ethanporcaro/eric-fulltext-meta huggingface dataset:
Consistent Commitment: Patterns of Engagement across Time in Massive Open Online Courses (MOOCs)
Author: Ferguson, Rebecca
Date: 2015-00-00
Type: Journal Articles
Subjects: Online Courses,Access to Education,Learner Engagement,Constructivism (Learning),Dropouts,Dropout Rate,Multivariate Analysis,Educational Research,Data Collection,Data Analysis,Electronic Learning

Massive open online courses (MOOCs) are being used across the world to provide millions of learners with access to education. Many who begin these courses complete them successfully, or to their own satisfaction, but the high numbers who do not finish remain a subject of concern. In 2013, a team from Stanford University analyzed engagement patterns on three MOOCs run on the Coursera platform. They found four distinct patterns of engagement that emerged from MOOCs based on videos and assessments. Subsequent studies on the FutureLearn platform, which is underpinned by social-constructivist pedagogy, indicate that patterns of engagement in these massive learning environments may be influenced by decisions about pedagogy and learning design. This paper reports on two of these studies of learner engagement with FutureLearn courses. Study One first tries, not wholly successfully, to replicate the findings of the Coursera study in a new context. It then uses the same methodological approach to identify patterns of learner engagement on the FutureLearn platform, and explores how these patterns may have been influenced by pedagogy and elements of learning design. Study Two investigates whether these patterns of engagement are stable on subsequent presentations of the same courses. Two patterns are found consistently in this and other work: samplers who visit briefly, and completers who fully engage with the course. The paper concludes by exploring the implications for both research and practice.
Analysis of Dynamic Resource Access Patterns in Online Courses
Author: Hecking, Tobias
Date: 2014-00-00
Type: Journal Articles
Subjects: Online Courses,Blended Learning,Educational Technology,Technology Uses in Education,Large Group Instruction,Masters Programs,Graduate Students,Learning Activities,Use Studies,Social Networks,Network Analysis,Data Collection,Data Analysis,Educational Resources,Foreign Countries

This paper presents an analysis of resource access patterns in two recently conducted online courses. One of these has been a master level university lecture taught as a blended learning course with a wide range of online learning activities and materials, including collaborative wikis, self-tests, and thematic videos. The other course has been offered in the form of a MOOC. As a specialty of this course, master level students from two different universities could participate as a regular university class and receive credits for successful completion. In both courses, online learning resources such as videos, scientific literature, and wikis played a central role. In this context, the motivation for our research was to investigate characteristic patterns of resource usage of the learners. In order to gain deeper insights into the usage of learning materials, we have adapted methods from social network analysis and applied them to dynamic bipartite student-resource networks built from event logs of the students' resource access. In particular, we describe the clustering of students and resources in such networks and propose a method to identify patterns of the cluster evolution over time.
LATUX: An Iterative Workflow for Designing, Validating, and Deploying Learning Analytics Visualizations
Author: Martinez-Maldonado, Roberto
Date: 2015-00-00
Type: Journal Articles
Subjects: Data Analysis,Learning,Design,Validity,Visual Aids,Computer Interfaces,Computer Uses in Education,Case Studies,Cooperative Learning,Higher Education

Designing, validating, and deploying learning analytics tools for instructors or students is a challenge that requires techniques and methods from different disciplines, such as software engineering, human-computer interaction, computer graphics, educational design, and psychology. Whilst each has established its own design methodologies, we now need frameworks that meet the specific demands of the cross-disciplinary space defined by learning analytics are needed. In particular, LAK [Learning Analytics and Knowledge] needs a systematic workflow for creating tools that truly underpin the learning experience. In this paper, we present a set of guiding principles and recommendations derived from the LATUX workflow. This is a five-stage workflow to design, validate, and deploy awareness interfaces in technology-enabled learning environment. LATUX is based on well-established design processes for creating, testing, and re-designing user interfaces. We extend existing approaches by integrating the pedagogical requirements needed to guide the design of learning analytics visualizations that can inform pedagogical decisions or intervention strategies. We illustrate LATUX in a case study of a classroom with collaborative activities. Finally, the paper proposes a research agenda to support designers and implementers of learning analytics interfaces.
Curricular Design Analysis: A Data-Driven Perspective
Author: Méndez, Gonzalo
Date: 2014-00-00
Type: Journal Articles
Subjects: Data Analysis,Data Collection,Educational Research,Curriculum Design,Curriculum Development,Case Studies,Undergraduate Students,Computer Science Education,Foreign Countries,Educational Indicators,Difficulty Level,Scaling,Grade Point Average,Academic Achievement,Curriculum Evaluation,Evaluation Methods,Dropouts,Academic Failure,Probability,Enrollment Trends

Learning analytics has been as used a tool to improve the learning process mainly at the micro-level (courses and activities). However, another of the key promises of learning analytics research is to create tools that could help educational institutions at the meso- and macro-level to gain better insight into the inner workings of their programs in order to tune or correct them. This work presents a set of simple techniques that, if applied to readily available historical academic data, could provide such insights. The techniques described are real course difficulty estimation, course impact on the overall academic performance of students, curriculum coherence, dropout paths, and load/performance graph. The usefulness of these techniques is validated through their application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum redesign.
Learning Analytics: From Big Data to Meaningful Data
Author: Merceron, Agathe
Date: 2015-00-00
Type: Journal Articles
Subjects: Educational Research,Data,Data Collection,Data Analysis,Online Courses,Natural Language Processing,Conferences (Gatherings),Man Machine Systems,Integrated Learning Systems

This article introduces the special issue from the 2015 Learning Analytics and Knowledge conference. We describe the current state of the field and identify some of the trends in recent research. As the field continues to expand, there seem to be at least three directions of vigorous growth: (1) the inclusion of multimodal data (gesture, eye-tracking, biosensors), (2) the diversification of learning environments (MOOCs, classrooms, hands-on learning), and (3) new types of research questions considering a broader set of learning-related constructs (e.g., moving away from the focus on student retention).
Peer Promotions as a Method to Identify Quality Content
Author: Gunnarsson, Bjorn Levi
Date: 2014-00-00
Type: Journal Articles
Subjects: Electronic Publishing,Web Sites,Diaries,Social Media,Educational Technology,Technology Uses in Education,Data Analysis,Data Collection,Cooperative Learning,Online Courses,Case Studies,College Students,Writing Assignments,Peer Evaluation,Peer Influence,Student Reaction,Grading

Promoting blog content is a social activity--a means of communicating one student's appreciation of another student's work. This article explores the feasibility of using student promotions of content, in a blogosphere, to identify quality content, and implications for students and instructors. We show that students actively and voluntarily promote content, identify quality material with considerable accuracy, and use promotion data to select what to read. We explore the benefits of knowing which students are good and poor predictors of quality content, and what instructors can do with this information in terms of feedback and guidance.
Learning Analytics for Online Discussions: Embedded and Extracted Approaches
Author: Wise, Alyssa Friend
Date: 2014-00-00
Type: Journal Articles
Subjects: Data Collection,Data Analysis,Asynchronous Communication,Computer Mediated Communication,Group Discussion,Intervention,Student Participation,Educational Technology,Technology Uses in Education,Mixed Methods Research,Journal Writing,Interviews,Behavior Change,College Students,Attitude Measures

This paper describes an application of learning analytics that builds on an existing research program investigating how students contribute and attend to the messages of others in asynchronous online discussions. We first overview the E-Listening research program and then explain how this work was translated into analytics that students and instructors could use to reflect on their discussion participation. Two kinds of analytics were designed: some &quot;embedded&quot; in the learning environment to provide students with real-time information on their activity in-progress; and some &quot;extracted&quot; from the learning environment and presented to students in a separate digital space for reflection. In addition, we describe the design of an intervention though which use of the analytics can be introduced as an integral course activity. Findings from an initial implementation of the application indicated that the learning analytics intervention supported changes in students' discussion participation. Five issues for future work on learning analytics in online discussions are presented. One, unintentional versus purposeful change; two, differing changes prompted by the same analytic; three, importance of theoretical buy-in and calculation transparency for perceived analytic value; four, affective components of students' reactions; and five, support for students in the process of enacting analytics-driven changes.
Modelling Learners' Cognitive, Affective, and Social Processes through Language and Discourse
Author: Dowell, Nia M. M.
Date: 2014-00-00
Type: Journal Articles
Subjects: Cognitive Processes,Affective Behavior,Computational Linguistics,Intelligent Tutoring Systems,Cooperative Learning,Collaborative Writing,Epistemology,Teaching Methods,Discourse Analysis,Computer Mediated Communication,Data Collection,Online Courses

An emerging trend toward computer-mediated collaborative learning environments promotes lively exchanges between learners in order to facilitate learning. Discourse can play an important role in enhancing epistemology, pedagogy, and assessments in these environments. In this paper, we highlight some of our recent work showing the advantages using theoretically grounded automated linguistics tools to identify pedagogically valuable discourse features that can be applied in collaborative learning, intelligent tutoring systems (ITS), computer-mediated collaborative learning (CMCL), and MOOC environments.
Analyzing Engineering Design through the Lens of Computation
Author: Worsley, Marcelo
Date: 2014-00-00
Type: Journal Articles
Subjects: Data Analysis,Data Collection,Engineering,Design,Computation,Educational Research,Computer Uses in Education,Man Machine Systems,Artificial Intelligence,Engineering Education,Programming,Coding,Experience,Hypothesis Testing,Object Manipulation,Expertise,Generalizability Theory,Multivariate Analysis

Learning analytics and educational data mining are introducing a number of new techniques and frameworks for studying learning. The scalability and complexity of these novel techniques has afforded new ways for enacting education research and has helped scholars gain new insights into human cognition and learning. Nonetheless, there remain some domains for which pure computational analysis is currently infeasible. One such area, which is particularly important today, is open-ended, hands-on, engineering design tasks. These open-ended tasks are becoming increasingly prevalent in both K-12 and post-secondary learning institutions, as educators are adopting this approach in order to teach students real-world science and engineering skills (e.g., the &quot;Maker Movement&quot;). This paper highlights findings from a combined human-computer analysis of students as they complete a short engineering design task. The study uncovers novel insights and serves to advance the field's understanding of engineering design patterns. More specifically, this paper uses machine learning on hand-coded video data to identify general patterns in engineering design and develop a fine-grained representation of how experience relates to engineering practices. Finally, the paper concludes with ideas on how the specific findings from this study can be used to improve engineering education and the nascent field of &quot;making&quot; and digital fabrication in education. We also discuss how human-computer collaborative analyses can grow the learning analytics community and make learning analytics more central to education research.
Goffman's Front Stage and Backstage Behaviours in Online Education
Author: Gilmore, Dawn
Date: 2014-00-00
Type: Journal Articles
Subjects: Online Courses,Student Participation,Mixed Methods Research,Student Experience,College Students,Engineering Education,Psychology,Student Behavior,Questionnaires,Transcripts (Written Records),Content Analysis,Semi Structured Interviews,Behavior Patterns,Behavior Theories

This research applies Goffman's Presentation of Self in Everyday Life to analyze online and offline student participation in two online subjects. Mixed-methods will be used to produce a fuller account of student experiences.
A Longitudinal Study on Learner Career Advancement in MOOCs
Author: Wang, Yuan
Date: 2014-00-00
Type: Journal Articles
Subjects: Career Development,Longitudinal Studies,Online Courses,Educational Research,Data Collection,Data Analysis,Electronic Learning

In this paper, we present progress towards a longitudinal study of the post-course career advancement of MOOC learners. We present initial results and analysis plans for how to link this to in-course behaviour, towards better understanding the goals of all MOOC learners.
Constructing Programming Tests from an Item Pool: Pushing the Limits of Student Knowledge Using Assessment and Learning Analytics
Author: Ivancevic, Vladimir
Date: 2014-00-00
Type: Journal Articles
Subjects: Item Banks,Educational Research,Data Collection,Data Analysis,Difficulty Level,Test Items,Programming,Integrated Learning Systems,Item Response Theory,Tests,Foreign Countries,College Students

Tests targeting the upper limits of student ability could aid students in their learning. This article gives an overview of an approach to the construction of such tests in programming, together with ideas on how to implement and refine them within a learning management system.
Leveraging MSLQ Data for Predicting Student Achievement Goal Orientations
Author: Ali, Liaqat
Date: 2014-00-00
Type: Journal Articles
Subjects: Goal Orientation,Academic Achievement,Learning Strategies,Questionnaires,Electronic Learning,Undergraduate Students,Correlation,Predictor Variables,Student Motivation

This study aims to investigate how the learning strategies and achievement goal orientations of students relate to their academic behaviours and performance in the context of an online learning system. The study also develops and validates a relational model between student learning strategies and achievement goals.
Learning Analytics for Networked Learning Models
Author: Joksimovic, Srecko
Date: 2014-00-00
Type: Journal Articles
Subjects: Data Collection,Data Analysis,Educational Research,Interaction,Models,Networks,Evaluation Methods,Research Methodology,Social Media,Online Courses,Educational Technology,Technology Uses in Education

Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to analyze their effects. Therefore, the main goal of this research is the development of a theoretical model of networked learning that allows for a comprehensive and scalable analysis of how and why learners engage in collaboration in networked communities. The proposed research method, anticipated research outcomes, and contributions to the learning analytics field are discussed.
Learning Analytics for Communities of Inquiry
Author: Kovanovic, Vitomir
Date: 2014-00-00
Type: Journal Articles
Subjects: Communities of Practice,Doctoral Dissertations,Electronic Learning,Active Learning,Inquiry,Models,Social Networks,Network Analysis,Discussion Groups,Coding,Database Management Systems,Information Management,Technology Uses in Education,Statistical Analysis

This paper describes doctoral research that focuses on the development of a learning analytics framework for inquiry-based digital learning. Building on the Community of Inquiry model (CoI)--a foundation commonly used in the research and practice of digital learning and teaching--this research builds on the existing body of knowledge in two important ways. First, given that the CoI model requires substantial manual coding of student discourse, its potential for guiding pedagogical interventions are limited. Thus, the first contribution is the development of a learning analytics system that automates this coding process by means of a novel text-classification algorithm that takes into the account the process nature of inquiry-based learning and the specifics of communication through asynchronous discussions. Furthermore, it is equally important to investigate how learning processes unfold over time through student interactions with information, technology, and other course participants. With this in mind, the second contribution of this research focuses on the development of analytical models that provide insight into these important aspects of inquiry-based learning.
eWorkbooks for Mathematics: Mapping the Independent Learning Experiences of Elementary Students with Learning Disabilities
Author: Kaczorowski, Tara
Date: 2014-00-00
Type: Journal Articles
Subjects: Workbooks,Electronic Publishing,Learning Experience,Independent Study,Elementary School Students,Elementary School Mathematics,Learning Disabilities,Case Studies,Video Technology,Mathematics Instruction,Technology Uses in Education,Qualitative Research,Grade 3,Multimedia Materials,Grounded Theory,Technological Advancement

In this paper, we describe a small case study exploring how four elementary students with mathematics learning disabilities utilized mobile technology (the eWorkbook) during core math instruction in a general education setting. The lead author designed the eWorkbook intervention to provide a flexible learning experience optimized for diverse learners. Sophisticated video-coding software was used to map the actions of the students as they engaged with the tool during the independent practice portion of math instruction. Results related to engagement, independence, accuracy, and specific affordances and barriers to learning with mobile technology are reported.
Labour Market Driven Learning Analytics
Author: Kobayashi, Vladimer
Date: 2014-00-00
Type: Journal Articles
Subjects: Labor Market,Data Collection,Data Analysis,Employment,Readiness,Goal Orientation,Skill Development,Job Skills,Computer Software,Technology Uses in Education,Guidance

This paper briefly outlines a project about integrating labour market information in a learning analytics goal-setting application that provides guidance to students in their transition from education to employment.
Why Theory Matters More than Ever in the Age of Big Data
Author: Wise, Alyssa Friend
Date: 2015-00-00
Type: Journal Articles
Subjects: Learning Theories,Predictor Variables,Data,Data Analysis,Data Interpretation,Models,Research Methodology,Student Records,Educational Research,Barriers

It is an exhilarating and important time for conducting research on learning, with unprecedented quantities of data available. There is a danger, however, in thinking that with enough data, the numbers speak for themselves. In fact, with larger amounts of data, theory plays an ever-more critical role in analysis. In this introduction to the special section on learning analytics and learning theory, we describe some critical problems in the analysis of large-scale data that occur when theory is not involved. These questions revolve around what variables a researcher should attend to and how to interpret a multitude of micro-results and make them actionable. We conclude our comments with a discussion of how the collection of empirical papers included in the special section, and the commentaries that were invited on them, speak to these challenges, and in doing so represent important steps towards theory-informed and theory-contributing learning analytics work. Our ultimate goal is to provoke a critical dialogue in the field about the ways in which learning analytics research draws on and contributes to theory.
Dialogue as Data in Learning Analytics for Productive Educational Dialogue
Author: Knight, Simon
Date: 2015-00-00
Type: Journal Articles
Subjects: Dialogs (Language),Data Collection,Data Analysis,Artificial Intelligence,Natural Language Processing,Man Machine Systems,Accountability,Group Discussion,Computer Mediated Communication

This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on- and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers.