ethanporcaro/eric-fulltext-meta huggingface dataset:
Analyzing Social Media and Learning through Content and Social Network Analysis: A Faceted Methodological Approach
Author: Gruzd, Anatoliy
Date: 2016-00-00
Type: Journal Articles
Subjects: Social Media,Teaching Methods,Network Analysis,Workshops,Data Analysis,Data Collection,Educational Research,Evaluation Methods,Telecommunications,Handheld Devices,Large Group Instruction,Online Courses,Educational Technology,Technology Uses in Education

In just a short period, social media have altered many aspects of our daily lives, from how we form and maintain social relationships to how we discover, access, and share information online. Now social media are also affecting how we teach and learn. In this paper, we discuss methods that can help researchers and educators evaluate and understand the observed and potential use of social media for teaching and learning through content and network analyses of social media texts and networks. This paper is based on a workshop given at the 2014 Learning Analytics and Knowledge conference, and presents an overview of the measures and potential of a multi-method approach for studying learning via social media. The theoretical discussion is augmented with study of the case of Twitter discussion from a cMOOC class.
Understanding Learning and Learning Design in MOOCs: A Measurement-Based Interpretation
Author: Milligan, Sandra
Date: 2016-00-00
Type: Journal Articles
Subjects: Online Courses,Large Group Instruction,Thinking Skills,Learning Processes,Electronic Publishing,Collaborative Writing,Teaching Methods,Educational Technology,Technology Uses in Education,Foreign Countries,Peer Evaluation,Self Evaluation (Individuals),Learner Engagement,Student Satisfaction

The paper describes empirical investigations of how participants in a MOOC learn, and the implications for MOOC design. A learner capability to generate higher order learning in MOOCs--called crowd-sourced learning (C-SL) capability--was defined from learning science literature. The capability comprised a complex yet interrelated array of attitudes, beliefs, and understandings about learning that participants bring to a MOOC and which shape their behaviour and explain why individuals differ in their ability to generate higher order learning. The capability was formulated as a developmental progression describing behaviours associated with five levels, from novice to expert, charting the degree to which learners regulate their own learning, effectively exploit the scale and diversity of MOOCs, and harness opportunities for distributed teaching. Item response theory was applied to log stream data in two MOOCs to construct empirically validated measures of this capability, enabling each MOOC learner to be assessed for learning capability on a scale from novice to expert. The majority of participants did not behave in ways conducive to the generation of higher order learning, but the C-SL progression suggested principles to guide MOOC design to make them more efficacious, which, when empirically investigated, were found to be efficacious.
The Development of Multiple Intelligence Capabilities for Early Childhood Development Center, Local Administration Organization in Chaiyaphum Province
Author: Siphai, Sunan
Date: 2017-01-23
Type: Journal Articles
Subjects: Multiple Intelligences,Early Childhood Education,Young Children,Interviews,Student Characteristics,Student Behavior,Learning Activities,Behavior Modification,Foreign Countries,Class Activities,Pretests Posttests,Preschool Teachers,Skill Development,Statistical Analysis

The aim of this research is to promote multiple intelligence capabilities for Early Childhood Care Center of a Sub-district Administration Organization in Chaiyaphum Province. The sample applied were 61 children aging between 3 and 5 years old at Child Development Center, Tambon Ban Kok, Amphoe Chaturus, Chaiyaphum Province, who were selected through the purposive sampling method. Caretakers were interviewed and the instruments were integrated planning of activities to promote abilities and the behavioral notification form for multiple intelligence capabilities. The study indicated that there were high behavioral changes of multiple intelligence capabilities of the children during all planned activities. Furthermore, comparison of multiple intelligence capabilities of child behavior before and after the activities, such as linguistic, logic, mathematics, spatial, physical and body-movement, music and rhythm skills, ability of human relationship, self-understanding, love of natural environment and higher level of existence were applied. Its result was statistical significant at 0.01.
Developing a Code of Practice for Learning Analytics
Author: Sclater, Niall
Date: 2016-00-00
Type: Journal Articles
Subjects: Data Analysis,Information Policy,Ethics,Standard Setting,Legal Problems,Privacy,Advisory Committees,Taxonomy,Program Design,Program Development,Program Validation,Design Requirements,Models,Student Records,Progress Monitoring,Foreign Countries

Ethical and legal objections to learning analytics are barriers to development of the field, thus potentially denying students the benefits of predictive analytics and adaptive learning. Jisc, a charitable organization that champions the use of digital technologies in UK education and research, has attempted to address this with the development of a Code of Practice for Learning Analytics. The Code covers the main issues institutions need to address in order to progress ethically and legally. This paper outlines the extensive research and consultation activities carried out to produce a document that covers the concerns of institutions and, critically, the students they serve. The resulting model for developing a code of practice includes a literature review, setting up appropriate governance structures, developing a taxonomy of the issues, drafting the code, consulting widely with stakeholders, as well as publication, dissemination, and embedding it in institutions.
Practical Measurement and Productive Persistence: Strategies for Using Digital Learning System Data to Drive Improvement
Author: Krumm, Andrew E.
Date: 2016-00-00
Type: Journal Articles
Subjects: Measurement,Persistence,Electronic Learning,Data Analysis,Data Collection,Improvement,Student Behavior,Two Year College Students,Community Colleges,Remedial Mathematics,Measures (Individuals),Predictive Validity

This paper outlines the development of practical measures of productive persistence using digital learning system data. Practical measurement refers to data collection and analysis approaches originating from improvement science; productive persistence refers to the combination of academic and social mindsets as well as learning behaviours that are important drivers of student success within the Carnegie Foundation for the Advancement of Teaching's Community College Pathways (CCP) Network Improvement Community (NIC). Strategies for operationalizing non-cognitive factors using digital learning system data as well as approaches for utilizing them during improvement efforts are described.
Analytics for Knowledge Creation: Towards Epistemic Agency and Design-Mode Thinking
Author: Chen, Bodong
Date: 2016-00-00
Type: Journal Articles
Subjects: Epistemology,Educational Research,Data Collection,Data Analysis,Educational Innovation,Educational Objectives,Competence

Innovation and knowledge creation call for high-level epistemic agency and design-mode thinking, two competencies beyond the traditional scopes of schooling. In this paper, we discuss the need for learning analytics to support these two competencies, and more broadly, the demand for education for innovation. We ground these arguments on a distinctive Knowledge Building pedagogy that treats education as a knowledge-creation enterprise. By critiquing current learning analytics for their focus on static-state knowledge and skills, we argue for &quot;agency-driven, choice-based&quot; analytics more attuned to higher order competencies in innovation. We further describe ongoing learning analytics initiatives that attend to these elements of design. Prospects and challenges are discussed, as well as broader issues regarding analytics for higher order competencies.
Microgenetic Learning Analytics Methods: Workshop Report
Author: Aghababyan, Ani
Date: 2016-00-00
Type: Journal Articles
Subjects: Educational Research,Data Collection,Data Analysis,Workshops,Student Records,Elementary School Students,Student Behavior,Student Attitudes,Affective Behavior,Educational Technology,Technology Uses in Education,Programming,Observation,Coding

Learning analytics is an emerging discipline and, as such, benefits from new tools and methodological approaches. This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the second annual Learning Analytics Summer Institute in Cambridge, Massachusetts, on 30 June 2014. Specifically, this paper introduces educational researchers to our experience using data analysis techniques with the RStudio development environment to analyze temporal records of 52 elementary students' affective and behavioural responses to a digital learning environment. In the RStudio development environment, we used methods such as hierarchical clustering and sequential pattern mining. We also used RStudio to create effective data visualizations of our complex data. The scope of the workshop, and this paper, assumes little prior knowledge of the R programming language, and thus, covers everything from data import and cleanup to advanced microgenetic analysis techniques. Additionally, readers will be introduced to software setup, R data types, and visualizations. This paper not only adds to the toolbox for learning analytics researchers (particularly when analyzing time series data), but also shares our experience interpreting a unique and complex dataset.
Teachers as Producers of Data Analytics: A Case Study of a Teacher-Focused Educational Data Science Program
Author: McCoy, Chase
Date: 2016-00-00
Type: Journal Articles
Subjects: Case Studies,Semi Structured Interviews,Educational Research,Data Collection,Data Analysis,Educational Technology,Integrated Learning Systems,Technology Uses in Education,College Faculty,Higher Education,Educational Assessment,Statistics

Educational data science (EDS) is an emerging, interdisciplinary research domain that seeks to improve educational assessment, teaching, and student learning through data analytics. Teachers have been portrayed in the EDS literature as users of pre-constructed data dashboards in educational technologies, with little consideration given to them as active producers of data analytics. This article presents the case study results of an EDS program at a large university in Midwestern U.S.A. in which faculty and instructors were provided with access to institutional data and data analytics technologies in order to explore questions related to their classroom and departmental environments. Semi-structured interviews of program participants were conducted to examine the participants' experiences as practitioner researchers in EDS. The analysis showed that participants were motivated to participate to improve their learning and educational environments through data analytics, as opposed to developing a research agenda in EDS; that participants experienced a range of barriers related to data literacy; and that participant community support in addition to administrative support are vital to teacher-focused EDS programs. This study adds to a small but growing body of research in EDS that considers teachers as producers and not just consumers of data analytics.
Application of Case-Task Based Approach in Business English Teaching--A Case Study of the Marketing Course in SEIB of GDUFS
Author: Guiyu, Dai
Date: 2017-00-00
Type: Journal Articles
Subjects: Business Communication,Business English,English (Second Language),Second Language Learning,Second Language Instruction,Problem Solving,Linguistic Competence,Marketing,Diachronic Linguistics,Business Administration Education,Case Method (Teaching Technique),International Trade,Foreign Countries,Teaching Methods,Course Evaluation,Case Studies,Task Analysis

Business English Teaching aims at cultivating students' ability to analyze and solve problems, improving students' comprehensive language competence and honing their business practical skills. Adhering to the principle of learning by doing and learning by teaching others, Case-Task Based Approach emphasizes students' ability of language use in authentic situation as well as their competence of taking part in social practices, which, to a large extent, corresponds to the objectives of Business English Teaching. Based on a diachronic combing of research ideas of Case-Task Based Approach, this writing analyzes the marketing course offered in School of English for International Business of Guangdong University of Foreign Studies through Case-Task Based Approach, expounds the implementation process of this approach and investigates into its strengths and weaknesses. Finally this writing will have a tentative exploration on how to improve the approach in practice so as to enhance the understanding of its application in Business English Teaching.
LEA in Private: A Privacy and Data Protection Framework for a Learning Analytics Toolbox
Author: Steiner, Christina M.
Date: 2016-00-00
Type: Journal Articles
Subjects: Privacy,Guidelines,Ethics,Information Security,Correlation,Data Analysis,Laws,Research and Development,Program Descriptions,Informed Consent,Foreign Countries,Civil Rights,Information Management,Classification,Ownership,Trust (Psychology),Best Practices

To find a balance between learning analytics research and individual privacy, learning analytics initiatives need to appropriately address ethical, privacy, and data protection issues. A range of general guidelines, model codes, and principles for handling ethical issues and for appropriate data and privacy protection are available, which may serve the consideration of these topics in a learning analytics context. The importance and significance of data security and protection are also reflected in national and international laws and directives, where data protection is usually considered as a fundamental right. Existing guidelines, approaches, and regulations served as a basis for elaborating a comprehensive privacy and data protection framework for the LEA's BOX project. It comprises a set of eight principles to derive implications for ensuring ethical treatment of personal data in a learning analytics platform and its services. The privacy and data protection policy set out in the framework is translated into the learning analytics technologies and tools that were developed in the project and may be used as best practice for other learning analytics projects.
A Tutorial on Epistemic Network Analysis: Analyzing the Structure of Connections in Cognitive, Social, and Interaction Data
Author: Shaffer, David Williamson
Date: 2016-00-00
Type: Journal Articles
Subjects: Epistemology,Network Analysis,Data Analysis,Coding,Models,Evaluation Methods,Robustness (Statistics),Visualization,Cognitive Processes,Social Networks,Metadata,Matrices,Correlation

This paper provides a tutorial on epistemic network analysis (ENA), a novel method for identifying and quantifying connections among elements in coded data and representing them in dynamic network models. Such models illustrate the structure of connections and measure the strength of association among elements in a network, and they quantify changes in the composition and strength of connections over time. Importantly, ENA enables comparison of networks both directly and via summary statistics, so the method can be used to explore a wide range of qualitative and quantitative research questions in situations where patterns of association in data are hypothesized to be meaningful. While ENA was originally developed to model cognitive networks--the patterns of association between knowledge, skills, values, habits of mind, and other elements that characterize complex thinking--ENA is a robust method that can be used to model patterns of association in any system characterized by a complex network of dynamic relationships among a relatively small, fixed set of elements.
Student Attraction, Persistence and Retention in STEM Programs: Successes and Continuing Challenges
Author: Sithole, Alec
Date: 2017-00-00
Type: Journal Articles
Subjects: STEM Education,Academic Persistence,Student Interests,Success,Teaching Methods,Orientation,Progress Monitoring,Mathematics Instruction,Communities of Practice,Faculty Development,Outreach Programs,Barriers,Academic Advising,Course Selection (Students),Mathematics Skills,Study Habits,Peer Teaching,Mentors,Time Management,Secondary Education,Higher Education

Low student enrollment and high attrition rates in Science, Technology, Engineering, and Mathematics (STEM) education are major challenges in higher education. Many STEM entrants end-up switching their majors to non-STEM fields, perform poorly relative to their peers in other programs, and/or drop out of college without earning any academic qualification. Therefore, it is important to examine strategies for reducing attrition in STEM programs. This paper reviews the major factors impeding student interest, success, and persistence in STEM programs, and current institutional practices aimed at addressing these issues. Suggested institutional strategies to improve persistence in STEM programs and their implications that are discussed in this paper include: provision of orientation programs, adoption of early warning systems, Mathematics review sessions, creation of student learning communities, professional development of faculty, as well as collaborative and outreach programs. It is hoped that this review will encourage debate toward solving the major challenges facing STEM education.
Learning Analytics in Small-Scale Teacher-Led Innovations: Ethical and Data Privacy Issues
Author: Rodríguez-Triana, María Jesús
Date: 2016-00-00
Type: Journal Articles
Subjects: Ethics,Privacy,Instructional Innovation,Higher Education,Guidelines,Teaching Methods,Elementary School Students,Educational Environment,Data Collection,Information Security,Formative Evaluation,Data Analysis,Preschool Teachers,Teacher Education,Teacher Responsibility,Vignettes,Foreign Countries,Case Studies,Intervention

As a further step towards maturity, the field of learning analytics (LA) is working on the definition of frameworks that structure the legal and ethical issues that scholars and practitioners must take into account when planning and applying LA solutions to their learning contexts. However, current efforts in this direction tend to be focused on institutional higher education approaches. This paper reflects on the need to extend these ethical frameworks to cover other approaches to LA; more concretely, small-scale classroom-oriented approaches that aim to support teachers in their practice. This reflection is based on three studies where we applied our teacher-led learning analytics approach in higher education and primary school contexts. We describe the ethical issues that emerged in these learning scenarios, and discuss them according to three dimensions: the overall learning analytics approach, the particular solution to learning analytics adopted, and the educational contexts where the analytics are applied. We see this effort as a first step towards the wider objective of providing a more comprehensive and adapted ethical framework to learning analytics that is able to address the needs of different learning analytics approaches and educational contexts.
Towards Careful Practices for Automated Linguistic Analysis of Group Learning
Author: Howley, Iris
Date: 2016-00-00
Type: Journal Articles
Subjects: Automation,Computational Linguistics,Cooperative Learning,Discourse Analysis,Undergraduate Students

The multifaceted nature of collaborative learning environments necessitates theory to investigate the cognitive, motivational, and relational dimensions of collaboration. Several existing frameworks include aspects related to each of these three. This article explores the capability of multi-dimensional frameworks for analysis of collaborative processes to isolate and assess these separate dimensions of collaboration. Much successful work has contributed towards computational modelling for automated collaborative process analysis in the past decade. In this paper, we explore the extent to which evidence points to an intertwining between dimensions, raising important caveats for careful consideration when making assessments based on the observation of codes as they are applied to collaborative discourse. We conclude with a research agenda for future investigation to address this limitation.
Using Learning Analytics to Characterize Student Experimentation Strategies in Engineering Design
Author: Vieira, Camilo
Date: 2016-00-00
Type: Journal Articles
Subjects: Engineering,Design,Experiments,Student Behavior,Models,Identification,Middle School Students,Interaction,Computer Assisted Design,Comparative Analysis,Data Analysis,Pretests Posttests,Correlation,Learning Processes,Technology Uses in Education

Engineering design is a complex process both for students to participate in and for instructors to assess. Informed designers use the key strategy of conducting experiments as they test ideas to inform next steps. Conversely, beginning designers experiment less, often with confounding variables. These behaviours are not easy to assess in educational settings because they occur throughout the design process. This paper reports on a two-fold study carried out to test the model for identifying student behaviours during design experimentation. The first phase uses the process data from 48 middle-school students designing an energy-plus house. The study utilized learner interaction data sets collected through automatic, unobtrusive logging of student actions in a CAD platform. The analysis of learner process data is compared to student performance on an open-ended post-test. The second phase correlates the number of experiments students conducted to the quality of student prototypes. The results suggest that the proposed model can be used to identify, characterize, and assess student strategies associated with conducting experiments. Implications of this work are relevant to engineering and design educators as well as researchers interested in the role of learning analytics in studying complex processes.
Tracking and Visualizing Student Effort: Evolution of a Practical Analytics Tool for Staff and Student Engagement
Author: Nagy, Robin
Date: 2016-00-00
Type: Journal Articles
Subjects: Learner Engagement,Student Evaluation,Secondary School Students,Visual Aids,Data Analysis,Case Studies,Foreign Countries,Student Behavior,Persistence,Scoring Rubrics,Computer Software,Computer Uses in Education,Student Attitudes,Self Evaluation (Individuals)

There is an urgent need for our educational system to shift assessment regimes from a narrow, high-stakes focus on grades, to more holistic definitions that value the qualities that lifelong learners will need. The challenge for learning analytics in this context is to deliver actionable assessments of these hard-to-quantify qualities, valued by both educators and learners. This practitioner report contributes to this by documenting the iterative refinement of a practical approach for tracking student effort, deployed in successive versions over six years in secondary schools. This demonstrates how teachers can assess a student quality such as &quot;effort&quot; in a practical way, and the insights that visual analytics can provide as a basis for productive dialogue among staff and students. The engagement and professional development of teachers is critical to embedding and sustaining novel analytics of this sort.