ethanporcaro/eric-fulltext-meta huggingface dataset:
A Resource-Constrained Approach to Implementing Analytics in an Institution of Higher Education: An Experience Report
Author: Buerck, John P.
Date: 2014-00-00
Type: Journal Articles
Subjects: Higher Education,Educational Research,Data Analysis,Data Collection,Academic Achievement,School Holding Power,Integrated Learning Systems

Academic analytics and learning analytics have been increasingly adopted by academic institutions of higher learning for improving student performance and retention. While several studies have reported the implementation details and the successes of specific analytics initiatives, relatively fewer studies exist in literature that describe the possible constraints that can preclude an academic or learning analytics initiative from succeeding fully, meeting the criteria of success as defined by the stakeholders affected by such initiatives. Our aim in this article is to describe the constraints that precluded a successful completion of our analytics initiative and how we re-envisioned our approach and scope to achieve our primary goals while operating within the constraints and tools associated with our academic environment.
Epistemology, Assessment, Pedagogy: Where Learning Meets Analytics in the Middle Space
Author: Knight, Simon
Date: 2014-00-00
Type: Journal Articles
Subjects: Data Analysis,Data Collection,Educational Research,Epistemology,Educational Assessment,Instruction,Design,Discourse Analysis,Scaffolding (Teaching Technique),Beliefs,Apprenticeships,Communities of Practice,Constructivism (Learning),Models

Learning Analytics is an emerging research field and design discipline that occupies the &quot;middle space&quot; between the learning sciences/educational research and the use of computational techniques to capture and analyze data (Suthers &amp; Verbert, 2013). We propose that the literature examining the triadic relationships between &quot;epistemology&quot; (the nature of knowledge), &quot;pedagogy&quot; (the nature of learning and teaching), and &quot;assessment&quot; provide critical considerations for bounding this middle space. We provide examples to illustrate the ways in which the understandings of particular analytics are informed by this triad. As a detailed worked example of how one might design analytics to scaffold a specific form of higher order learning, we focus on the construct of epistemic beliefs: beliefs about the nature of knowledge. We argue that analytics grounded in a pragmatic, socio-cultural perspective are well placed to explore this construct using discourse-centric technologies. The examples provided throughout this paper, through emphasizing the consideration of intentional design issues in the middle space, underscore the &quot;interpretative flexibility&quot; (Hamilton &amp; Feenberg, 2005) of new technologies, including analytics.
A Review of Psychometric Data Analysis and Applications in Modelling of Academic Achievement in Tertiary Education
Author: Gray, Geraldine
Date: 2014-00-00
Type: Journal Articles
Subjects: Psychometrics,Data Analysis,Academic Achievement,Postsecondary Education,Educational Research,Data Collection,Learning Processes,Learning Strategies,Cognitive Ability,Electronic Learning,Motivation,Correlation,Grade Point Average,Hierarchical Linear Modeling,Regression (Statistics),Bayesian Statistics,College Students,Intelligent Tutoring Systems,Self Efficacy

Increasing college participation rates, and diversity in student population, is posing a challenge to colleges in their attempts to facilitate learners achieve their full academic potential. Learning analytics is an evolving discipline with capability for educational data analysis that could enable better understanding of learning process, and therefore mitigate these challenges. The outcome from such data analysis will be dependent on the range, type, and quality of available data and the type of analysis performed. This study reviewed factors that could be used to predict academic performance, but which are currently not systematically measured in tertiary education. It focused on psychometric factors of ability, personality, motivation, and learning strategies. Their respective relationships with academic performance are enumerated and discussed. A case is made for their increased use in learning analytics to enhance the performance of existing student models. It is noted that lack of independence, linear additivity, and constant variance in the relationships between psychometric factors and academic performance suggests increasing relevance of data mining techniques, which could be used to provide useful insights on the role of such factors in the modelling of learning process.
An Adaptive Model of Student Performance Using Inverse Bayes
Author: Lang, Charles
Date: 2014-00-00
Type: Journal Articles
Subjects: Bayesian Statistics,Academic Achievement,Prediction,Student Behavior,Models,Adolescents

This article proposes a coherent framework for the use of Inverse Bayesian estimation to summarize and make predictions about student behaviour in adaptive educational settings. The Inverse Bayes Filter utilizes Bayes theorem to estimate the relative impact of contextual factors and internal student factors on student performance using time series data across a range of possible dimensions. The Inverse Bayesian algorithm treats the student as a Bayesian learner; her partial credit score or confidence is proportional to both her prior knowledge and how she interprets her environment. Once the algorithm has weighted internal and external factors, this information is used to make a prediction about the student's next attempt.
Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes
Author: Pardos, Zachary A.
Date: 2014-00-00
Type: Journal Articles
Subjects: Affective Behavior,Student Behavior,Learner Engagement,Web Based Instruction,Educational Technology,Technology Uses in Education,Tutoring,High Stakes Tests,Mathematics Tests,Longitudinal Studies,Probability,Problem Solving,Correlation,Scaffolding (Teaching Technique),Models,Predictor Variables,Standardized Tests,Scores,Performance,Mathematics Instruction,Middle School Students,Urban Schools,Observation,Coding

In this paper, we investigate the correspondence between student affect and behavioural engagement in a web-based tutoring platform throughout the school year and learning outcomes at the end of the year on a high-stakes mathematics exam in a manner that is both longitudinal and fine-grained. Affect and behaviour detectors are used to estimate student affective states and behaviour based on post-hoc analysis of tutor log-data. For every student action in the tutor, the detectors give us an estimated probability that the student is in a state of boredom, engaged concentration, confusion, or frustration, and estimates of the probability that the student is exhibiting off-task or gaming behaviours. We used data from the ASSISTments math tutoring system and found that boredom during problem solving is negatively correlated with performance, as expected; however, boredom is positively correlated with performance when exhibited during scaffolded tutoring. A similar pattern is unexpectedly seen for confusion. Engaged concentration and, surprisingly, frustration are both associated with positive learning outcomes. In a second analysis, we build a unified model that predicts student standardized examination scores from a combination of student affect, disengaged behaviour, and performance within the learning system. This model achieves high overall correlation to standardized exam score, showing that these types of features can effectively infer longer-term learning outcomes.
Contemporary Privacy Theory Contributions to Learning Analytics
Author: Heath, Jennifer
Date: 2014-00-00
Type: Journal Articles
Subjects: Privacy,Theories,Data Analysis,Higher Education,Information Security

With the continued adoption of learning analytics in higher education institutions, vast volumes of data are generated and &quot;big data&quot; related issues, including privacy, emerge. Privacy is an ill-defined concept and subject to various interpretations and perspectives, including those of philosophers, lawyers, and information systems specialists. This paper provides an overview of privacy and considers the potential contribution contemporary privacy theories can make to learning analytics. Conclusions reflect on the suitability of these theories towards the advancement of learning analytics and future research considers the importance of hearing the student voice in this space.
Characterizing User Behaviours and Products in an Online Educational Community: A Comparison between Novices and Elders
Author: Yuan, Min
Date: 2014-00-00
Type: Journal Articles
Subjects: Electronic Learning,Novices,Comparative Analysis,Users (Information),Behavior,Older Adults,Communities of Practice

This study focuses on two user types in an online educational community--novices and elders--and explores the characteristics of their behaviours and contributed products by analyzing usage logs. Results show that novices and elders have different behavioural trajectories. In addition, the products created by elders attract more views.
Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative
Author: Jayaprakash, Sandeep M.
Date: 2014-00-00
Type: Journal Articles
Subjects: At Risk Students,College Students,Open Source Technology,Data Analysis,Educational Research,Higher Education,Cooperative Programs,Data Collection,Predictive Validity,Intervention,Dropout Prevention,Pilot Projects,Community Colleges,Private Colleges,Public Colleges,Black Colleges,Technology Uses in Education

The Open Academic Analytics Initiative (OAAI) is a collaborative, multi-year grant program aimed at researching issues related to the scaling up of learning analytics technologies and solutions across all of higher education. The paper describes the goals and objectives of the OAAI, depicts the process and challenges of collecting, organizing and mining student data to predict academic risk, and report results on the predictive performance of those models, their portability across pilot programs at partner institutions, and the results of interventions on at-risk students.
Practice and Repetition during Exam Preparation in Blended Learning Courses: Correlations with Learning Results
Author: Andergassen, Monika
Date: 2014-00-00
Type: Journal Articles
Subjects: Drills (Practice),Repetition,Blended Learning,Correlation,Integrated Learning Systems,Computer Uses in Education,Scores,Transfer of Training,Instructional Materials,Foreign Countries,Business Administration Education,Data Analysis,Grades (Scholastic),Study,Undergraduate Students

Learner-centric research on factors influencing learning results has focused, among other things, on student characteristics, demographic data, and usage patterns in learning management systems (LMSs). This paper complements the existing research by investigating potential correlations between learning results and LMS usage during exam preparation, focusing on practice and repetition. Based on 250 million log-file entries used to analyze student interactions within specific courses and overall in the LMS, results show positive, albeit modest, correlations between usage variables and final exam grades. Regarding practice, the number of learning days and the number of days between the first and the last learning sessions correlate better than the coverage of different learning materials. The findings for repetition indicate that it is more beneficial to transfer learning to new tasks than to repeat the same items many times. The study not only looks at single usage variables but also examines the distribution of the descriptive and dependent variables and uses visualization techniques and quantiles to deal with outliers. This paper describes the largest empirical study of learner interactions in blended learning courses conducted so far (at least according to the authors' knowledge) and including techniques for processing and analyzing large datasets about LMS usage.
Setting Learning Analytics in Context: Overcoming the Barriers to Large-Scale Adoption
Author: Ferguson, Rebecca
Date: 2014-00-00
Type: Journal Articles
Subjects: Educational Research,Data Collection,Data Analysis,Barriers,Adoption (Ideas),Case Studies,Foreign Countries,Higher Education,Technology Uses in Education,Open Universities,Program Implementation,Curriculum Development,Educational Assessment,Educational Technology,Intervention

A core goal for most learning analytic projects is to move from small-scale research towards broader institutional implementation, but this introduces a new set of challenges because institutions are stable systems, resistant to change. To avoid failure and maximize success, implementation of learning analytics at scale requires explicit and careful consideration of the entire TEL [technology-enhanced learning] technology complex: the different groups of people involved, the educational beliefs and practices of those groups, the technologies they use, and the specific environments within which they operate. It is crucial not only to provide analytics and their associated tools, but also to begin with a clear strategic vision, assess institutional culture critically, identify potential barriers to adoption, develop approaches that can overcome these, and put in place appropriate forms of support, training, and community building. In this paper, we offer tools and case studies that will support educational institutions in deploying learning analytics at scale with the goal of achieving specified learning and teaching objectives. The ROMA [RAPID Outcome Mapping Approach] Framework offers a step-by-step approach to the institutional implementation of learning analytics and this approach is grounded here by case studies of practice from the UK and Australia.
Analysis and Reflections on the Third Learning Analytics and Knowledge Conference (LAK 2013)
Author: Ochoa, Xavier
Date: 2014-00-00
Type: Journal Articles
Subjects: Data Analysis,Data Collection,Conferences (Gatherings),Content Analysis,Bibliometrics,Citation Analysis,Visualization,Behavioral Sciences,Socialization,Online Courses,Ethics,Scaling,Educational Trends,Interdisciplinary Approach,Instruction,Epistemology,Authors,Word Frequency,Periodicals,Scholarship,Conference Papers

Analyzing a conference, especially one as young and focused as LAK, provides the opportunity to observe the structure and contributions of the scientific community around it. This work will perform a Scientometric analysis, coupled with a more in-depth manual content analysis, to extract this insight from the proceedings and program of LAK 2013. Authorship analysis reveals an open and international community, while internal citation analysis provides evidence of the construction of a body of knowledge central to Learning Analytics. The analysis of the content of the papers found five main topics: visualization, behaviour analysis, social learning analytics, learning analytics for MOOCs, and learning analytics issues (ethical, scalability, etc.), as well as papers reflecting on the field itself. We discuss representative papers presented at the conference, highlighting trends and new developments. Learning analytics is a diverse multidisciplinary field with an emerging interdisciplinary core, well situated to benefit from productive dialogue concerning its scope and purpose and to reflect on the pedagogies and epistemologies implied by its methods.
Connecting Analytics and Curriculum Design: Process and Outcomes of Building a Tool to Browse Data Relevant to Course Designers
Author: Dunbar, Robert L.
Date: 2014-00-00
Type: Journal Articles
Subjects: Curriculum Design,Data Analysis,Data Collection,Higher Education,Evidence Based Practice,College Faculty,Instructional Design,Educational Research,Surveys,Focus Groups,Models,Educational Technology,Technology Uses in Education

The disconnect between data collection and analysis across academic and administrative units within institutions of higher education makes it challenging to incorporate diverse data into curricular design. Understanding the factors related to student retention and success is unlikely to occur by focusing on only one unit at a time. By promoting course design informed by data from diverse units, we are more likely to understand important connections that could encourage more effective and holistic change. Facilitating evidence-based course design should begin with a faculty-driven process to identify how best to explore data across units for integration into the traditional course/curricular design. Our paper describes how data from institutional, learning, and what we call &quot;developmental&quot; analytics can be incorporated into course and curricular design by using a purposefully built analysis tool that permits the exploration of data relevant to course/curriculum design. This Browser of Student and Course Objects (BoSCO) is being built in a faculty-driven process and can be used as a bridge between the analytics space and the course/curriculum design environments, therefore encouraging faculty to use analytics for course and curricular design.
Analyzing the Main Paths of Knowledge Evolution and Contributor Roles in an Open Learning Community
Author: Halatchliyski, Iassen
Date: 2014-00-00
Type: Journal Articles
Subjects: Path Analysis,Knowledge Level,Communities of Practice,Computer Mediated Communication,Network Analysis,Cooperation,Graphs,Visualization,Educational Resources,Learning Processes,Social Networks,Cooperative Learning,Data Collection,Data Analysis,Biology,Engineering

This paper introduces the scientometric method of main path analysis and its application in an exemplary study of the paths of knowledge development and the roles of contributors in Wikiversity. Data from two scientific domains in this online learning community has been used. We see this as a step forward in adapting and adopting network analysis techniques for analyzing collaboration processes in knowledge building communities. The analysis steps are presented in detail including the description of a tool environment (&quot;workbench&quot;) designed for flexible use by non-computer experts. By identifying directed acyclic graphs, the meaningful interconnections between developing learning resources are analyzed by considering their temporal sequence. A schema for the visualization of the results is introduced. The potential of the method is elaborated for the evaluation of the overall learning process in different domains as well as for the individual contributions of the participants. Different outstanding roles of contributors in Wikiversity are presented and discussed.
Introduction into Sparks of the Learning Analytics Future
Author: Pechenizkiy, Mykola
Date: 2014-00-00
Type: Journal Articles
Subjects: Educational Research,Data Collection,Data Analysis,Doctoral Programs,Graduate Students,Longitudinal Studies,Network Analysis,Online Courses,Educational Technology,Blended Learning,Electronic Learning,Summer Programs

This section offers a compilation of 16 extended abstracts summarizing research of the doctoral students who participated in the Second Learning Analytics Summer Institute (LASI 2014) held at Harvard University in July 2014. The abstracts highlight the motivation, main goals and expected contributions to the field from the ongoing learning analytics doctoral research around the globe. These works cover several major topics in learning analytics including novel methods for automated annotations, longitudinal analytic studies, networking analytics, multi-modal analytics, dashboards, and data-driven feedback and personalization. The assumed settings include the traditional classroom, online and mobile learning, blended learning, and massive open online course education models.
Learning Dashboards
Author: Charleer, Sven
Date: 2014-00-00
Type: Journal Articles
Subjects: Information Management,Information Utilization,Student Records,Progress Monitoring,Visual Aids,Data Collection,Semi Structured Interviews

This article explores how information visualization techniques can be applied to learning analytics data to help teachers and students deal with the abundance of learner traces. We also investigate how the affordances of large interactive surfaces can facilitate a collaborative sense-making environment for multiple students and teachers to explore these learner traces together.
Keys to Detecting Writing Flexibility over Time: Entropy and Natural Language Processing
Author: Snow, Erica L.
Date: 2015-00-00
Type: Journal Articles
Subjects: Writing (Composition),Writing Strategies,Hypothesis Testing,Essays,High School Students,Natural Language Processing,Automation,Scientific Concepts,Prior Learning,Literacy,Reading Comprehension,Vocabulary,Student Characteristics,Educational Technology,Technology Uses in Education,Scoring,Individual Differences,Writing Skills,Pretests Posttests,Reading Tests

Writing researchers have suggested that students who are perceived as strong writers (i.e., those who generate texts rated as high quality) demonstrate flexibility in their writing style. While anecdotally this has been a commonly held belief among researchers and educators, there is little empirical research to support this claim. This study investigates this hypothesis by examining how students vary in their use of two linguistic features (i.e., narrativity and cohesion) across 16 prompt-based essays. Forty-five high school students wrote 16 essays across 8 sessions within an Automated Writing Evaluation (AWE) system. Natural language processing (NLP) techniques and Entropy analyses were used to calculate how rigid or flexible students were in their use of narrative and cohesive linguistic features over time and how this trait related to individual differences in literacy abilities (i.e., vocabulary knowledge and comprehension ability), prior world knowledge, and essay quality. For instance, through the unique combination of NLP and Entropy, we found that patterns of narrative flexibility (or rigidity) were related, significantly and reliably, to students' prior reading comprehension ability after 2 sessions (4 essays). Similarly, students' flexible (or rigid) use of cohesive features was reliably related to their prior reading comprehension ability after 5 sessions (10 essays). These exploratory methodologies are important for researchers and educators, as they indicate that writing flexibility is indeed a trait of strong writers and can be detected rather quickly using the combination of textual features and dynamic analyses. [There is a page numbering error on the PDF. The pages of the PDF are numbered 40-46, 38-54.]