Table of Contents

• Formative assessment method of real-world learning by integrating heterogeneous elements of behavior, knowledge, and the environmentMasaya Okada, Masahiro Tada. 1-10 [doi]
• Clustering for improving educational process miningAlejandro Bogarín, Cristóbal Romero, Rebeca Cerezo, Miguel Sánchez-Santillán. 11-15 [doi]
• Customized course advising: investigating engineering student success with incoming profiles and patterns of concurrent course enrollmentSungJin Nam, Steven Lonn, Thomas Brown, Cinda-Sue Davis, Darryl Koch. 16-25 [doi]
• Explaining predictive models to learning specialists using personasChristopher A. Brooks, Jim E. Greer. 26-30 [doi]
• Temporal learning analytics for computer based testingZacharoula K. Papamitsiou, Vasileios Terzis, Anastasios A. Economides. 31-35 [doi]
• Sleepers' lag - study on motion and attentionMirko Raca, Roland Tormey, Pierre Dillenbourg. 36-43 [doi]
• Clustering of design decisions in classroom visual displaysMa. Victoria Almeda, Peter Scupelli, Ryan S. Baker, Mimi Weber, Anna Fisher. 44-48 [doi]
• Data wranglers: human interpreters to help close the feedback loopDoug Clow. 49-53 [doi]
• Toward unobtrusive measurement of reading comprehension using low-cost EEGYueran Yuan, Kai-min Chang, Jessica Nelson Taylor, Jack Mostow. 54-58 [doi]
• Learning analytics in CSCL with a focus on assessment: an exploratory study of activity theory-informed cluster analysisWanli Xing, Bob Wadholm, Sean P. Goggins. 59-67 [doi]
• On using markov chain to evidence the learning structures and difficulty levels of one digit multiplicationBehnam Taraghi, Martin Ebner, Anna Saranti, Martin Schön. 68-72 [doi]
• Context personalization, preferences, and performance in an intelligent tutoring system for middle school mathematicsStephen E. Fancsali, Steven Ritter. 73-77 [doi]
• Interaction design for improved analyticsMaria Mendiburo, Brian Sulcer, Ted S. Hasselbring. 78-82 [doi]
• Visualizing patterns of student engagement and performance in MOOCsCarleton Coffrin, Linda Corrin, Paula de Barba, Gregor Kennedy. 83-92 [doi]
• Small to big before massive: scaling up participatory learning analyticsDaniel T. Hickey, Tara Alana Kelley, Xinyi Shen. 93-97 [doi]
• Success, activity and drop-outs in MOOCs an exploratory study on the UNED COMA coursesJosé Luis Santos, Joris Klerkx, Erik Duval, David Gago, Luis Rodríguez. 98-102 [doi]
• Engagement vs performance: using electronic portfolios to predict first semester engineering student retentionEveraldo Aguiar, Nitesh V. Chawla, Jay Brockman, G. Alex Ambrose, Victoria Goodrich. 103-112 [doi]
• Perceptions and use of an early warning system during a higher education transition programStephen Aguilar, Steven Lonn, Stephanie D. Teasley. 113-117 [doi]
• Modest analytics: using the index method to identify students at risk of failureTim Rogers, Cassandra Colvin, Belinda A. Chiera. 118-122 [doi]
• Analytics of the effects of video use and instruction to support reflective learningDragan Gasevic, Negin Mirriahi, Shane Dawson. 123-132 [doi]
• Peer assessment based on ratings in a social media courseAndrii Vozniuk, Adrian Holzer, Denis Gillet. 133-137 [doi]
• Collaborative spatial classificationEric Coopey, R. Benjamin Shapiro, Ethan Danahy. 138-142 [doi]
• Assessing elementary students' science competency with text analyticsSamuel P. Leeman-Munk, Eric N. Wiebe, James C. Lester. 143-147 [doi]
• Techniques for data-driven curriculum analysisGonzalo Méndez, Xavier Ochoa, Katherine Chiluiza. 148-157 [doi]
• The impact of learning analytics on the dutch education systemHendrik Drachsler, Slavi Stoyanov, Marcus Specht. 158-162 [doi]
• An exercise in institutional reflection: the learning analytics readiness instrument (LARI)Kimberly E. Arnold, Steven Lonn, Matthew D. Pistilli. 163-167 [doi]
• Competency map: visualizing student learning to promote student successJeff Grann, Deborah Bushway. 168-172 [doi]
• Analysis of dynamic resource access patterns in a blended learning courseTobias Hecking, Sabrina Ziebarth, Heinz Ulrich Hoppe. 173-182 [doi]
• Analyzing the log patterns of adult learners in LMS using learning analyticsIl-Hyun Jo, DongHo Kim, Meehyun Yoon. 183-187 [doi]
• Practice exams make perfect: incorporating course resource use into an early warning systemRichard Joseph Waddington, SungJin Nam. 188-192 [doi]
• Educational data sciences: framing emergent practices for analytics of learning, organizations, and systemsPhilip J. Piety, Daniel T. Hickey, M. J. Bishop. 193-202 [doi]
• Designing pedagogical interventions to support student use of learning analyticsAlyssa Friend Wise. 203-211 [doi]
• A cognitive processing framework for learning analyticsAndrew Gibson, Kirsty Kitto, Jill Willis. 212-216 [doi]
• Statistical discourse analysis of online discussions: informal cognition, social metacognition and knowledge creationMing Ming Chiu, Nobuko Fujita. 217-225 [doi]
• Uncovering what matters: analyzing transitional relations among contribution types in knowledge-building discourseBodong Chen, Monica Resendes. 226-230 [doi]
• Current state and future trends: a citation network analysis of the learning analytics fieldShane Dawson, Dragan Gasevic, George Siemens, Srecko Joksimovic. 231-240 [doi]
• Teaching the unteachable: on the compatibility of learning analytics and humane educationTimothy D. Harfield. 241-245 [doi]
• Establishing an ethical literacy for learning analyticsJenni Swenson. 246-250 [doi]
• Setting learning analytics in context: overcoming the barriers to large-scale adoptionRebecca Ferguson, Doug Clow, Leah Macfadyen, Alfred Essa, Shane Dawson, Shirley Alexander. 251-253 [doi]
• Learning analytics for the social media ageAnatoliy A. Gruzd, Caroline Haythornthwaite, Drew Paulin, Rafa Absar, Michael Huggett. 254-256 [doi]
• Building institutional capacities and competencies for systemic learning analytics initiativesKimberly E. Arnold, Grace Lynch, Daniel Huston, Lorna Wong, Linda Jorn, Christopher W. Olsen. 257-260 [doi]
• Hanzi handwriting acquisition with automatic feedbackChin-Hwa Kuo, Jian-Wen Peng, Wen-Chen Chang. 261-262 [doi]
• Analyzing student notes and questions to create personalized study guidesPerry J. Samson. 263-264 [doi]
• Visual analytics of academic writingDuygu Simsek, Simon Buckingham Shum, Anna De Liddo, Rebecca Ferguson, Ágnes Sándor. 265-266 [doi]
• Open academic early alert system: technical demonstrationSandeep M. Jayaprakash, Eitel J. M. Lauría. 267-268 [doi]
• Educational technology approach toward learning analytics: relationship between student online behavior and learning performance in higher educationTaeho Yu, Il-Hyun Jo. 269-270 [doi]
• Visualizing semantic space of online discourse: the knowledge forum caseBodong Chen. 271-272 [doi]
• eGraph tool: graphing the learning process in LMSsRebeca Cerezo, Natalia Suarez, J. Carlos Núñez, Miguel Sánchez-Santillán. 273-274 [doi]
• Effects of image-based and text-based activities on student learning outcomesAnne K. Greenberg, Melissa Gross, Mary C. Wright. 275-276 [doi]
• Peer evaluation of student generated contentJared Tritz, Nicole Michelotti, Ginger Shultz, Tim McKay, Barsaa Mohapatra. 277-278 [doi]
• Patterns of persistence: what engages students in a remedial english writing MOOC?John Whitmer, Eva Schiorring, Pat James. 279-280 [doi]
• National differences in an international classroomJennifer DeBoer, Glenda S. Stump. 281-282 [doi]
• DCLA14: second international workshop on discourse-centric learning analyticsRebecca Ferguson, Anna De Liddo, Denise Whitelock, Maarten De Laat, Simon Buckingham Shum. 283-284 [doi]
• Computational approaches to connecting levels of analysis in networked learning communitiesHeinz Ulrich Hoppe, Daniel D. Suthers. 285-286 [doi]
• Learning analytics and machine learningDragan Gasevic, Carolyn Penstein Rosé, George Siemens, Annika Wolff, Zdenek Zdráhal. 287-288 [doi]
• The learning analytics & knowledge (LAK) data challenge 2014Hendrik Drachsler, Stefan Dietze, Eelco Herder, Mathieu d'Aquin, Davide Taibi. 289-290 [doi]