Shan Li

Department of Educational and Counselling Psychology, McGill University, Montreal, QC, Canada // shan.li2@mail.mcgill.ca 

Juan Zheng

Department of Educational and Counselling Psychology, McGill University, Montreal, QC, Canada // juan.zheng@mail.mcgill.ca 

Susanne P. Lajoie

Department of Educational and Counselling Psychology, McGill University, Montreal, QC, Canada // susanne.lajoie@mcgill.ca 

ABSTRACT: 

Examining the sequential patterns of self-regulated learning (SRL) behaviors is gaining popularity to understand students’ performance differences. However, few studies have looked at the transition probabilities among different SRL behaviors. Moreover, there is a lack of research investigating the temporal structures of students’ SRL behaviors (e.g., repetitiveness and predictability) and how they related to students’ performance. In this study, 75 students from a top North American university were tasked to diagnose a virtual patient in an intelligent tutoring system. We used recurrence quantification analysis and sequential analysis to analyze the temporal structures and sequential patterns of students’ SRL behaviors. We compared the differences between low and high performers. We found that low performers had more single, isolated recurrent behaviors in problem-solving, whereas the recurrent behaviors of high performers were more likely to be part of a behavioral sequence. High performers also demonstrated a higher transition probability across the three phases of SRL than low performers. In addition, high performers were unique in that their behavioral state transitions were cyclically sustained. This study provided researchers with theoretical insights regarding the cyclical nature of SRL. This study has also methodological contributions to the analysis of the temporal structures of SRL behaviors.

Keywords: 

Self-regulated learning, SRL behavior, Recurrence quantification analysis, Temporal structure, Intelligent tutoring system

Min-Hwi Seo

Department of Educational Technology, Ewha Womans University, Seoul, South Korea // minhwiseo@ewha.ac.kr 

Hyo-Jeong So

Department of Educational Technology, Ewha Womans University, Seoul, South Korea // hyojeongso@ewha.ac.kr

ABSTRACT: 

The purpose of this research was to design and evaluate the efficacy of a gesture-based exhibit with augmented reality (AR) for understanding complex scientific concepts. In particular, this study focuses on the effect of differently guided conditions in a gesture-based AR. We first present the design and development of a gesture-based AR exhibit about the conductor resistance phenomenon. An experiment was conducted to examine the effect of guided and unguided experiences on complex conceptual learning. In the experiment, 40 participants between 15 and 17 years-old were randomly assigned to either the guided (visual and docent explanation) or unguided condition. Their understanding of complex concepts was measured through the pre-test and post-test. The results indicate that while the participants increased cognitive understanding after experiencing the gesture-based AR exhibit, there was no significant difference between the two conditions. This may imply that the provision of extra guidance does not necessarily lead to better conceptual learning. In conclusion, this study provides some implications concerning the design of new types of immersive exhibits in museum contexts.

Keywords: 

Augmented reality, Informal learning, Science museum, Conceptual learning

Hui-Tzu Hsu

Language Centre, National Chin-Yi University of Technology, Taichung, Taiwan (R.O.C.) // lindahsu85@gmail.com

Chih-Cheng Lin

Department of English, National Taiwan Normal University, Taiwan (R.O.C.) // cclin@ntnu.edu.tw 

ABSTRACT: 

Mobile technology is regarded as a helpful tool facilitating language learning. However, the success of mobile technology largely depends on learners’ acceptance. This study explored the factors that may affect students’ intention formation regarding mobile-assisted language learning (MALL) in the context of higher education through the lens of action control theory. The study adopted mixed methods: an online survey of 557 students and individual interviews with 70 students. The findings indicated factors in each of the three dimensions (preoccupation, hesitation, and volatility) of action control theory that positively or negatively influenced the students’ intention to use mobile technology for language learning. According to the findings, these influential factors may be related experiences in the preoccupation dimension, design and feature interference of MALL applications and teachers’ teaching style influence in the hesitation dimension, and overall appraisal and performance impact and other novelty interference in the volatility dimension. Students’ success in initiating and completing a MALL task depends on mainly depends on their acceptance of MALL, and this acceptance is affected by these factors in a positive or negative direction. The strengthening of the positive influence and the weakening of the negative influence caused by these factors should be paid attention to in the process of performing and engaging in a MALL task. Students’ concerns regarding the use of mobile technology in language education are addressed with suggestions for future research and practice in light of the findings.

Keywords: 

Mobile technology, Language learning, Learning intention, Action control, Higher education

Sandy C. Li

Hong Kong Baptist University, HKSAR, PRC // sandyli@hkbu.edu.hk

Karen B. Petersen

Aarhus University, Denmark // kp@edu.au.dk

ABSTRACT:

While infusion of technology into schools has been one of the top priorities of the education reform agenda across the world, findings from many large-scale international assessments indicate that students’ use of information and communication technology (ICT) has mixed effects on their academic achievements. In this paper, we argue that these ambivalent findings were due to the oversight of the indirect effects of ICT use mediated by other ICT-related variables. We employed multilevel structural equation modelling to unfold the relationship between students’ ICT use and their academic achievements based on PISA 2015 data. The results indicated that students’ autonomy in ICT use and students’ interest in ICT use were found to have significant positive direct effects on students’ academic achievements at both within-school and between-school levels. These two variables played a significant role in mediating the indirect effects of ICT use outside school for schoolwork and ICT resources on students’ academic achievements. On the contrary, ICT resources and ICT use at school exerted either no direct effect or a negative direct effect on students’ academic achievements and students’ perceived autonomy related to ICT use, suggesting that mere provision and use of ICT resources in school did not necessarily guarantee success in student performance. At the school level, school’s transformational leadership and collaborative climate helped promote students’ autonomy in ICT use. 

Keywords: 

ICT use, Academic achievement, Multilevel analysis, Structural equation modelling, PISA 2015

Alejandro Romero-Hernandez

Complutense University of Madrid, Spain // alerom02@ucm.es

Manuel Gonzalez-Riojo

Complutense University of Madrid, Spain // manuel.gonzalez@ucm.es

Meriem El Yamri

Complutense University of Madrid, Spain // melyamri@ucm.es 

Borja Manero

Complutense University of Madrid, Spain // bmanero@ucm.es

ABSTRACT: 

The performing arts are currently in a critical situation worldwide. Various reports warn that the lack of audience. If we focus on dance, and especially folk dances, the situation is worse. In various countries and continents, folk dances are slowly disappearing. In Spain, we find evidence of the downward trend in terms of the number of attendees to performances of Spanish dance -an art form that is highly valued throughout the world. In a generation marked by technological advancements, the only way for classic performing arts to reach young audiences - or digital natives – is to speak the same language they use with new technologies. This paper presents a study in collaboration with the Spanish National Dance Company, carried out with 877 students (aged from 9 to 12) from 12 different schools in the community of Madrid, Spain. We designed a two-phase experiment. In the first phase, we separated the students into 3 groups: students who played a videogame called “Dancing a Treasure,” those who received a workshop from professional dancers, and a control group. In the second phase that took place two weeks later, the participants attended to a real show of Spanish dance, and we studied how the previous educational approaches affected to the students increase of interest after the show. The experiment demonstrated that the videogame was, at least, as effective in increment interest about dance in younger generations as a workshop taught by expert dance professionals. Thus, in terms of scalability, the videogame is a better option because it can be applied with the same results to larger groups with no additional cost.

Keywords: 

Interest, Video games, Spanish dance, M-Learning, Serious games

Simon Knight

University of Technology Sydney, TD School, Centre for Research on Education in a Digital Society, UTS Broadway St, Broadway, NSW 2007, Australia // simon.knight@uts.edu.au 

Camillia Matuk

New York University, Steinhardt School of Culture, Education and Human Development, Educational Communication and Technology, 370 Jay St., Brooklyn, NY 11201, United States // cmatuk@nyu.edu

Kayla DesPortes

New York University, Steinhardt School of Culture, Education and Human Development, Educational Communication and Technology, 370 Jay St., Brooklyn, NY 11201, United States // kayla.desportes@nyu.edu 

ABSTRACT: 

With growing awareness of, and attention to, the potential of data to inform decisions across contexts, has come an increasing recognition and need to develop data literacy strategies that support people to learn to be critical of data, given this consequential nature of data use (and abuse). To achieve a just society, inequities in both capacity for data literacy, and the applications of data in society, must be addressed. A key aim is to create learning experiences that engage learners with issues of power and inequity, including those typically marginalized by data literacy education. In this way, data literacy and social justice learning goals are intertwined, and mutually supportive, in developing data literacy in learning about, through, and for social justice. This special issue assembles five empirical studies on learning at the intersection of data literacy and social justice, and that illustrate various approaches to intertwining data science and social justice learning goals. They moreover highlight the importance of the learning sciences as a perspective for understanding how people learn in specific contexts of data justice. This essay reflects on themes raised by these contributions, and offers a framework for conceptualizing the intersections between the learning of data literacy and justice. In particular, we draw on existing distinctions between “reading” and “writing the world,” and propose a mapping of data literacy justice activities from data comprehension to participation, and from thin to thick justice.

Keywords: 

Data justice, Numeracy, Mathematics education, Civics education, Equity 

Jennifer B. Kahn

University of Miami, USA // jkahnthorne@miami.edu 

Lee Melvin Peralta

Michigan State University, USA // peralt11@msu.edu 

Laurie H. Rubel

University of Haifa, Israel // lrubel@edu.haifa.ac.il

Vivian Y. Lim

CUNY Guttman Community College, USA //  vivian.liu@guttman.cuny.edu

Shiyan Jiang

North Carolina State University, USA // sjiang24@ncsu.edu 

Beth Herbel-Eisenmann

Michigan State University, USA // bhe@msu.edu

ABSTRACT: 

In this paper, we introduce Notice, Wonder, Feel, Act, and Reimagine (NWFAR) to promote social justice in data science (DS) education. NWFAR draws on intersectional feminist DS to scaffold critical perspectives towards systems of power and oppression and attend to students’ experiences in designs for learning. NWFAR adds three practices that are typically not emphasized in learning designs for DS: feel—engaging emotions and the physical body; act—challenging, inspiring, or informing others towards change; and reimagine—envisioning how data, data methods, and data technologies could pursue different problems, solutions, and perspectives. We illustrate NWFAR through two design-based research projects from prior empirical work. Through these two examples, we demonstrate what thinking with NWFAR could look like in practice and highlight future possibilities for learning. We conclude with a discussion that focuses on the reimagining dimension, in which we highlight social-justice oriented theories. 

Keywords: 

Data science education, Data feminism, Critical data literacies, Social justice

Rahul Bhargava

Northeastern University, USA // r.bhargava@northeastern.edu 

Amanda Brea

Northeastern University, USA // brea.am@northeastern.edu

Victoria Palacin

Univerisity of Helsinki, Finland // victoria.palacin@helsinki.fi 

Laura Perovich

Northeastern University, USA // l.perovich@northeastern.edu 

Jesse Hinson

Northeastern University, USA // j.hinson@northeastern.edu

ABSTRACT: 

Data literacy is a growing area of focus across multiple disciplines in higher education. The dominant forms of introduction focus on computational toolchains and statistical ways of knowing. As data driven decision-making becomes more central to democratic processes, a larger group of learners must be engaged in order to ensure they have a seat at the table in civic settings. This requires a rethinking to support many paths into data literacy for a variety of learning styles. In this paper we introduce “data theatre,” a set of activities designed for data novices that may have limited experience or comfort with spreadsheets, math, and other quantitative operations. Through iterative co-design over three workshops, we tested and produced two activity guides for educators, building on long-standing practices in participatory theatre that center social justice and liberation. Our initial findings provide very early evidence that this approach can help these learners overcome hesitations to working with information, begin building a critical perspective when viewing data, and create emotionally impactful data stories told through theatrical performance. This prototype work suggests to us that the concept of “Data theatre” warrants further study to build a more robust understanding of its affordances and limitations. 

Keywords: 

Data literacy, Participatory theatre, Education, Social justice

Ben Rydal Shapiro

Georgia State University, USA // ben@benrydal.com

Amanda Meng

Georgia Institute of Technology, USA // am377@gatech.edu

Annabel Rothschild

Georgia Institute of Technology, USA // arothschild@gatech.edu

Sierra Gilliam

Georgia State University, USA // sgilliam7@student.gsu.edu

Cicely Garrett

Georgia Institute of Technology, USA // cicelycmgarrett@gmail.com

Carl DiSalvo

Georgia Institute of Technology, USA // cdisalvo@gatech.edu 

Betsy DiSalvo

Georgia Institute of Technology, USA // bdisalvo@cc.gatech.edu

ABSTRACT: 

Informed by critical data literacy efforts to promote social justice, this paper uses qualitative methods and data collected during two years of workplace ethnography to characterize the notion of critical novice data work. Specifically, we analyze everyday language used by novice data workers at DataWorks, an organization that trains and employs historically excluded populations to work with community data sets. We also characterize challenges faced by these workers in both cleaning and being critical of data during a project focused on police-community relations. Finally, we highlight novel approaches to visualizing data the workers developed during this project, derived from data cleaning and everyday experience. Findings and discussion highlight the generative power of everyday language and visualization for critical novice data work, as well as challenges and opportunities to foster critical data literacy with novice data workers in the workplace.

Keywords: 

Data science education, Critical data literacy, Social justice, Data visualization, Workplace ethnography

Golnaz Arastoopour Irgens

Clemson University, U.S.A // garasto@clemson.edu 

Ibrahim Adisa

Clemson University, U.S.A // iadisa@clemson.edu 

Cinamon Bailey 

Clemson University, U.S.A // cinamob@clemson.edu

Hazel Vega Quesada

Clemson University, U.S.A // hvegaqu@clemson.edu 

ABSTRACT: 

As big data algorithm usage becomes more ubiquitous, it will become critical for all young people, particularly those from historically marginalized populations, to have a deep understanding of data science that empowers them to enact change in their local communities and globally. In this study, we explore the concept of critical machine learning: integrating machine learning knowledge content with social, ethical, and political effects of algorithms. We modified an intergenerational participatory design approach known as cooperative inquiry to co-design a critical machine learning educational program with and for youth ages 9 - 13 in two after-school centers in the southern United States. Analyzing data from cognitive interviews, observations, and learner artifacts, we describe the roles of children and researchers as meta-design partners. Our findings suggest that cooperative inquiry and meta-design are suitable frameworks for designing critical machine learning educational environments that reflect children’s interests and values. This approach may increase youth engagement around the social, ethical, and political implications of large-scale machine learning algorithm deployment.

Keywords: 

Critical data science, Machine learning, Algorithmic bias, Participatory design research, Community youth program

Josephine Louie

Education Development Center, USA // jlouie@edc.org

Jennifer Stiles

Education Development Center, USA // jstiles@edc.org

Emily Fagan

Education Development Center, USA // efagan@edc.org 

Beth Chance

California Polytechnic State University, USA // bchance@calpoly.edu

Soma Roy

California Polytechnic State University, USA // soroy@calpoly.edu

ABSTRACT: 

To promote understanding of and interest in working with data among diverse student populations, we developed and studied a high school mathematics curriculum module that examines income inequality in the U.S. Designed as a multi-week set of applied data investigations, the module supports student analyses of income inequality using U.S. Census Bureau microdata and the online data analysis tool CODAP. Pre- and post-module data show that use of this module was associated with statistically significant growth in students’ understanding of fundamental data concepts and individual interests in statistics and data analysis, with small to moderate effect sizes. Student survey responses and interview data from students and teachers suggest that the topic of income inequality, features within CODAP, the use of person-level data, and opportunities to engage in multivariable thinking helped to support critical data literacy and its foundations among participating students. We describe our definitions of data literacy and critical data literacy and discuss curriculum strategies to develop them.

Keywords: 

K-12 education, Intercultural competence, 21 st century skills, Inquiry learning