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4 results for “argument maps”
Argument maps as tools to support the development of new media literacies: a systematic review.
<p>The post-digital era is characterized by the vast presence of platforms imposing their digital affordances and algorithmic control on our behavior. This environment is challenging education and training, with implications for digital and transmedial literacy. Investigating instructional methodologies is crucial to foster critical comprehension of such novel informational environments. The argument maps (AM), which were first created and evaluated in static information contexts (analogical/old web), could be useful in the emergence of dynamic (post-digital) textual forms.</p> <p>The current paper describes a comprehensive literature review based on the assumptions above. We looked into state of the art in research on using AM to handle dynamic information. We found 150 papers using a PRISMA procedure and then examined 25 of them. Our review produced pertinent data about the current state of AM, including the sorts of texts on which they are used and the tools (especially digital and AI-based) that have been employed. Our research lays the groundwork for teaching the literacies needed in new informational settings, such as multimodal, dynamic, algorithmic, and data-driven contexts, with a specific focus on AM as an effective mediational tool.</p> <p>This Zenodo record presents the full dataset composed of the following sheets:</p> <ol> <li>Codebook</li> <li>Italian Journals</li> <li>List of articles extracted from SCOPUS</li> <li>List of articles extracted from ERIC</li> <li>List of articles extracted from WOS</li> <li>List of articles extracted from DOAJ</li> <li>PRISMA workflow</li> <li>Analysis - First Level (classification of 25 articles selected)</li> <li>Analysis - Second Level (List of 19 articles selected)</li> <li>Final Dataset</li> <li>Interrater Agreement.</li> </ol> <p>As for the Keywords' Map, a primo file .txt displays the text over which basis was performed the keyword maps analysis. The second .txt file shows notes relating to the analysis procedures using the software VOS-Viewer <a href="http://www.vosviewer.com/">http://www.vosviewer.com/</a> </p> <p>Any comments or improvements are welcome!</p>
Dataset on "Argument maps as a proxy for critical thinking development: A Lab for undergraduate students"
<p>Argumentative skills are crucial for any individual at the personal and professional levels. In recent decades, there has been an increasing concern about the weak undergraduates' skills and considerable difficulty in reworking and expressing one's own reflection on a topic. In turn, this has implications for being a critical thinker, able to express an original point of view. Tailored interventions in Higher Education could constitute a powerful approach to promote argumentative skills and extend these skills to professional and personal life. In this regard, argument maps (AM) could prove to be a valuable support to the visualization process of arguments. They don’t just create associations between concepts, but trace the logical relationships between different statements, allowing you to track the reasoning chain and understand it better. We conducted an experimental study to investigate how a path with AM could support students in increasing the level of text comprehension (CoT) competence, in terms of identifying the elements of an argumentative text, and critical thinking (CT), in terms of reconstructing meaning and building their own reflection. </p> <p>Our preliminary descriptive analysis suggested the positivity of the role of AM in increasing students’ CoT and CT proficiency levels</p> <p> </p> <p>This Zenodo record follows the full analysis process with R (https://cran.r-project.org/bin/windows/base/ ) composed of the following datasets and script:</p> <p>1. Comprehension of Text and AMs Results - ExpAM.xlsx</p> <p>2. Critical Thinking Results - CriThink.xlsx</p> <p>3. Argumentative skills in Forum - ExpForum.xlsx</p> <p>4. Selfassessment Results - Dataset_Quest.xlsx</p> <p>5. Data for Correlation and Regression - Dataset_CorRegr.xlsx</p> <p>6. Descriptive Statistics - Preliminary Analysis.R</p> <p>7. Inferential Statistics - Correlation and Regression.R</p> <p> </p> <p>Any comments or improvements are welcome!</p>
Maps and AI: a lab to support undergraduates' argumentative skills
<p>Argumentative abilities are required both individually and professionally to digest complex information (CoI) related to the critical reconstruction of meaning (critical thinking - CT). This is still a particularly important objective, especially in the age of social media and artificial intelligence-mediated information. Recently, the advent of the generative artificial intelligence (GenAI), with the specific example of ChatGPT (OpenAI, 2022), has made it easier to obtain and share knowledge, and new tools are sorely needed to deal with the abundance of post-digital information without getting lost.</p> <p>The research activity aimed to investigate the extent to which AMs, already used to develop argumentative skills and critical thinking, helped students increase their level of comprehension of multimodal information and their ability to rework their own opinion about it. Juxtaposed to this line of thinking was the idea of adopting the ChatGPT intelligent chatbot to extrapolate and analyze whether an initial communicative and argumentative interaction with an intelligent agent would help students reframe their arguments. </p> <p>Our study was based on a two-group quasi-experiment with 24 students in the Education Evaluation teaching, from the Bachelor of Science in Education and Training, Education and Human Resource Development curriculum, University of Padua (L-19). The control group (G1) conducted the information comprehension and reprocessing course in an analogue context, and the experimental group (G2) was exposed to multimodality with texts and tutorials bearing dynamic and digital elements. Both groups then interacted with ChatGPT, integrated within an interactive lecture in the middle of the course. </p> <p>Preliminary analyses showed that in both groups, AMs improved students' text comprehension and critical thinking. The group that used analog texts (G1) showed more uniform improvement than the group with multimodal texts (G2), which had greater variability in results. However, multimodality stimulated deeper and more integrative reflection. The use of ChatGPT showed potential to improve information reprocessing, with students recognizing the usefulness of AI and taking a critical approach to its interaction. Future research will aim to better integrate these tools to maximize learning.</p> <p>This Zenodo record follows the full analysis process with R (https://cran.r-project.org/bin/windows/base/ ) composed of the following datasets and script:</p> <p>1. Comprehension of Text and AMs Results - Arg_G1.xlsx & Arg_G2.xlsx</p> <p>2. Opinion and Critical Thinking level - Opi_G1.xlsx & Opi_G2.xlsx</p> <p>3. Data for Correlation and Regression - CorRegr_G1.xlsx & CorRegr_G2.xlsx</p> <p>4. Descriptive and Inferential Statistics Comprehension and AMs Building - Comprehension&AM_VAL.R</p> <p>5. Descriptive and Inferential Statistics Opinion and Critical Thinking level - Opinion&CriticalThinking_VAL.R</p> <p>6. Correlation and Regression - Analysis_VAL.R</p> <p> </p> <p>Any comments or improvements are welcome!</p>
Le mappe argomentative come comparator di feedback interno: un laboratorio per studenti universitari [Argument maps as comparator for internal feedback: a Lab for undergraduate students ]
<p>Il feedback interno è un costrutto divenuto recentemente rilevante per il suo impatto sulla regolazione metacognitiva e affettivo-relazionale, in relazione a diverse skills e contesti formativi. In particolare, il concetto di comparator, ovvero gli strumenti, gli interventi o le risorse che attivano il feedback interno, necessita di approfondimento tramite ricerca empirica. In questo contributo si parte dall’ipotesi che la componente visiva delle mappe argomentative (MA), già legate allo sviluppo di skills argomentative e di pensiero critico, possa essere una fonte generativa di comparazione concreta, permettendo una comprensione facilitata ed una migliore ricostruzione del senso dell’informazione testuale argomentativa. Infatti, le AM diagrammano le relazioni logiche fra i diversi enunciati, permettendo di seguire e di comprendere meglio la catena di ragionamento. Per verificare la suddetta ipotesi, è stato condotto uno studio sperimentale per indagare in che misura un percorso con le AM favorisse gli studenti nell’incremento di: a) feedback interno (FI) associato quindi a b) il livello di competenza di comprensione del testo (CoT) e a c) lo sviluppo di pensiero critico (CT). </p> <p>Questo record Zenodo presenta i quattro dataset (anonimizzati) raccolti durante la sperimentazione ed adottati per le analisi descrittive ed inferenziali, di cui si presenta una parte nel presente paper. Inoltre, si allega ilo Script R utile alla replica delle analisi. Qualsiasi commento o miglioramento è benvenuto!</p> <p>=========</p> <p>ENGLISH</p> <p>Internal feedback is a construct that become recently relevant for the impact it has on metacognitive and affective-relational regulation, in relation to different skills and learning contexts. In particular, the concept of comparator, i.e. the tools, interventions, or resources that activate internal feedback, requires the support of empirical research. In this contribution, we take as an initial hypothesis that the argument maps’ (AM) visual component, already linked to the development of argumentative and critical thinking skills, could be a generative source of concrete comparison, allowing for a facilitated comprehension and an improvement of the sense-making abilities within argumentative texts. In fact, AMs diagram the logical relationships between different utterances, allowing the learner to keep track and better understand the reasoning chain. To test the above hypothesis, an experimental study was conducted to investigate the extent to which a course with AMs favored students in increasing: a) internal feedback (IF), associated with b) their level of text comprehension (CoT) and hence, c) critical thinking (CT). </p> <p>This Zenodo record presents the four (anonymised) datasets collected during the experimentation and adopted for the descriptive and inferential analyses, part of which are presented in this paper. Also attached is the R Script useful for replicating the analyses. Any comments or improvements are welcome!</p>
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