given in 14 weeks and 42 hours of online training and analyzed according to the relevant analytical rubric. To measure the development in the training process, pre-test and post-test were measured with the Web 2.0 tools usage competencies scale. At the end of the training, the participants' views on the effect of infographics on their learning processes and its use in education were analyzed by descriptive analysis. Accordingly, it was observed that the participants produced designs with effective content in their infographic assignments during the process. At the end of the training, there was a significant increase in their use of web 2.0 tools, which is the scope of the course, in support of this. According to the participants' views, results such as facilitating learning, permanent learning and having fun while learning were also obtained.

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (eg, GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models.

International research in the field of education focuses on the approach and use of mental knowledge-related mechanisms. In the educational research in Romania, we see the absence of studies focused on revision, analysis, and summary of the literature on the concept of representation and a shortage of studies tackling issues concerning creation of quality representations in primary school pupils. The aim of the study is to analyse teachers' opinions on nine different themes of interest in the lexical field of representation, explored throughout three standard interviews. The interviewees are teaching staff, a professor and two pre-university teachers. The data collected are presented in tables and analysed by manual codification. The results are significant in relation to the theoretical substantiation. The main conclusion of the study is: representations constitute a premise for the optimal functioning of the intellect. Some predictors favourable to creation of quality representations in pupils are the use of previous cognitive experiences, the accessibility and monitoring of learning as well as the quality of educational aids. By using suitable didactic resources, which reproduce the reality in a simplified manner or bring about elaborated abstractions, the teacher facilitates learning.

How does conceptual structure of external representations contribute to learning? This investigation considered the influence of generative concept sorting and of external structure information moderated by perceived difficulty. In Study 1, undergraduate students completed a perceived difficulty survey and comprehension pretest, then a sorting task, and finally a comprehension posttest. Results showed that both perceived difficulty and comprehension pretest significantly predicted comprehension posttest performance. Learners who perceived that history is difficult attained significantly greater posttest scores and had more expert-like networks. In Study 2, participants completed the same perceived difficulty survey and comprehension pretest, then were randomly assigned to read a text with different external structure support, either an expert network or an equivalent outline of the text, and finally all completed the same sorting task posttest and a comprehension posttest. In Study 2, there was no significant difference for external structure support on posttest comprehension (outline = network), but reading with an outline led to a linear conceptual structure that matched the topic order of the text while reading with a network led to a more expert-like relational structure. As in Study 1, comprehension pretest and perceived difficulty significantly predicted posttest performance, but in contrast to Study 1, learners who perceived that history is easy attained significantly greater posttest scores. Practitioners should consider using generative sorting tasks when relearning history content. For theory building purposes, post-reading mental representations matched the form of the external representation used when reading, thus the conceptual structure of the external representation should be considered in future research.

With the increased popularity of electronic textbooks, there is a growing interest in developing a new generation of "intelligent textbooks," which have the ability to guide readers according to their learning goals and current knowledge. Intelligent textbooks extend regular textbooks by integrating machine-manipulable knowledge, and the most popular type of integrated knowledge is a list of relevant concepts mentioned in the textbooks. With these concepts, multiple intelligent operations, such as content linking, content recommendation, or student modeling, can be performed. However, existing automatic keyphrase extraction methods, even supervised ones, cannot deliver sufficient accuracy to be practically useful in this task. Manual annotation by experts has been demonstrated to be a preferred approach for producing high-quality labeled data for training supervised models. However, most researchers in the education domain still consider the concept annotation process as an ad-hoc activity rather than a carefully executed task, which can result in low-quality annotated data. Using the annotation of concepts for the "Introduction to Information Retrieval" textbook as a case study, this paper presents a knowledge engineering method to obtain reliable concept annotations. As demonstrated by the data we collected, the inter-annotator agreement gradually increased along with our procedure, and the concept annotations we produced led to better results in document linking and student modeling tasks. The contributions of our work include a validated knowledge engineering procedure, a codebook for technical concept annotation, and a set of concept annotations for the target textbook, which could be used as a gold standard in further intelligent textbook research.

This article aims to present a Mixed Integrative Heuristic Approach (MIHA) for image studies based on textual content (media news) analysis. MIHA was developed during an exploratory study on the image of China and the Chinese in Zambia and Angola. The analyzed database was relatively small (2477 articles), and the material was very heterogeneous, because of the wide scope of topics covered by the texts and the fact that we analyzed content from both English- and Portuguese-language media. To obtain the best possible results, we decided to employ the mixed methods approach for a bottom-up created dictionary, and to design a set of sentiment indexes based on both automatic and manual coding. This paper focuses on the two most important steps of the method development: dictionary building and sentiment indexes design.

Concept mapping, a tool originally developed to facilitate student learning by organizing and visualizing key concepts and their relationships, can also be used to represent the composition of the knowledge contained in a course. In this paper, the authors describe a specific application of concept mapping to help instructors and students visualize the knowledge contained in an introductory programming course of an undergraduate Computer Information Systems program. The authors show how representing the knowledge structure of a course using a concept map can enable the faculty to assess the breadth and depth of the knowledge imparted through the course. The authors discuss how a concept map that depicts the composition of a course can function as a useful instructional tool to assess and improve the quality of instruction that may enable meaningful learning among students.

In intelligent e-learning systems that adapt a learning and teaching process to student knowledge, it is important to adapt the system as quickly as possible. However, adaptation is not possible until the student model is initialized. In this paper, a new approach to student model initialization using domain knowledge representative subset is described. The approach defines which concepts from domain knowledge should be included in the initial test so the system can make conclusions about what students truly know about domain knowledge. This representative subset of domain knowledge is defined using non-semantic mathematical approach based on graph theory. The initial test, created over a domain knowledge representative subset, guarantees encompassing all concepts that are relevant to domain knowledge. A two-level case study is conducted on what would be the representative subset of one selected domain knowledge. It compares semantically selected domain knowledge representative subsets (semantical analysis was done by domain area experts) to a non-semantical, mathematically selected domain knowledge representative subset. The results of the case study show that problems of inequality of semantically selected domain knowledge representative subsets are easily overcome using the presented approach.

Thinking is the essence of human existence, unquestionably the highest product of human evolution. Yet it is not possible to convey coherently to students the sum and attainment of all humanity's knowledge. Students would be incapable of absorbing such an enormous quantity of knowledge (corresponding to current developments in individual academic disciplines), as this is often highly abstract. Therefore educational content must be selected, to the extent that it presents a representative selection of knowledge. One of the most effective selection methods could be the use of conceptual mapping in the educational process. In this study the authors focus on the issue of structured teaching material and its relational association with creating students' mental representation, and have several objectives. The first is to analyze constructivist-oriented teaching processes; the authors also give attention to basic theses by which it is possible to delineate the main pillars of constructivist-oriented teaching. Attention is also given to learning strategies. No less important is the analysis of non-linear structured teaching material and its specific types, with an emphasis on conceptual mapping and its role in the teaching process -- in the empirical part the effect of non-linear material in recording topical unit meaningfulness in students' mental representation. In the research undertaken, the methods of conceptual mapping experimenting and testing were used. The consistency of the conceptual maps was evaluated through the relational method using ordinal variables: meaningfulness of conceptual maps. The authors assessed the quality of the conceptual maps with the aid of IRT theory, specifically the latent correlations model. The research sample comprised 96 respondents. Analysis of results showed that the use of non-linear structured teaching material improved students' results in the experimental groups in the researched operationalization parameter.

This article addresses some fundamental issues of concept mapping relevant to discipline-based education. The focus is on manufacturing knowledge representation from the viewpoints of both human and machine learning. The concept of new-generation manufacturing (Industry 4.0, smart manufacturing, and connected factory) necessitates learning factory (human learning) and human-cyber-physical systems (machine learning). Both learning factory and human-cyber-physical systems require semantic web-embedded dynamic knowledge bases, which are subjected to syntax (machine-to-machine communication), semantics (the meaning of the contents), and pragmatics (the preferences of individuals involved). This article argues that knowledge-aware concept mapping is a solution to create and analyze the semantic web-embedded dynamic knowledge bases for both human and machine learning. Accordingly, this article defines five types of knowledge, namely, analytic a priori knowledge, synthetic a priori knowledge, synthetic a posteriori knowledge, meaningful knowledge, and skeptic knowledge. These types of knowledge help find some rules and guidelines to create and analyze concept maps for the purposes human and machine learning. The presence of these types of knowledge is elucidated using a real-life manufacturing knowledge representation case. Their implications in learning manufacturing knowledge are also described. The outcomes of this article help install knowledge-aware concept maps for discipline-based education.

Background: Creating concept maps can help students overcome challenges of accurate knowledge monitoring and thus foster learning. However, students' knowledge often contains gaps and misconceptions, even after concept map creation. Theoretically, students could benefit from additional support, but it is unclear whether this might also be the case for (more practical-oriented) secondary vocational students. Objectives: This study investigated whether the effectiveness of concept maps for learning could be improved by providing students with expert examples and reflection prompts in addition to their self-generated concept maps. Methods: First-year secondary vocational students (N = 91, M[subscript age] = 17.3 years) participated in this study, which utilized a pretest-intervention-posttest design. Regarding the intervention, students worked in two successive online learning environments, in which they had to present their knowledge in concept maps. After creation, students' concept maps were, depending on condition, supplemented with (1) an expert example with comparative feedback (a combined concept map) and related reflection prompts, (2) the combined concept map only, or (3) no combined concept map and no prompts. Results and Conclusions: Analyses based on students' domain knowledge demonstrate that students significantly increased their knowledge in all conditions. Data indicate that there was no significant difference in knowledge gain between conditions. Further analysis showed that students in the experimental conditions demonstrated higher learning gains if they consulted the combined concept map more often than their peers. Implications: Access to an example in addition to students' self-generated concept maps seems promising in fostering their knowledge acquisition. However, secondary vocational students might need additional ways of support to guarantee higher learning gains. Avenues to increase the effectiveness of support are discussed.

We report a longitudinal study carried out along 3 years in an early childhood education (ECE) classroom in which we examined children's (aged 3-6) engagement with science representations. The research questions are as follows: (1) How do children's science representations develop from ECE1 to ECE3? (2) What are the features and affordances of the teacher's scaffolding of the production of science representations and how is it facilitated from the first to the third year of ECE? The participants were 21 children and their teacher. The group was involved in long-term science projects that lasted for 5 months each. Sessions (N = 30) were recorded and children's drawings (N = 487) gathered. Data were analyzed using discourse and content analyses, coupled with an analysis of the intensity of scaffolding. The results indicate that children's representations of science phenomena became more complex along several dimensions. We have identified teacher's scaffolding strategies which supported children's increasing autonomy in producing representations. Implications are drawn for teaching science at the ECE level as well as for further research.

Context-Based Learning (CBL) and learning through developing and using models are two important teaching approaches for chemistry conceptual understanding. We aimed to examine the influence of a CBL approach on students' understanding of Multiple Models of Knowledge Representations ("MMKRs") and "multiple molecular representations." Research participants included high-school students in three different research groups. The "context-based group" (N = 271) studied food chemistry in a CBL approach. The "traditional group" studied according to the traditional curriculum (high-school organic chemistry), and the "traditional + food group" studied according to the traditional curriculum, with the addition of food-related topics (N = 99). The "context-based group" had a greater effect on increasing students' ability to understand and relate to "MMKRs" and manipulate and connect among various "multiple molecular representations" (both indicators for conceptual understanding in chemistry). Food chemistry topics were also beneficial for students who did not learn in a CBL approach, however, with a lower effect. More students in the "context-based group" have shifted to being high achievers. Additionally, low-achievers have progressed significantly more than medium and high achievers. This research connects CBL to aspects of the practice of 'developing and using models' in chemistry, and a way to look at, and assess, conceptual understanding in chemistry.

Extension concept mapping is a technique to connect prior existing concept maps with new knowledge structures. It offers advantages in each stage of the knowledge-integrating process and encourages learners to improve their performance. While previous studies have confirmed that the extended kit-build concept map outperformed the extended scratch-build approach in terms of comprehension test scores and map size, they have yet to evaluate the quality of concept maps and students' perceptions. Although the size of the concept map components could represent the breadth of personal knowledge, it does not constantly describe the good knowledge structure. In addition, the student's degree of acceptance after the concept mapping demonstrates their intention to use systems in the future. The present study aims to compare the effect of extended scratch-build and extended kit-build on the students' quality of knowledge structures and perceptions. Fifty-five second-year university students were involved and divided into two groups: control and experimental. The control group utilized the extended scratch-build map, while the experimental group used the extended kit-build concept mapping tool. Quality of propositions and structural map scores as learning outcomes were used to measure the students' knowledge structures. The possibility of a relationship between quality scores was expressed using the Spearman correlation. This study involved the Technology Acceptance Model to confirm the students' perceptions of extension concept mapping tools. The perceived ease-of-use, perceived usefulness, and behavioral intention constructs were used to investigate users' intentions. The findings suggest that the quality of propositions and structural map scores in the experimental group were significantly higher than in the control group. This study also found that the extended kit-build method achieved better perceptions scores than the extended scratch-build.

There has been a long-standing debate on situational and symbolic mathematics, which is associated with how to design and execute mathematics education for all students. Brain studies can give some clues for how to deal with the debate. There are situational, verbalized, and visuospatial brain networks and the connectivity among the networks, which suggest the representation of mathematical knowledge should be situational, verbalized, and symbolic. Moreover, the representation should be mapped onto one another. Previous studies have shown the importance of situational and verbalized mathematics, but they are typically treated as preliminary and auxiliary introductions for formal symbolic mathematics. Each of the three mathematics types and their mapping would be the core learning goal in mathematics education.

This study examined student language in learning the human circulatory system, to understand the challenges they face in representing the knowledge in this topic. Data for this study comprised students' written responses to a range of structured test items. Responses to 17 test items from 70 students across four classes were analysed using tools adapted from the Systemic Functional Linguistics framework using the seven categories of: "Process" error, "Participant" error, "Descriptor" error, "Circumstance" error, "Connective" error, Missing word and Irrelevant word. The analysis revealed six problems in language use that occurred in all four classes taught by three different teachers. Students in the study encountered most difficulties with the use of "Participants." Reasons for the prevalence of this error type are proposed. The problematic nature of student language use is discussed in relation to both the conceptual demands inherent in the topic and the scientific practice of language generation in science. Examining the student language illuminates the need for students to understand the rationale for the finely distinguished terminology found in this topic.

A form-preparation task in the language production field was adopted to examine output phonological representations in Chinese dyslexia and their susceptibility to training. Forty-one Chinese children with dyslexia (7-11 years old) and 36 chronological age controls completed this task. The controls demonstrated a marginally significant syllable facilitation effect (d = -0.13), indicating their use of syllable-sized phonological representations during speech production, while the group with dyslexia showed a significantly different pattern (d = 0.04), opposite to the direction of a facilitation effect. The children with dyslexia were then randomly assigned to either metalinguistic training (N = 22) or working memory training (N = 19). Only the metalinguistic training subgroup demonstrated a significant syllable facilitation effect afterward (metalinguistic: d = -0.13; working memory: d = -0.01). The results suggest the presence of a phonological representation deficit at the syllable level in Chinese dyslexia and its possible remediation by metalinguistic training. Such a phonological deficit in readers of a logographic script strongly supports the impaired phonological representation view of developmental dyslexia. A video abstract of this article can be viewed at https://youtu.be/zT2Be0xMkh0.

Analysis of principles knowledge representation in information systems led to the necessity of improving the structuring knowledge. It is caused by the development of software component and new possibilities of information technologies. The article combines methodological aspects of structuring knowledge and effective usage of information resources which are designed for the scientific and methodological study of practical recommendations in Digital learning implementation. It is shown at the paper that one of the most important problems, which face the processing of knowledge or construction systems, is knowledge representation. The main idea of the paper is increasing the efficiency at the university learning process on the basis of a possible applications and rational structuring knowledge. The proposed concept of using information resources of Digital Learning is based on the idea of using the principles of abstraction, encapsulation, modularity, hierarchy, typing, concurrency preservation and implementation in such stages of this process as algorithmic support of knowledge structuring and structured transfer of knowledge to students. The topic of the article is unique as we try to see Digital learning in multidisciplinary aspects: education, mathematics, and statistics. The article combines a theoretical approach to structuring knowledge that is based on the integrated usage of fuzzy semantic network, theory of predicates, Boolean functions, theory of complexity of network structures and practical aspects and ways of construction Digital Learning Systems at the University

STEM instruction often uses visual representations. To benefit from these, students need to understand how representations show domain-relevant concepts. Yet, this is difficult for students. Prior research shows that physical representations (objects that students manipulate by hand) and virtual representations (objects on a computer screen that students manipulate via mouse and keyboard) have complementary advantages for conceptual learning. However, physical and virtual representations are often embedded into different social classroom contexts, which may affect social mechanisms through which students construct connections between concepts and representations. Therefore, this paper focuses on the social events that precede concept-representation connections. Twelve high-school students worked collaboratively with physical and virtual representations of chemistry. Frequent pattern mining of discourse data was used to identify social events that often preceded concept-representation connections. Qualitative analysis investigated social mechanisms through which these events may enhance concept-representation connections. Results show that students construct concept-representation connections incrementally. Further, meta-cognitive strategies and instructor prompts often preceded concept-representation connections. Finally, differences between physical and virtual representations were mainly due to different social supports being available in the context in which the representations were embedded. I discuss contributions to literature on learning with multiple representations and to interventions that blend representation modes.

The purpose of the current study is to determine the effect of talking drawings on Turkish preschool children's mental models of the water cycle. The study was conducted in the city of Kastamonu, located in the north-west of Turkey. A total of 40 five-year-old preschool children participated in the study in the spring term of the 2015-2016 school year. Within the context of the study, prior to the initiation of the experimental process, the children were asked to illustrate their opinions about the water cycle through drawings. At the end of the experimental process, they were asked once more to draw pictures to depict the water cycle, after which the code differences between the drawings were determined. At the end of the study it was found that, before the initiation of the experimental process, the codes used by the children most frequently within the framework of the water cycle could be presented in rank order as follows: rain (f = 42, 95.4%), cloud (f = 36, 81.8%) and human (f = 24, 54.5%); after the completion of the experimental process, the same rank order was found: rain (f = 39, 88.6%), cloud (f = 39, 88.6%) and human (f = 28, 63.6%). On the basis of the post-test results, the Mann-Whitney U Test was conducted and revealed a significant difference in the children's drawings in favor of the experimental group (U = 28, z = -5.531, p = 0.000, r = 0.8). Thus, it was concluded that the technique of talking drawings had a positive effect on the children's mental models of the water cycle. In light of this finding, it can be stressed that the technique of talking drawings built on both student-student and teacher-student dialogues, and we recommend that a great emphasis be placed on group work in early childhood science education.

The study aimed to know how does mind-map software assess the representation of learners' knowledge and how brain tester software assess the performance of left, right, auditory, and visual lobes of the brain to represent information. It also aimed to study the differential effectiveness of audio, visual, and audio-visual information to represent knowledge. Mind map tool used to know the students' knowledge representation in science, and for that purpose, different audio, visual, and audio-visual tools used to know how learners process cognitive information; those were experienced in the study. There was no hypothesis to draw inferences but three working hypotheses or the research questions framed to find out the result. Every individual has an independent information processor in left, right, auditory, and visual lobe of brain to represent information. It was also resulted that audio-visual information was more effective to process and represent knowledge than audio and visual information.

Students try to understand many abstract concepts by using mental models they have created by thinking and imagining. Students must have a functional and dynamic atomic model in their minds in order to understand the atomic and atomic concepts. In this study; it was aimed to determine the models of science student candidates using the analogy of atomic concepts by using analogies. The study carried out in the spring semester of 2016-2017 academic year; 47 science teachers were involved with the undergraduate students studying in the second grade. In the study, it was desired to establish an analogy of the atomic concept and to explain this analogy in order to determine the atomic models in the minds of the prospective teachers. Subsequently, in the course of modern physics, the students were told about the atom and atom modeling, and they were asked to establish an analogy with the atomic concept. First of all, their characteristics were examined by analyzing the types of analogies, origin, relationship and similarity that teacher candidates established. Then the answers given by the teacher candidates were separated by categorized by qualitative analysis method and the frequency values were obtained. It has been determined that prospective teachers use daily information in their analogies and use concrete concepts to establish analogies. It is also seen that teacher candidates often try to establish similarities in their analogies in their constructs. Teacher candidates have established similarities in the analogies of atoms such as shape, orientation, disintegration. The analogies used by the prospective teachers initially represented Dalton, Thomson and Bohr atom models; and modern atomic theory, Bohr and Rutherford atomic models. It has been determined that the students do not represent any atomic model of the analogy established by the 7th modern physics lesson before 19th.

In this paper, we demonstrate how machine learning could be used to quickly assess a student's multimodal representational thinking. Multimodal representational thinking is the complex construct that encodes how students form conceptual, perceptual, graphical, or mathematical symbols in their mind. The augmented reality (AR) technology is adopted to diversify student's representations. The AR technology utilized a low-cost, high-resolution thermal camera attached to a smartphone which allows students to explore the unseen world of thermodynamics. Ninth-grade students (N = 314) engaged in a prediction-observation-explanation (POE) inquiry cycle scaffolded to leverage the augmented observation provided by the aforementioned device. The objective is to investigate how machine learning could expedite the automated assessment of multimodal representational thinking of heat energy. Two automated text classification methods were adopted to decode different mental representations students used to explain their haptic perception, thermal imaging, and graph data collected in the lab. Since current automated assessment in science education rarely considers multilabel classification, we resorted to the help of the state-of-the-art deep learning technique-bidirectional encoder representations from transformers (BERT). The BERT model classified open-ended responses into appropriate categories with higher precision than the traditional machine learning method. The satisfactory accuracy of deep learning in assigning multiple labels is revolutionary in processing qualitative data. The complex student construct, such as multimodal representational thinking, is rarely mutually exclusive. The study avails a convenient technique to analyze qualitative data that does not satisfy the mutual-exclusiveness assumption. Implications and future studies are discussed.

Do we structure object-related conceptual information according to real-world sensorimotor experience, or can it also be shaped by linguistic information? This study investigates whether a feature of language coded in grammar--numeral classifiers--affects the conceptual representation of objects. We compared speakers of Mandarin (a classifier language) with speakers of Dutch (a language without classifiers) on how they judged object similarity in 4 studies. In the first 3 studies, participants had to rate how similar a target object was to 4 comparison objects, 1 of which shared a classifier with the target. Objects were presented as either words or pictures. Overall, the target object was always rated as most similar to the object with the shared classifier, but this was the case regardless of the language of the participant. In a final study using a successive pile-sorting task, we also found that the underlying object concepts were similar for speakers of Mandarin and Dutch. Speakers of a nonclassifier language are therefore sensitive to the same conceptual similarities that underlie classifier systems in a classifier language. Classifier systems may therefore reflect conceptual structure, rather than shape it.

When using the Internet to learn about complex topics or issues, students often encounter information that is both complementary and conflicting. Building on prior work identifying differences in how students reason about multiple conflicting texts, we examine students' connection formation and summative conceptualization of texts systematically designed to vary in a number of ways. We examine students' connection formation and summative conceptualization of texts introducing consistent or conflicting main ideas, accompanied by supporting reasons that are overlapping or distinct. We investigate the extent to which students form three types of connections across texts: "evidentiary" (i.e., corroborating specific information across texts), "thematic" (i.e., connecting main ideas), and "contextual" (i.e., comparing texts' meta-features such as author). Moreover, we analyze how students summatively describe texts related to one another in a variety of ways. In general, the text condition reflecting the greatest degree of cross-textual consistency (i.e., texts that included consistent main ideas and that presented overlapping supporting reasons) resulted in students being able to form a greater number of cross-textual connections, overall, and more evidentiary and thematic connections, in particular, as compared to the most discrepant text condition (i.e., when texts had conflicting main ideas and presented distinct supporting reasons). Discrepant main ideas across texts were found to increase students' contextual connection formation.

Many recent technologies provide the ability to dynamically adjust the interface depending on the emerging cognitive and collaborative needs of the learning episode. This means that educators can adaptively re-design the learning environment during the lesson, rather than purely relying on preemptive learning design thinking. Based on a three-semester design-based research study this paper explores how adaptive learning design can be used to provide learning environments that enable more effective collaboration and representation of information. The analysis culminates in a framework for adaptive learning design of a web-conferencing environment that depends on the type of knowledge being represented and the nature of interaction anticipated. Heuristics for adaptive learning design in synchronous multimodal environments are presented, and the potential role of students as co-designers is also discussed.

The education of architectural designers begins by learning drawing and digital modelling following the notion that students learn these new modes as instruments of thinking in design process. Curricular arguments persist about which mode should follow the other. Difficulties occur when one mode replaces the other. Students uninitiated to design seem to prefer the more immediate volumetric visualization of digital modelling over plans, sections, and elevations, representational views resulting from the un-real "viewpoint" of the section-cut, a means only drawn out of reality through a way-of-looking NOT natural-to-experience. Therefore, the primary difficulty in learning to think through drawings is their abstraction from, rather than connection to, realness--a needless initiating ordeal that confuses rather than clarifies. Digital modelling offers virtual three-dimensional images that seem to students, by contrast, not quite as abstracted from natural experience, albeit framed by non-physical, seductive, machine "otherness". This paper proposes drawing pedagogy that learns from digital modelling by making connections rather than distinctions that more seamlessly connect abstract to actual. Projects will be demonstrated that manipulate three-dimensional forms to initiate drawing learning experiences. Drawing and its abstractions can thus more readily be drawn out of experience and made ultimately more concrete for design thinking.

Knowing what skills underlie college success can allow students, teachers, and universities to identify and to help at-risk students. One skill that may underlie success across a variety of subject areas is structure building, the ability to create mental representations of narratives (Gernsbacher, Varner, & Faust, 1990). We tested if individual differences in structure-building ability predicted success in two college classes: introductory to psychology and introductory biology. In both cases, structure building predicted success. This effect was robust, with structure building explaining variance in course grades even after accounting for high school GPA and SAT scores (in the psychology course) or a measure of domain knowledge (in the biology course). The results support the claim that structure building is an important individual difference, one that is associated with learning in different domains. [This article was published in "Applied Cognitive Psychology," v30 n3 p454-459 May-Jun 2016.]

The purpose of this study was to describe the effect of multiple knowledge representations of physical and chemical changes on the development of primary pre-service teachers' cognitive structures. The study took place in an introductory general chemistry laboratory course in a four-year teacher education program. Multiple knowledge representations in chemistry refer to the macroscopic (visible), sub-microscopic (invisible), and symbolic (formulas and equations). The study adopted one group pretest-posttest design supported by qualitative data. Forty primary pre-service teachers participated in this study. The results revealed that enabling the primary pre-service teachers to learn multiple representations of physical and chemical changes was effective in developing both groups of pre-service teachers' cognitive structures, low and high-level understanding of particulate nature of matter, the latter benefitting the most. This finding was instructive because it emphasizes the difficulty that some primary pre-service teachers had on the particulate and symbolic representations of physical and chemical changes. The improvement in primary pre-service teachers' cognitive structures of physical and chemical change by the use of multiple representations.

Background: Engineering students commonly learn domain knowledge by engaging with visual representations of it. However, at times they have trouble accessing information from these representations due to the way information is encoded in features of the representation. Purpose: To describe how students engage with representation features, we explored two research questions: (a) what is the interplay between how concepts are encoded within representations, students' use of those concepts, and how students translate between representations during their problem-solving and (b) how is the interplay described in Research Question 1 similar and different across students in statics versus those in digital logic? Design/Method: We synthesized findings from two of our prior research studies using the constant comparative method. We described the effect of representations on students' ability to access and use domain knowledge during problem-solving within and across engineering disciplines. Results: We identified three themes that describe how visual representations affect students' reasoning. First, students conflated concepts that were represented using similar features. Second, students consistently failed to use concepts that were not represented with visually salient features. Third, statics students coordinated multiple representations when translating between representations during problem-solving more often than the digital logic students. Conclusions: These themes provide possible domain-general pathways for redesigning the notation and representations that we use to teach engineering concepts and suggest future avenues of research to further explore the generality of these findings.

Attribute-framing bias reflects people's tendency to evaluate positively framed objects more favorably than the same objects framed negatively. Most theoretical accounts of this bias emphasized the role of positive- and negative-framing valence in the message, disregarding the quantitative information that typically accompanies it. To examine the role of both framing valence and detailed quantitative information in attribute-framing bias, we applied the distinction between gist and verbatim representations, as proposed by fuzzy-trace theory. We hypothesized that gist representations retain the framing valence used in the scenario, consequently eliciting biased positive or negative evaluations, whereas verbatim representations retain detailed quantitative information that allows for fine-tuning of the evaluations reflective of the magnitude of the target attribute. In 2 experiments, we compared precise presentations of different magnitudes using percentages and pie charts with vague presentations using verbal descriptions. A substantial attribute-framing bias was found for both the precise and vague presentation conditions, consistent with the hypothesis that the framing bias is driven by coarse and imprecise gist representation. Critically, however, the findings reveal higher correlations between evaluations and the magnitude of the target attribute in the precise presentation conditions (percentages and pie charts) compared with the vague verbal presentation. This finding suggests a process of fine-tuning of the evaluations when a detailed verbatim representation of the quantitative information is available. We discuss the findings in view of the distinction between gist and verbatim representations and propose future research to examine similar cognitive mechanisms accounting for biases in judgment and decision making.

Mobile applications are rapidly growing in importance and can be used for various purposes. They had been used widely in education. One of the educational purposes for which mobile applications can be used is learning the right way to read and pronounce the verses of the Holy Quran. There are many applications that translate the Quran into several languages. In addition, there are applications that help users to read the Quran, and search for a particular word or phrase in the text as well as listening to verses of the Holy Quran. This research aims to study the relationships of behavioral factors and perceived usefulness of using the mobile application "Say Quran" for learning Quran on students' perceived performance, satisfaction and behavior. In this research a group of 118 students of the Computer Sciences and Information Systems College at Al Imam Muhammed Bin Saud Islamic University who are studying the Holy Quran course had been asked to use the application to help them on studying the Quran, then a survey had been distributed in order to collect the data. The results from this study provide evidence that there is a positive relationship between mobile application "Say Quran" and students' perceived performance, satisfaction and behavior while engaged in studying the Holy Quran.

Schema is one of the most common terms used for classifying and constructing knowledge. Therefore, a schema is a pre-planned set of concepts. It usually contains social information and is used to represent chain of events, perceptions, situations, relationships and even objects. For example, Kant initially defines the idea of schema as some natural structures that enable us to comprehend nature. According to the perspective of Kant who is a strong nativist, the provided opportunities of human mind help us to comprehend temporal relations and their components. In the 1930s, a content of schema idea similar to Kant's was represented again in a more planned and intentional way by Bartlett from Cambridge University in his study "Remembering: A Study in Experimental and Social Psychology (1932/reprint,1950)" Therefore, it is vital for readers to compute information using textual or discourse-related means and integrate their background information in the boundaries of textual content and context. Although it is related to the past, Bartlett's approach "[s]chema is an active organization of past experience and reactions. Environmental stimulus contribute to these organized schema" is successful to protect its effects even in today thanks to abstraction of linguistic ideas (cognitive psychology and language), language processing and examination of memory in its natural context. Considering that children who attend elementary school constantly live in close contact with "verbal or written discourse products", the only certainty for all these efforts to comprehend is that it is necessary to continue to produce data for all grade levels until a more integrated approach, supported by more comprehensive and reliable data, is developed. Considering that the research on text interpretation processes have not yet provided an applicable, stand alone alternative, it is evident that such an alternative must benefit from a versatile and interdisciplinary network of information. Furthermore, it is known in the literature on the subject that countless discussions have been and will be held around the scope in question. For instance, despite all the scientific discussions, no assumptions on the nature and processes of text interpretation independent of statements of other fields can be developed.

The aim of this research is identify the effectiveness of a multiple representation-based learning model, which builds a mental model within the concept of atomic structure. The research sample of 108 students in 3 classes is obtained randomly from among students of Mathematics and Science Education Studies using a stratified random sampling technique. The same number of students formed the control group. In the experimental class, the learning was conducted by using multiple representations, whereas the control classes undertook conventional learning. Result of the research shows that (1) Learning with multiple representations is more effective in constructing students' mental models in understanding the concept of atomic structure compared with the conventional learning; (2) Learning with multiple representations is suitable for lessons in classes where the students have low capability level to keep up with those who have a medium and high capability level. These findings indicate that lessons, which involve the macro-sub-micro-symbolic phenomena using multiple representations, may improve their mental model and effectiveness of atomic structure studies. The learning model is discussed as an alternative in class lessons in order for the students with initially low capability to keep up with those of medium and high capability in constructing their mental models.

Knowledge of the appropriate technologies for sustainable development projects has encouraged grass roots development, which has in turn promoted sustainable and successful community development, which a requirement is to share and reuse this knowledge effectively. This research aims to propose a tutorial ontology effectiveness modeling on organic rice farming as an appropriate technology based on sustainable development projects for non-science and technology educated farmers using knowledge engineering approach, using Phrao District in Chiang Mai Province, Thailand as a case study. The effective tutorial ontology model focuses on social science and technology ontologies based on Thai's curriculum of lower secondary school which provides biology, chemistry, math and physics concepts in order to effectively represent knowledge of the organic rice farming. The additional social science ontology knowledge developed in this research provides such a support to vocational learning and used by rural community in the case study to effectively navigate and utilize the appropriate technological knowledge of the sustainable development project knowledge to enhance their communities. The effectiveness of tutorial social science ontology in learning process was measured by counting and validating the average throughput of organic rice farming domain knowledge in learning process in terms of practicing domain knowledge, appropriate domain knowledge with their community and acquiring knowledge by themselves in both control and experimental groups. This study concludes by emphasizing the benefits of effective tutorial ontology modeling on organic rice farming for supporting knowledge transfer technique for non-science and technology educated farmers using knowledge engineering in the community of the case study to enhance their vocational lifelong learning. Ultimately, the tutorial ontology modeling in appropriate technology provides a knowledge transfer for non-science and technology educated farmers effectively.

This journal article discusses Advanced Mathematical Thinking (AMT) and how to enhance it. AMT is ability in representing, abstracting, creative thinking, and mathematical proving. The importance of AMT ability development in accord with government expectation who realize about the importance of mathematical competency mastery for student's life. The advantage in developing AMT among others are: 1) student capable to deliver idea which is possessed in verbal, symbol, table, graphic and diagram. 2) Find deep connection among mathematic branches, 3) know the outcome in one of branch which can trigger conjecture in branch which is related, 4) technique and method from one branch can be applied to prove the outcome in branch which is related, 5) capable to find new idea in problem solving, and 5) capable to understand and construct the proof. One of theory which not only enhance student's learning outcome but also can construct knowledge through student's mental activity, enhance creativity, create class atmosphere which is enjoyable and challenging, and capable to enhance student's AMT ability is APOS approach.

This study aims to explore pre-service elementary teachers' understandings of the environment. A survey method was carried out in this study. A close-ended questionnaire and Draw-An-Environment Test (DAET) are administered to pre-service teachers (N = 255) after instruction of an Environmental Education course. A rubric (DAET-R) is used for assessing the mental models or images of the environment held by preservice teachers. Results of this study suggest that the participants' mental models of the environment are incomplete. Majority of participants' drawings reflect biotic and abiotic environment. Few drawings include human beings as a part of the environment. The results shows that mean scores do not differ regarding to gender, environmental education background, high school type, parental education level, parental occupation, and monthly family income.

This installation is one of a series made and being made while the author writes a life-long poem chronicle, "thecanadaproject." The photographs were taken as the author sifted through her personal archive--a collection that is at once intimate and filled with fragments from a public repository: that of the bombing of an airplane in 1985, in which her aunt and uncle were passengers. The author began a process of writing about the bombing in 2009, and that process led her to the work of Angela Failler, and in conversation with her, to the ideas of R. I. Simon. The current photo series explores the idea of a "transactional space" where private grief meets public memory-history, as unsettled and ongoing--where objectification is toyed with, that which is inherent in record-keeping, in news gathering, and in all forms of representation.

This study (Note 1) investigates potential differences in language use between genders, by applying a modified model of thought representation. Our hypothesis is that women use more direct forms of thought representation than men in modern spoken British English. Women are said to favour "private speech" that creates intimacy and nearness through discourse, which involves direct forms of speech, and thought, representation. Men are said to prefer a more distancing "public speech" style, in order to maintain independence and to hold and negotiate status, which often involves the display of skill and knowledge. In order to investigate this hypothesis, we examine a slightly modified form of the Lancaster SW & TP Spoken corpus, which has been tagged for the full spectrum of the primary categories of thought representation. The results of this study prove our thesis to be correct, there are statistically highly significant differences between the genders' use of direct forms of thought representation. British women use the direct forms more than their male counterparts. This greater use of direct thought categories in their daily discourse depicts a lucidity and consciousness, supposedly faithfully repeating the actual thoughts of the speaker, and it often occurs in a moment of heightened emotional or cognitive state. Therefore, because women seem to be able to express their emotions more lucidly then men, and are more inclined to express their thoughts in more detail, they tend to use more direct forms of thought representation than men in daily discourse.

Since the initial recognition that human knowledge is structured in a relational manner, technologies have been developed for assessing and analyzing the "structure" of knowledge for a variety of purposes. A computer-based text analytic offline software system, "ALA-Reader," that was developed to assess this knowledge structure (KS) reflected in a text has been modified and improved since its initial announcement (Clariana 2004) through a number of investigations in various kinds of learning environments across several languages. Based on the empirical evidence from the "ALA-Reader," we have recently developed the online version of the "ALA-Reader," called Graphical Interface of Knowledge Structure ("GIKS"), that can immediately convert students' writings into visually represented KS network graphs to indicate specific areas of their knowledge strengths and weaknesses compared to the referent KS (e.g., a teacher), regardless of which language is used. This paper presents an overview of the "ALA-Reader" system and applications, as well as the implication of the "GIKS" system in online contexts.

Constructing a knowledge representation from multiple texts requires the integration of information across texts. The aim of the current study was to investigate how elementary school students integrate information across multiple text passages and, particularly, whether students use information from a prior text to improve understanding of a current text. A sample of 105 children in grades 4 and 6 participated in the experiment. The multiple-text integration paradigm was used to study integration processes across texts during reading. Recall and (application) questions were used to investigate the extent to which information from different text passages was integrated into knowledge representations after reading. Individual differences in reading comprehension ability and working memory were also considered. The results indicate that children in both grades spontaneously activate information from an earlier text to aid their understanding during reading, and that they integrate information across texts in their knowledge representations. This was the case regardless of grade or individual differences in reading-comprehension ability and working memory. These findings provide insight into the mechanisms that may be involved in the integration of information across texts.