Pub Date : 2023-12-26DOI: 10.1108/ils-06-2023-0087
Annette Markham, Riccardo Pronzato
Purpose This paper aims to explore how critical digital and data literacies are facilitated by testing different methods in the classroom, with the ambition to find a pedagogical framework for prompting sustained critical literacies. Design/methodology/approach This contribution draws on a 10-year set of critical pedagogy experiments conducted in Denmark, USA and Italy, and engaging more than 1,500 young adults. Multi-method pedagogical design trains students to conduct self-oriented guided autoethnography, situational analysis, allegorical mapping, and critical infrastructure analysis. Findings The techniques of guided autoethnography for facilitating sustained data literacy rely on inviting multiple iterations of self-analysis through sequential prompts, whereby students move through stages of observation, critical thinking, critical theory-informed critique around the lived experience of hegemonic data and artificial intelligence (AI) infrastructures. Research limitations/implications Critical digital/data literacy researchers should continue to test models for building sustained critique that not only facilitate changes in behavior over time but also facilitate citizen social science, whereby participants use these autoethnographic techniques with friends and families to build locally relevant critique of the hegemonic power of data/AI infrastructures. Originality/value The proposed literacy model adopts a critical theory stance and shows the value of using multiple modes of intervention at micro and macro levels to prompt self-analysis and meta-level reflexivity for learners. This framework places critical theory at the center of the pedagogy to spark more radical stances, which is contended to be an essential step in moving students from attitudinal change to behavioral change.
{"title":"A critical (theory) data literacy: tales from the field","authors":"Annette Markham, Riccardo Pronzato","doi":"10.1108/ils-06-2023-0087","DOIUrl":"https://doi.org/10.1108/ils-06-2023-0087","url":null,"abstract":"Purpose This paper aims to explore how critical digital and data literacies are facilitated by testing different methods in the classroom, with the ambition to find a pedagogical framework for prompting sustained critical literacies. Design/methodology/approach This contribution draws on a 10-year set of critical pedagogy experiments conducted in Denmark, USA and Italy, and engaging more than 1,500 young adults. Multi-method pedagogical design trains students to conduct self-oriented guided autoethnography, situational analysis, allegorical mapping, and critical infrastructure analysis. Findings The techniques of guided autoethnography for facilitating sustained data literacy rely on inviting multiple iterations of self-analysis through sequential prompts, whereby students move through stages of observation, critical thinking, critical theory-informed critique around the lived experience of hegemonic data and artificial intelligence (AI) infrastructures. Research limitations/implications Critical digital/data literacy researchers should continue to test models for building sustained critique that not only facilitate changes in behavior over time but also facilitate citizen social science, whereby participants use these autoethnographic techniques with friends and families to build locally relevant critique of the hegemonic power of data/AI infrastructures. Originality/value The proposed literacy model adopts a critical theory stance and shows the value of using multiple modes of intervention at micro and macro levels to prompt self-analysis and meta-level reflexivity for learners. This framework places critical theory at the center of the pedagogy to spark more radical stances, which is contended to be an essential step in moving students from attitudinal change to behavioral change.","PeriodicalId":44588,"journal":{"name":"Information and Learning Sciences","volume":"31 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139157574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-19DOI: 10.1108/ils-07-2023-0090
Susan Gardner Archambault
Purpose Research shows that postsecondary students are largely unaware of the impact of algorithms on their everyday lives. Also, most noncomputer science students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aims to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors and pedagogical considerations to aid faculty in teaching algorithmic literacy to postsecondary students. Design/methodology/approach Eleven semistructured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. A content analysis was manually performed on the transcripts using a mixture of deductive and inductive coding. Data analysis was aided by the coding software program Dedoose (2021) to determine frequency totals for occurrences of a code across all participants along with how many times specific participants mentioned a code. Then, findings were organized around the three themes of knowledge components, coping behaviors and pedagogy. Findings The findings suggested a set of 10 knowledge components that would contribute to students’ algorithmic literacy along with seven behaviors that students could use to help them better cope with algorithmic systems. A set of five teaching strategies also surfaced to help improve students’ algorithmic literacy. Originality/value This study contributes to improved pedagogy surrounding algorithmic literacy and validates existing multi-faceted conceptualizations and measurements of algorithmic literacy.
{"title":"Toward a new framework for teaching algorithmic literacy","authors":"Susan Gardner Archambault","doi":"10.1108/ils-07-2023-0090","DOIUrl":"https://doi.org/10.1108/ils-07-2023-0090","url":null,"abstract":"\u0000Purpose\u0000Research shows that postsecondary students are largely unaware of the impact of algorithms on their everyday lives. Also, most noncomputer science students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aims to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors and pedagogical considerations to aid faculty in teaching algorithmic literacy to postsecondary students.\u0000\u0000\u0000Design/methodology/approach\u0000Eleven semistructured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. A content analysis was manually performed on the transcripts using a mixture of deductive and inductive coding. Data analysis was aided by the coding software program Dedoose (2021) to determine frequency totals for occurrences of a code across all participants along with how many times specific participants mentioned a code. Then, findings were organized around the three themes of knowledge components, coping behaviors and pedagogy.\u0000\u0000\u0000Findings\u0000The findings suggested a set of 10 knowledge components that would contribute to students’ algorithmic literacy along with seven behaviors that students could use to help them better cope with algorithmic systems. A set of five teaching strategies also surfaced to help improve students’ algorithmic literacy.\u0000\u0000\u0000Originality/value\u0000This study contributes to improved pedagogy surrounding algorithmic literacy and validates existing multi-faceted conceptualizations and measurements of algorithmic literacy.\u0000","PeriodicalId":44588,"journal":{"name":"Information and Learning Sciences","volume":" 817","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138960392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.1108/ils-06-2023-0075
Lukas Höper, Carsten Schulte
Purpose In today’s digital world, data-driven digital artefacts pose challenges for education, as many students lack an understanding of data and feel powerless when interacting with them. This paper aims to address these challenges and introduces the data awareness framework. It focuses on understanding data-driven technologies and reflecting on the role of data in everyday life. The paper also presents an empirical study on young school students’ data awareness. Design/methodology/approach The study involves a teaching unit on data awareness framed by a pre- and post-test design using a questionnaire on students’ awareness and understanding of and reflection on data practices of data-driven digital artefacts. Findings The study’s findings indicate that the data awareness framework supports students in understanding data practices of data-driven digital artefacts. The findings also suggest that the framework encourages students to reflect on these data practices and think about their daily behaviour. Originality/value Students learn a model about interactions with data-driven digital artefacts and use it to analyse data-driven applications. This approach appears to enable students to understand these artefacts from everyday life and reflect on these interactions. The work contributes to research on data and artificial intelligence literacies and suggests a way to support students in developing self-determination and agency during interactions with data-driven digital artefacts.
{"title":"The data awareness framework as part of data literacies in K-12 education","authors":"Lukas Höper, Carsten Schulte","doi":"10.1108/ils-06-2023-0075","DOIUrl":"https://doi.org/10.1108/ils-06-2023-0075","url":null,"abstract":"\u0000Purpose\u0000In today’s digital world, data-driven digital artefacts pose challenges for education, as many students lack an understanding of data and feel powerless when interacting with them. This paper aims to address these challenges and introduces the data awareness framework. It focuses on understanding data-driven technologies and reflecting on the role of data in everyday life. The paper also presents an empirical study on young school students’ data awareness.\u0000\u0000\u0000Design/methodology/approach\u0000The study involves a teaching unit on data awareness framed by a pre- and post-test design using a questionnaire on students’ awareness and understanding of and reflection on data practices of data-driven digital artefacts.\u0000\u0000\u0000Findings\u0000The study’s findings indicate that the data awareness framework supports students in understanding data practices of data-driven digital artefacts. The findings also suggest that the framework encourages students to reflect on these data practices and think about their daily behaviour.\u0000\u0000\u0000Originality/value\u0000Students learn a model about interactions with data-driven digital artefacts and use it to analyse data-driven applications. This approach appears to enable students to understand these artefacts from everyday life and reflect on these interactions. The work contributes to research on data and artificial intelligence literacies and suggests a way to support students in developing self-determination and agency during interactions with data-driven digital artefacts.\u0000","PeriodicalId":44588,"journal":{"name":"Information and Learning Sciences","volume":" 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138994613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-18DOI: 10.1108/ils-07-2023-0088
C. Matuk, Ralph Vacca, Anna Amato, M. Silander, Kayla Desportes, Peter J. Woods, Marian Tes
Purpose Arts-integration is a promising approach to building students’ abilities to create and critique arguments with data, also known as informal inferential reasoning (IIR). However, differences in disciplinary practices and routines, as well as school organization and culture, can pose barriers to subject integration. The purpose of this study is to describe synergies and tensions between data science and the arts, and how these can create or constrain opportunities for learners to engage in IIR. Design/methodology/approach The authors co-designed and implemented four arts-integrated data literacy units with 10 teachers of arts and mathematics in middle school classrooms from four different schools in the USA. The data include student-generated artwork and their written rationales, and interviews with teachers and students. Through maximum variation sampling, the authors identified examples from the data to illustrate disciplinary synergies and tensions that appeared to support different IIR processes among students. Findings Aspects of artistic representation, including embodiment, narrative and visual image; and aspects of the culture of arts, including an emphasis on personal experience, the acknowledgement of subjectivity and considerations for the audience’s perspective, created synergies and tensions that both offered and hindered opportunities for IIR (i.e. going beyond data, using data as evidence and expressing uncertainty). Originality/value This study answers calls for humanistic approaches to data literacy education. It contributes an interdisciplinary perspective on data literacy that complements other context-oriented perspectives on data science. This study also offers recommendations for how designers and educators can capitalize on synergies and mitigate tensions between domains to promote successful IIR in arts-integrated data literacy education.
{"title":"Promoting students’ informal inferential reasoning through arts-integrated data literacy education","authors":"C. Matuk, Ralph Vacca, Anna Amato, M. Silander, Kayla Desportes, Peter J. Woods, Marian Tes","doi":"10.1108/ils-07-2023-0088","DOIUrl":"https://doi.org/10.1108/ils-07-2023-0088","url":null,"abstract":"\u0000Purpose\u0000Arts-integration is a promising approach to building students’ abilities to create and critique arguments with data, also known as informal inferential reasoning (IIR). However, differences in disciplinary practices and routines, as well as school organization and culture, can pose barriers to subject integration. The purpose of this study is to describe synergies and tensions between data science and the arts, and how these can create or constrain opportunities for learners to engage in IIR.\u0000\u0000\u0000Design/methodology/approach\u0000The authors co-designed and implemented four arts-integrated data literacy units with 10 teachers of arts and mathematics in middle school classrooms from four different schools in the USA. The data include student-generated artwork and their written rationales, and interviews with teachers and students. Through maximum variation sampling, the authors identified examples from the data to illustrate disciplinary synergies and tensions that appeared to support different IIR processes among students.\u0000\u0000\u0000Findings\u0000Aspects of artistic representation, including embodiment, narrative and visual image; and aspects of the culture of arts, including an emphasis on personal experience, the acknowledgement of subjectivity and considerations for the audience’s perspective, created synergies and tensions that both offered and hindered opportunities for IIR (i.e. going beyond data, using data as evidence and expressing uncertainty).\u0000\u0000\u0000Originality/value\u0000This study answers calls for humanistic approaches to data literacy education. It contributes an interdisciplinary perspective on data literacy that complements other context-oriented perspectives on data science. This study also offers recommendations for how designers and educators can capitalize on synergies and mitigate tensions between domains to promote successful IIR in arts-integrated data literacy education.\u0000","PeriodicalId":44588,"journal":{"name":"Information and Learning Sciences","volume":"97 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138965014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-11DOI: 10.1108/ils-03-2023-0026
J. Donaldson, Ahreum Han, Shulong Yan, Seiyon Lee, Sean Kao
Purpose Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis. Design/methodology/approach This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory. Findings The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations. Research limitations/implications LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning. Practical implications LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data. Originality/value To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.
{"title":"Learning experience network analysis for design-based research","authors":"J. Donaldson, Ahreum Han, Shulong Yan, Seiyon Lee, Sean Kao","doi":"10.1108/ils-03-2023-0026","DOIUrl":"https://doi.org/10.1108/ils-03-2023-0026","url":null,"abstract":"\u0000Purpose\u0000Design-based research (DBR) involves multiple iterations, and innovations are needed in analytical methods for understanding how learners experience a learning experience in ways that both embrace the complexity of learning and allow for data-driven changes to the design of the learning experience between iterations. The purpose of this paper is to propose a method of crafting design moves in DBR using network analysis.\u0000\u0000\u0000Design/methodology/approach\u0000This paper introduces learning experience network analysis (LENA) to allow researchers to investigate the multiple interdependencies between aspects of learner experiences, and to craft design moves that leverage the relationships between struggles, what worked and experiences aligned with principles from theory.\u0000\u0000\u0000Findings\u0000The use of network analysis is a promising method of crafting data-driven design changes between iterations in DBR. The LENA process developed by the authors may serve as inspiration for other researchers to develop even more powerful methodological innovations.\u0000\u0000\u0000Research limitations/implications\u0000LENA may provide design-based researchers with a new approach to analyzing learner experiences and crafting data-driven design moves in a way that honors the complexity of learning.\u0000\u0000\u0000Practical implications\u0000LENA may provide novice design-based researchers with a structured and easy-to-use method of crafting design moves informed by patterns emergent in the data.\u0000\u0000\u0000Originality/value\u0000To the best of the authors’ knowledge, this paper is the first to propose a method for using network analysis of qualitative learning experience data for DBR.\u0000","PeriodicalId":44588,"journal":{"name":"Information and Learning Sciences","volume":"10 34","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138584582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-11DOI: 10.1108/ils-07-2023-0091
Andy Nguyen, Joni Lämsä, Adinda Dwiarie, Sanna Järvelä
Purpose Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners’ SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA. Design/methodology/approach This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners (n = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL. Findings Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA. Originality/value This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts’ perspectives for designing SRLA. The results of the study will answer the questions of how learners’ voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.
{"title":"Lifelong learner needs for human-centered self-regulated learning analytics","authors":"Andy Nguyen, Joni Lämsä, Adinda Dwiarie, Sanna Järvelä","doi":"10.1108/ils-07-2023-0091","DOIUrl":"https://doi.org/10.1108/ils-07-2023-0091","url":null,"abstract":"\u0000Purpose\u0000Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners’ SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA.\u0000\u0000\u0000Design/methodology/approach\u0000This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners (n = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL.\u0000\u0000\u0000Findings\u0000Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA.\u0000\u0000\u0000Originality/value\u0000This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts’ perspectives for designing SRLA. The results of the study will answer the questions of how learners’ voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.\u0000","PeriodicalId":44588,"journal":{"name":"Information and Learning Sciences","volume":"10 26","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138584589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-07DOI: 10.1108/ils-02-2023-0019
Tali Gazit
Purpose The purpose of this study was to obtain valuable insights into students’ engagement and experiences within the virtual learning environment, especially in the context of crises. Among the innumerable challenges people throughout the world faced during the first year of the COVID-19 pandemic, those of students in institutions of higher education needing to engage in online academic studies are of special interest. Using an online survey, this study could predict students’ online engagement during the COVID-19 pandemic through three theoretical frameworks: the students’ academic motivation to study, the Big Five personality traits, and loneliness, and with a new tool measuring the participation in the Zoom platform. Design/methodology/approach To examine the psychological and technological factors predicting the students’ engagement, this study surveyed 547 students from different academic institutions of higher learning. Findings Findings show that the less lonely the students felt, the less neurotic they were, and the higher they scored in levels of extroversion, agreeableness, consciousnesses and openness to experience, the greater their engagement in their academic studies. In addition, students who were older, more educated, with higher intrinsic motivation and lower lack of motivation were more engaged in their online academic studies. Finally, participating in classes through the Zoom platform and experiencing it positively was a significant predictor of higher academic engagement. Originality/value Recognizing these factors can enable educators, institutions of higher learning, counselling services and students to obtain tools for higher engagement in online learning.
{"title":"“For students shall not live by Zoom alone”: psychological factors explaining the engagement of students during the COVID-19","authors":"Tali Gazit","doi":"10.1108/ils-02-2023-0019","DOIUrl":"https://doi.org/10.1108/ils-02-2023-0019","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study was to obtain valuable insights into students’ engagement and experiences within the virtual learning environment, especially in the context of crises. Among the innumerable challenges people throughout the world faced during the first year of the COVID-19 pandemic, those of students in institutions of higher education needing to engage in online academic studies are of special interest. Using an online survey, this study could predict students’ online engagement during the COVID-19 pandemic through three theoretical frameworks: the students’ academic motivation to study, the Big Five personality traits, and loneliness, and with a new tool measuring the participation in the Zoom platform.\u0000\u0000\u0000Design/methodology/approach\u0000To examine the psychological and technological factors predicting the students’ engagement, this study surveyed 547 students from different academic institutions of higher learning.\u0000\u0000\u0000Findings\u0000Findings show that the less lonely the students felt, the less neurotic they were, and the higher they scored in levels of extroversion, agreeableness, consciousnesses and openness to experience, the greater their engagement in their academic studies. In addition, students who were older, more educated, with higher intrinsic motivation and lower lack of motivation were more engaged in their online academic studies. Finally, participating in classes through the Zoom platform and experiencing it positively was a significant predictor of higher academic engagement.\u0000\u0000\u0000Originality/value\u0000Recognizing these factors can enable educators, institutions of higher learning, counselling services and students to obtain tools for higher engagement in online learning.\u0000","PeriodicalId":44588,"journal":{"name":"Information and Learning Sciences","volume":"46 10","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138593375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-07DOI: 10.1108/ils-06-2023-0076
Leanne Bowler, Irene Lopatovska, Mark S. Rosin
Purpose The purpose of this study is to explore teen-adult dialogic interactions during the co-design of data literacy activities in order to determine the nature of teen thinking, their emotions, level of engagement, and the power of relationships between teens and adults in the context of data literacy. This study conceives of co-design as a learning space for data literacy. It investigates the teen–adult dialogic interactions and what these interactions say about the nature of teen thinking, their emotions, level of engagement and the power relationships between teens and adults. Design/methodology/approach The study conceives of co-design as a learning space for teens. Linguistic Inquiry and Word Count (LIWC-22), a natural language processing (NLP) software tool, was used to examine the linguistic measures of Analytic Thinking, Clout, Authenticity, and Emotional Tone using transcriptions of recorded Data Labs with teens and adults. Linguistic Inquiry and Word Count (LIWC-22), a natural language processing (NLP) software tool, was used to examine the linguistic measures of Analytic Thinking, Clout, Authenticity and Emotional Tone using transcriptions of recorded Data Labs with teens and adults. Findings LIWC-22 scores on the linguistic measures Analytic Thinking, Clout, Authenticity and Emotional Tone indicate that teens had a high level of friendly engagement, a relatively low sense of power compared with the adult co-designers, medium levels of spontaneity and honesty and the prevalence of positive emotions during the co-design sessions. Practical implications This study provides a concrete example of how to apply NLP in the context of data literacy in the public library, mapping the LIWC-22 findings to STEM-focused informal learning. It adds to the understanding of assessment/measurement tools and methods for designing data literacy education, stimulating further research and discussion on the ways to empower youth to engage more actively in informal learning about data. Originality/value This study applies a novel approach for exploring teen engagement within a co-design project tasked with the creation of youth-oriented data literacy activities.
{"title":"Teen-adult interactions during the co-design of data literacy activities for the public library: insights from a natural language processing analysis of linguistic patterns","authors":"Leanne Bowler, Irene Lopatovska, Mark S. Rosin","doi":"10.1108/ils-06-2023-0076","DOIUrl":"https://doi.org/10.1108/ils-06-2023-0076","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study is to explore teen-adult dialogic interactions during the co-design of data literacy activities in order to determine the nature of teen thinking, their emotions, level of engagement, and the power of relationships between teens and adults in the context of data literacy. This study conceives of co-design as a learning space for data literacy. It investigates the teen–adult dialogic interactions and what these interactions say about the nature of teen thinking, their emotions, level of engagement and the power relationships between teens and adults.\u0000\u0000\u0000Design/methodology/approach\u0000The study conceives of co-design as a learning space for teens. Linguistic Inquiry and Word Count (LIWC-22), a natural language processing (NLP) software tool, was used to examine the linguistic measures of Analytic Thinking, Clout, Authenticity, and Emotional Tone using transcriptions of recorded Data Labs with teens and adults. Linguistic Inquiry and Word Count (LIWC-22), a natural language processing (NLP) software tool, was used to examine the linguistic measures of Analytic Thinking, Clout, Authenticity and Emotional Tone using transcriptions of recorded Data Labs with teens and adults.\u0000\u0000\u0000Findings\u0000LIWC-22 scores on the linguistic measures Analytic Thinking, Clout, Authenticity and Emotional Tone indicate that teens had a high level of friendly engagement, a relatively low sense of power compared with the adult co-designers, medium levels of spontaneity and honesty and the prevalence of positive emotions during the co-design sessions.\u0000\u0000\u0000Practical implications\u0000This study provides a concrete example of how to apply NLP in the context of data literacy in the public library, mapping the LIWC-22 findings to STEM-focused informal learning. It adds to the understanding of assessment/measurement tools and methods for designing data literacy education, stimulating further research and discussion on the ways to empower youth to engage more actively in informal learning about data.\u0000\u0000\u0000Originality/value\u0000This study applies a novel approach for exploring teen engagement within a co-design project tasked with the creation of youth-oriented data literacy activities.\u0000","PeriodicalId":44588,"journal":{"name":"Information and Learning Sciences","volume":"53 24","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138593462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.1108/ils-06-2023-0069
Mengxi Zhou, Selena Steinberg, Christina Stiso, Joshua Danish, Kalani Craig
Purpose This study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data. Design/methodology/approach The researchers designed six locally relevant network visualization activities to support students’ data reasoning practices toward understanding aggregate patterns in data. Cultural historical activity theory (Engeström, 1999) guides the analysis to identify how network visualization activities mediate students’ emerging understanding of aggregate data sets. Findings Pre/posttest findings indicate that this implementation positively impacted students’ understanding of network visualization concepts, as they were able to identify and interpret key relationships from novel networks. Interaction analysis (Jordan and Henderson, 1995) of video data revealed nuances of how activities mediated students’ improved ability to interpret network data. Some challenges noted in other studies, such as students’ tendency to focus on familiar concepts, are also noted as teachers supported conversations to help students move beyond them. Originality/value To the best of the authors’ knowledge, this is the first study the authors are aware of that supported elementary students in exploring data literacy through network visualization. The authors discuss how network visualizations and locally/personally meaningful data provide opportunities for learning data literacy concepts across the curriculum.
本研究旨在探讨网络可视化如何为学习者提供利用本地和个人相关数据探索数据素养概念的机会。设计/方法/方法研究人员设计了六个与本地相关的网络可视化活动，以支持学生的数据推理实践，以理解数据中的聚合模式。文化历史活动理论(Engeström, 1999)指导了分析，以确定网络可视化活动如何介导学生对汇总数据集的新兴理解。研究结果表明，这种实施对学生对网络可视化概念的理解产生了积极的影响，因为他们能够识别和解释新网络中的关键关系。对视频数据的交互分析(Jordan and Henderson, 1995)揭示了活动如何促进学生解释网络数据能力提高的细微差别。在其他研究中指出的一些挑战，如学生倾向于关注熟悉的概念，也被指出为教师支持的对话，以帮助学生超越他们。原创性/价值据作者所知，这是作者所知的第一个支持小学生通过网络可视化探索数据素养的研究。作者讨论了网络可视化和本地/个人有意义的数据如何为跨课程学习数据素养概念提供机会。
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Pub Date : 2023-11-30DOI: 10.1108/ils-06-2023-0064
Ina Sander
Purpose In light of a need for more critical education about datafication, this paper aims to develop a framework for critical datafication literacy that is grounded in theoretical and empirical research. The framework draws upon existing critical data literacies, an in-depth analysis of three well-established educational approaches – media literacy, the German “(politische) Bildung” and Freirean “critical pedagogy” – and empirical analyses of online educational resources about datafication. Design/methodology/approach The study interconnects theoretical analyses with an empirical mixed methods investigation that includes expert interviews with creators of online educational resources about datafication and a qualitative survey with educators interested in teaching about data technologies. Findings The research identified novel findings on the goals of resource creators and educators, such as a focus on empowering and emancipatory approaches, fostering systemic understanding of datafication and encouraging collective action. Such perspectives are rare in existing critical data literacy conceptualisations but show resemblance to traditional education scholarship. This highlights how much can be learnt from practitioners and from these more established educational approaches. Based on these findings, a framework for critical datafication literacy is suggested that aims for systemic understanding of datafication, encouraging critical thinking and enabling learners to make enlightened choices and take different forms of action. Originality/value The study is unique in its interconnection of theoretical and empirical research, and it advances previous research by suggesting a grounded framework for critical datafication literacy.
{"title":"Critical datafication literacy – a framework for educating about datafication","authors":"Ina Sander","doi":"10.1108/ils-06-2023-0064","DOIUrl":"https://doi.org/10.1108/ils-06-2023-0064","url":null,"abstract":"Purpose In light of a need for more critical education about datafication, this paper aims to develop a framework for critical datafication literacy that is grounded in theoretical and empirical research. The framework draws upon existing critical data literacies, an in-depth analysis of three well-established educational approaches – media literacy, the German “(politische) Bildung” and Freirean “critical pedagogy” – and empirical analyses of online educational resources about datafication. Design/methodology/approach The study interconnects theoretical analyses with an empirical mixed methods investigation that includes expert interviews with creators of online educational resources about datafication and a qualitative survey with educators interested in teaching about data technologies. Findings The research identified novel findings on the goals of resource creators and educators, such as a focus on empowering and emancipatory approaches, fostering systemic understanding of datafication and encouraging collective action. Such perspectives are rare in existing critical data literacy conceptualisations but show resemblance to traditional education scholarship. This highlights how much can be learnt from practitioners and from these more established educational approaches. Based on these findings, a framework for critical datafication literacy is suggested that aims for systemic understanding of datafication, encouraging critical thinking and enabling learners to make enlightened choices and take different forms of action. Originality/value The study is unique in its interconnection of theoretical and empirical research, and it advances previous research by suggesting a grounded framework for critical datafication literacy.","PeriodicalId":44588,"journal":{"name":"Information and Learning Sciences","volume":"507 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139205670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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