Conference Sessions – Friday

Friday, March 17, 2017

Morning Sessions (10:30 AM – 12:00 PM)

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Session: Keynote Discussion and Panel

Session Chair: Alyssa Wise
Keynote Q & A 6A1 (30 min)
Session Chair: Shane Dawson
Panel 6A2 Round Table (55 min): “SoLAR: Initiatives, Opportunities, and Getting Involved”
Dragan Gašević, Moderator (University of Edinburgh, United Kingdom)
Stephanie Teasley, Grace Lynch, Abelardo Pardo & Xavier Ochoa.

Session:Understanding Discourse III

Session Chair: Roger Azevedo
Presentation 6B1 (30 min): “What’d You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor”
(Full Research Paper)
by Laura Allen, Cecile Perret, Aaron Likens & Danielle McNamara 
Abstract:
In this study, we investigated the degree to which the cognitive processes in which students engage during reading comprehension could be examined through dynamical analyses of their natural language responses to texts. High school students (n = 142) generated typed self-explanations while reading a science text. They then completed a comprehension test that measured their comprehension at both surface and deep levels. The recurrent patterns of the words in students’ self-explanations were first visualized in recurrence plots. These visualizations allowed us to qualitatively analyze the different self-explanation processes of skilled and less skilled readers. These recurrence plots then allowed us to calculate recurrence indices, which represented the properties of these temporal word patterns. Results of correlation and regression analyses revealed that these recurrence indices were significantly related to the students’ comprehension scores at both surface and deep levels. Additionally, when combined with summative metrics of word use, these indices were able to account for 32% of the variance in students’ overall text comprehension scores. Overall, our results suggest that recurrence quantification analysis can be utilized to guide both qualitative and quantitative assessments of students’ comprehension.
Presentation 6B2 (30 min):“Honing in on Social Learning Networks in MOOC Forums: Examining Critical Network Definition Decisions”
(Full Research Paper)
by Alyssa Friend Wise, Yi Cui & Wan Qi Jin
Abstract:
This study examines the influence of content-based network partitioning and tie definition on social network structures and interpretation for MOOC discussion forums. Using dynamic interrelated post and thread classification [5] based on a previously developed natural language model [27], 817 threads containing 3124 discussion posts from 567 learners in a MOOC on the use of statistics in medicine were characterized as either being related to the learning of course content or not. Content, non-content, and unparitioned interaction networks were then constructed based on five different tie definitions: Direct Reply, Star, Direct Reply-Star, Limited Copresence, and Total Copresence. Results showed the content and non-content networks to have distinct characteristics at the network, community, and individual node levels, with the unpartitioned network more closely resembling the non-content network. Network properties were less sensitive to differences in tie definition with the exception of Total Copresence, which showed distinct characteristics useful for detecting inflated social status due to “superthread” initiation.
Presentation 6B3 (20 min):“Using Correlational Topic Modeling for Automated Topic Identification in Intelligent Tutoring Systems”
(Short Research Paper)
by Stefan Slater, Ryan Baker, Ma. Victoria, Almeda, Alex Bowers & Neil Heffernan
Abstract:
Student knowledge modeling is an important part of modern personalized learning systems, but typically relies upon valid models of the structure of the content and skill in a domain. These models are often developed through expert tagging of skills to items. However, content creators in crowdsourced personalized learning systems often lack the time (and sometimes the domain knowledge) to tag skills themselves. Fully automated approaches that rely on the covariance of correctness on items can lead to effective skill-item mappings, but the resultant mappings are often difficult to interpret. In this paper we propose an alternate approach to automatically labeling skills in a crowdsourced personalized learning system using correlated topic modeling, a natural language processing approach, to analyze the linguistic content of mathematics problems. We find a range of potentially meaningful and useful topics within the context of the ASSISTments system for mathematics problem-solving.

Session: LA Adoption – Recommendations

Session Chair: Rebecca Ferguson
Presentation 6C1 (30 min):“Are We Losing the Trees for the Forest: A Case for Localized Longitudinal Learning Analytics”
(Practitioner Presentation)
by Alan HackbarthWarschauer
Abstract:
The Learning Analytics community has always advocated a multi-disciplinary approach as the field evolves, with a special emphasis on including of practitioners and stakeholders in education – higher education institutions, MOOCs, policy makers, administrators, K-12 institutions, teachers, and students. While significant strides have been made on the “analytic” side of the equation, the “learning” side of learning analytics, with all the acknowledged complexity, is proving to be the more challenging side of the proposition. Assumptions about teachers – more specifically what they believe about teaching and learning that would drive their interest in analytics – is conspicuously absent from the LA literature. In this paper I identify and discuss teacher dispositions and objectives that would drive the adoption of learning analytics in their practices and suggest what those learning analytics should tell them.
Presentation 6C2 (30 min):“Evolving a Process Model for Learning Analytics Implementation”
(Practitioner Presentation)
by Adam Cooper
Abstract:
The presentation will outline the lessons learned from applying and adapting the Cross Industry Standard Process for Data Mining (CRISP-DM) process model to guide data science activities for learning analytics. It will make connections with the complexity of institutional change processes and summarize an adapted form of CRISP-DM which better matches the need for data science activities to accommodate emerging institutional objectives and stakeholder engagement in institutions which are taking the first steps towards adoption in practice.
Presentation 6C3 (20 min):“What do students want? Towards an instrument for students’ evaluation of quality of learning analytics services”
(Short Research paper)
by Alexander Whitelock -Wainwright, Dragan Gašević & Ricardo Tejeiro
Abstract:
Quality assurance in any organization is important for ensuring that service users are satisfied with the service offered. For higher education institutes, the use of service quality measures allows for ideological gaps to be both identified and resolved. The learning analytic community, however, has rarely addressed the concept of service quality. A potential outcome of this the provision of a learning analytics service that only meets the expectations of certain stakeholders (e.g., managers), whilst overlooking those who are most important (e.g., students). In order to resolve this issue, we outline a framework and our current progress towards developing a scale to assess student expectations and perceptions of learning analytics as a service.

Session: Understanding Student Behaviour – General

Session Chair: Roberto Martinez-Maldonado
Presentation 6D1 (30 min):“Learning Analytics in a Seamless Learning Environment” (Full Research Paper)
by Kousuke Mouri, Hiroaki Ogata & Noriko Uosaki
Abstract:
This paper describes seamless learning analytics methods of VASCORLL (Visualization and Analysis System for COnnecting Relationships of Learning Logs). VASCORLL is a system for visualizing and analyzing the learning logs collected by the seamless learning system, which supports language learning in the real-world. As far, several studies have been made in the seamless learning environments in order to bridge formal learning over informal learning. However, their focus was the implementation of the seamless learning environment in education. This study focuses on visualizing and analyzing learning logs collected in the seamless learning environment. This paper describes how our analytics could contribute to bridging the gap between formal and informal learning. An experiment was conducted to evaluate 1) whether our developed VASCORLL is effective in connecting the words learned in formal learning to the ones learned in informal learning, 2) which social network algorithm is effective to enhance learning in the seamless learning environment. Twenty international students participated in the evaluation experiment, and they were able to increase their learning opportunities by using VASCORLL. In addition, it was found that the fundamental network algorithm called betweenness centrality is useful in finding central words bridging formal and informal learning.
Presentation 6D2 (30 min):“SPACLE: Investigating learning across virtual and physical spaces using spatial replays”
(Full Research Paper)
by Kenneth Holstein, Bruce M. McLaren & Vincent Aleven
Abstract:
Classroom experiments that evaluate the effectiveness of educational technologies do not typically examine the effects of classroom contextual variables (e.g., out-
of-software help-giving and external distractions). Yet, both within and across classroom studies, these variables may influence students’ instructional outcomes. In this paper, we introduce Spatial Classroom Log Exploration (SPACLE): a visualization method that facilitates the rapid discovery of relationships between within-software and out-of-software events. Unlike previous methods for retrospective analysis, SPACLE replays moment-by-moment analytics about student and teacher behaviors in their original spatial context. We present a data analysis workflow using SPACLE and demonstrate how this workflow can support causal discovery. We share the results of our initial replay analyses using SPACLE, which highlight the importance of considering spatial factors in the classroom when analyzing ITS log data. We also present the results of an investigation into the effects of student-teacher interactions on student learning in K-12 blended classrooms, using our workflow, which combines replay analysis with SPACLE and causal modeling. Our findings suggest that students’ awareness of being monitored by their teachers may promote learning, and that “gaming the system” behaviors may extend outside of educational software use.

Early Afternoon Sessions (01:00 PM – 02:20 PM)

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Session: Adaptive Learning

Session Chair: Srecko Joksimovic
Presentation 7A1 (30 min):“Implementation of Adaptive Learning for Automotive Examination Preparation at the British Columbia Institute of Technology Using Brightspace LeaP”
(Practitioner Presentation)
by Stephen Michaud & Lawrence Potyondi
Abstract:
Lawrence Potyondi at the British Columbia Institute of Technology (BCIT) took the 10 week “Automotive Service Technician IP Refresher” course and rebuilt it using Brightspace LeaP, an adaptive learning tool. With hundreds of content items and thousands of questions for use with LeaP, each module guided students through the course with LeaP paths to prepare them for the Automotive Service Technician Inter-Provincial (IP) examination and/or the respected ITA level exams. The pilot program involved 53 students and results will be available at the end of the pilot (December 31, 2016).
Presentation 7A2 (20 min):“Enhancing Learning Through Virtual Reality and Neurofeedback: A First Step”
(Short Research Paper)
by Ryan Hubbard, Aldis Sipolins & Lin Zhou
Abstract:
Virtual reality presents exciting new prospects for the delivery of educational materials to students. By combining this technology with biological sensors, a student in a virtual educational environment can be monitored for physiological markers of engagement or more cognitive states of learning. With this information, the virtual reality environment can be adaptively altered to reflect the student’s state, essentially creating a closed-loop feedback system. This paper explores these concepts, and presents preliminary data on a combined EEG-VR working memory experiment as a first step toward a broader implementation of an intelligent adaptive learning system. This first-pass neural time-series and oscillatory data suggest that while an EEG-based neurofeedback system is feasible, more work on removing artifacts and identifying relevant and important features will lead to higher prediction accuracy.
Presentation 7A3 (20 min):“Strategy for recommendation based on legacy VLE activity”
(Short Research Paper)
by Michal Huptych, Michal Bohuslavek, Martin Hlosta & Zdenek Zdrahal
Abstract: 
This paper introduces two measures for the recommendation of study materials based on students’ past study activity. We use records from the Virtual Learning Environment (VLE) and analyse the activity of previous students. This process is difficult to preform manually. Therefore, we assume that the activity of past students represents patterns, which can be used as a basis for recommendations to current students. The measures we define are Relevance, for description of a supposed VLE activity derived from previous students of the course, and Effort, that represents the actual effort of individual current students. Based on these measures, we propose a composite measure, which we call Importance. We use data from the previous course presentations to evaluate of the consistency of students’ behaviour. We use correlation of the defined measures Relevance and Average Effort to evaluate the behaviour of two different student cohorts and the Root Mean Square Error to measure the deviation of Average Effort and individual student Effort.

Session: Understanding Student Behaviour – Help-Seeking/Search

Session Chair: Inge Molenaar
Presentation 7B1 (30 min):“Supporting collaborative learning with tag recommendations: a real-world study in an inquiry-based classroom project”
(Full Research Paper)
by 
Simone Kopeinik, Elisabeth Lex, Paul Seitliner, Dietrich Albert & Tobias Le
y
Abstract:
In online social learning environments, tagging has demonstrated its potential to facilitate search, to improve recommendations and to foster reflection and learning. Studies have shown that as a prerequisite for learning, shared understanding needs to be established in the group. We hypothesise that this can be fostered through tag recommendation strategies that contribute to semantic stabilization. In this study, we investigate the application of two tag recommenders that are inspired by models of human memory: (i) the base-level learning equation BLL and (ii) Minerva. BLL models the frequency and recency of tag use while Minverva is based on frequency of tag use and semantic context. We test the impact of both tag recommenders on semantic stabilization in an online study with 51 students completing a group-based inquiry learning project in school. We find that displaying tags from other group members contributes significantly to semantic stabilization in the group, as compared to a strategy where tags from the students’ individual vocabularies are used. Testing for the accuracy of the different recommenders revealed that algorithms using frequency counts such as BLL performed better when individual tags were recommended. When group tags were recommended, the Minerva algorithm performed better. We conclude that tag recommenders, exposing learners to each other’s tag choices by simulating search processes on learners’ semantic memory structures, show potential to support semantic stabilization and thus, inquiry-based learning in groups.
Presentation 7B2 (20 min):“Classifying Help Seeking Behaviour in Online Communities”
(Short Research Paper)
by Sebastian Cross, Zak Waters, Kirsty Kitto & Guido Zuccon
Abstract:
While help seeking has been extensively studied using self report survey data and models, there is a lack of content analysis techniques that can be directly applied to classify help seeking behaviour. In this preliminary work we propose a coding scheme which is then applied to an open dataset that we have created by carefully selecting sub groups from two popular discussion sites (Reddit and StackExchange). We then explore the possibility for automatically classifying help seeking behaviour using machine learning models. A preliminary model provides good initial results, suggesting that it may indeed be possible to construct student support systems that build off of an accurate classifier.
Presentation 7B3 (20 min):“Using learning analytics to explore help-seeking learner profiles in MOOCs”
(Short Research Paper)
by Linda Corrin, Paula G. de Barba & Aneesha Bakharia
Abstract:
In online learning environments, learners are often required to be more autonomous in their approach to learning. In scaled online learning environments, like Massive Open Online Courses (MOOCs), there are differences in the ability of learners to access teachers and peers to get help with their study than in more traditional educational environments. This exploratory study examines the help-seeking behaviour of learners across several MOOCs with different audiences and designs. Learning analytics techniques (e.g., dimension reduction with t-sne and clustering with affinity propagation) were applied to identify clusters and determine profiles of learners on the basis of their help-seeking behaviours. Five help-seeking learner profiles were identified which also provide an insight into how learners’ help-seeking behaviour relates to performance. The development of a more in depth understanding of how learners seek help in large online learning environments is important to inform the way support for learners can be incorporated into the design and facilitation of online courses delivered at scale.

Session: Affective Learning

Session Chair: Xavier Ochoa
Presentation 7C1 (30 min):“EMODA: a Tutor Oriented Multimodal and Contextual Emotional Dashboard”
(Full Research Paper)
by Mohamed Ez Zaouia & Elise Lavoué
Abstract:
Learners’ emotional state has proven to be a key factor for successful learning. Visualizing learners’ emotions during synchronous on-line learning activity can help the tutor in creating and maintaining socio-affective relationships with their learners. However, few dashboards offer emotional information on the learning activity. The current study focuses on synchronous interactions via a videoconferencing tool dedicated to foreign language training. We collected data on learners’ emotions in real conditions during ten sessions (five sessions for two learners). We propose to adopt and combine different models of emotions (discrete and dimensional) and to use heterogeneous APIs for measuring learners’ emotions from different data sources (audio, video, self-reporting and interaction traces). Based on a deep data analysis, we propose an approach to combine the different cues to infer information on learners’ emotional states. We finally present the EMODA dashboard, an affective multimodal and contextual visual analytics dashboard that allows the tutor monitoring learners’ emotions and better understand their evolution during the synchronous learning activity.
Presentation 7C2 (20 min):“Person-Centered Approach to Explore Learner’s Emotionality in Learning within a 3D Narrative Game”
(Short Research Paper)
by Zhenhua Xu & Earl Woodruff
Abstract:
Emotions form an integral part of our cognitive function. Past research has demonstrated conclusive associations between emotions and learning achievement [7, 26, 27]. This paper used a person-centered approach to explore students’ (N = 65) facial behavior, emotions, learner traits and learning. An automatic facial expression recognition system was used to detect both middle school and university students’ real-time facial movements while they learned scientific tasks in a 3D narrative video game. The results indicated a strong statistical relationship between three specific facial movements (i.e., outer brow raising, lip tightening and lip pressing), student self-regulatory learning strategy and learning performance. Outer brow raising (AU2) had strong predictive power when a student is confronted with obstacles and does not know how to proceed. Both lip tightening and pressing (AU23 and AU24) were predictive when a student engaged in a task that requires a deep level of incoming information processing and short memory activation. The findings also suggested a correlational relationship between student self-regulatory learning strategy use and neutral state. It is hoped that this study will provide empirical evidence for helping us develop a deeper understanding of the relationship between facial behavior and complex learning especially in educational games.
Presentation 7C3 (20 min):“Using Data Visualizations to Foster Emotion Regulation during Self-Regulated Learning with Advanced Learning Technologies: A Conceptual Framework”
(Short Research Paper)
by Roger Azevedo, Garrett Millar, Michelle Taub, Nicholas Mudrick, Amanda Bradbury, Megan Price
Abstract:
Emotions play a critical role during learning and problem solving with advanced learning technologies (ALTs). Despite their importance, relatively few attempts have been made to understand learners’ emotional monitoring and regulation by using data visualizations of their own (and others) cognitive, affective, metacognitive, and motivational (CAMM) self-regulated learning (SRL) processes to potentially foster their emotion regulation (ER) during learning with ALTs. We present a theoretically-based and empirically-driven conceptual framework that addresses ER by proposing the use of visualizations of one’s and others’ CAMM-SRL multichannel data (e.g., cognitive strategy use, metacognitive monitoring accuracy, facial expressions of emotions, physiological arousal, eye-movement behaviors, etc.) to facilitate learners’ monitoring and regulation of their emotions during learning with ALTs. We use examples from several of our laboratory and classroom studies to illustrate the mapping between theoretical assumptions, ER strategies, and the types of data visualizations that can be used to enhance learners’ ER, including key processes such as emotion flexibility, emotion adaptivity, and emotion efficacy. We conclude with future directions that can lead to a systematic interdisciplinary research agenda that addresses outstanding ER-related issues by integrating models, theories, methods, and analytical techniques for the areas of cognitive, learning, and affective sciences, HCI, data visualization, big data, data mining, open learner models, and SRL.

Afternoon Sessions (02:50 PM – 04:20 PM) 

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Session: Students at-Risk – Systems

Session Chair: Srecko Joksimovic
Presentation 8A1 (30 min):“Guidance Counselor Reports of the Assistments College Prediction Model (ACPM)”
(Full Research Paper)
by Jaclyn Ocumpaugh, Ryan Baker, Stefan Slater, Maria Ofelia San Pedro, Neil Heffernan, Cristina Heffernan & Aaron Hawn
Abstract:
Advances in the learning analytics community have created opportunities to deliver early warnings that alert teachers and instructors when a student is at risk of not meeting academic goals [6], [71]. as well as for academic advisors in higher education [39], and school and district leaders [33], but less work has focused on creating reports for other individuals in the K-12 system. In this study, we use college enrollment models created for the ASSISTments learning system [55] to provide reports for guidance counselors, who often work directly with students, but usually not in classroom settings. These reports are designed to help guidance counselors work with students to set long term academic and career goals. As such, they use provide both the predictions about whether or not an individual student is likely to attend college (the Assistments College Prediction Model or ACPM), alongside information on student engagement and learning that can inform efforts to support student achievement.
Presentation 8A2 (30 min):“Knowing the Score: Deploying a Risk Score Model in Excelsior’s Student Success Center”
(Practitioner Presentation)
by Lisa Daniels & Glenn Braddock
Abstract:
Lisa Daniels, Assistant Vice President for Analytics and Decision Support, and Glenn Braddock, Executive Director of the Student Success Center (SSC), will describe an ambitious initiative at Excelsior College to guide the delivery of coaching services to at-risk students through predictive analytics and technology. The SSC prioritizes students for support within a caseload management and communications platform based on a set of retention risk score models and an engagement model. The presenters will discuss successes and lessons learned during the first year of SSC operations.
Presentation 8A3 (20 min):“Don’t Call it a Comeback: Academic recovery and the timing of educational technology adoption”
(Short Research Paper)
by Michael Brown, Matt Demonbrun & Stephanie Teasley
Abstract:
Recent research using learning analytics data to explore student performance over the course of a term suggests that a substantial percentage of students who are classified as academically struggling manage to recover. In this study, we report the result of a hazard analysis based on students’ behavioral engagement with different digital instructional technologies over the course of a semester. We observe substantially different adoption and use behavior between students who did and did not experience academic difficulty in the course. Students who experienced moderate academic difficulty benefited the most from using tools that helped them plan their study behaviors. Students who experienced more severe academic difficulty benefited from tools that helped them prepare for exams. We observed that students adopted most tools and system features before they experienced academic difficulty, and students who adopted early were more likely to recover.

Session: Retention

Session Chair: Christopher Brooks
Presentation 8B1 (30 min):“Follow the Successful Crowd: Raising MOOC Completion Rates through Social Comparison at Scale”
(Full Research Paper)
by Dan Davis, Ioana Jivet, René Kizilcec, Guanliang Chen, Claudia Hauff & Geert-Jan Houben
Abstract:
Social comparison theory asserts that we establish our social and personal worth by comparing ourselves to others. In in-person learning environments, social comparison offers students critical feedback on how to behave and be successful. By contrast, online learning environments afford fewer social cues to facilitate social comparison. Can increased availability of such cues promote effective self-regulatory behavior and achievement in Massive Open Online Courses (MOOCs)? We developed a personalized feedback system that facilitates social comparison with previously successful learners based on an interactive visualization of multiple behavioral indicators. Across four randomized controlled experiments in engineering and business MOOCs (overall N = 33, 726), we found that (1) the availability of social comparison cues significantly increases completion rates, (2) this type of feedback benefits highly educated learners, and (3) learners’ cultural context plays a significant role in their course engagement and achievement.
Presentation 8B2 (30 min):“Planning Prompts Increase and Forecast Course Completion in Massive Open Online Courses”
(Full Research Paper)
by Michael Yeomans & Justin Reich
Abstract:
Among all of the learners in Massive Open Online Courses (MOOCs) who intend to complete a course, the majority fail to do so. This intention-action gap is found in many domains of human experience, and research in similar goal pursuit domains suggests that plan-making is a cheap and effective nudge to encourage follow-through. In a natural field experiment in three HarvardX courses, some students received open-ended planning prompts at the beginning of a course. These prompts increased course completion by 29%, and payment for certificates by 40%. This effect was largest for students enrolled in traditional schools. Furthermore, the contents of students’ plans could predict which students were least likely to succeed – in particular, students whose plans focused on specific times were unlikely to complete the course. Our results suggest that planning prompts can help learners adopted productive frames of mind at the outset of a learning goal that encourage and forecast student success.
Presentation 8B3 (20 min):“From prediction to impact: Evaluation of a learning analytics retention program”
(Short Research Paper)
by Shane Dawson, Jelena Jovanovic, Dragan Gašević , Abelardo Pardo
Abstract:
Learning analytics research has often been touted as a means to address concerns regarding student retention outcomes. However, few research studies to date, have examined the impact of the implemented intervention strategies designed to address such retention challenges. Moreover, the methodological rigor of some of the existing studies has been challenged. This study evaluates the impact of a pilot retention program. The study contrasts the findings obtained by the use of different methods for analysis of the effect of the intervention. The pilot study was undertaken between 2012 and 2014 resulting in a combined enrolment of 11,160 students. A model to predict attrition was developed, drawing on data from student information system, learning management system interactions, and assessment. The predictive model identified some 1868 students as academically at-risk. Early interventions were implemented involving learning and remediation support. Common statistical methods demonstrated a positive association between the intervention and student retention. However, the effect size was low. The use of more advanced statistical methods, specifically mixed-effect methods explained higher variability in the data (over 99%), yet found the intervention had no effect on the retention outcomes. The study demonstrates that more data about individual differences is required to not only explain retention but to also develop more effective intervention approaches.

Session: LA Adoption – Experiences

Session Chair: Rebecca Ferguson
Presentation 8C1 (30 min):“Developing a Strategy for the Implementation of Learning Analytics at the University of Strathclyde”
(Practitioner Presentation)
by Ainsley Hainey, Howard Ramsay, Brian Green, Helyn Gould, Scott Walker & Michael Aherne
Abstract:
Higher education institutions urgently require strategies for the use of emergent technologies such as learning analytics. The University of Strathclyde achieved this through an exploratory piloting structure and process. A Learning Analytics Strategy Group oversaw the use of learning analytics in five diverse classes, capturing evidence of impact on learning, teaching and student success. This evidence, combined with a wide range of literature, was used to develop the institutional strategy. Components of the strategy document, in addition to the keys to success and lessons learned from this approach, will be discussed.
Presentation 8C2 (30 min):“From the trenches: factors that affected Learning Analytics success with an institution-wide implementation”
(Practitioner Presentation)
by Jennifer Heath & David Fulcher
Abstract:
The implementation process of Learning Analytics (LA) capabilities at the University of Wollongong (UOW) has been undertaken using an ambitious institution wide approach and many factors impacted success. Engaging the academic community, organization of work structures and engaging with students are considered here. ‘Top-down’ and ‘bottom-up’ approaches offered a hybrid, pragmatic change management strategy for the implementation of learning analytics capabilities for teaching staff and students. This work has been informed by technology innovations and technology enabled learning (TEL) complexes proposed in earlier international research. The success of this project relied on multi-discipline, institution wide collaboration.
Presentation 8C3 (20 min):“Strategies for Data and Learning Analytics Informed National Education Policies: the Case of Uruguay”
(Short Research Paper)
by Cecilia Aguerrebere, Cristóbal Cobo, Marcela Gomez & Matías Mateu
Abstract:
This work provides an overview of an education and technology monitoring system developed at Plan Ceibal, a nationwide initiative created to enable technology enhanced learning in Uruguay. Plan Ceibal currently offers one-to-one access to technology and connectivity to every student and teacher (from primary and secondary education) as well as a comprehensive set of educational software platforms. All these resources generate massive amounts of data about the progress and style of students learning. This work introduces the conceptual framework, design and preliminary results of the Big Data Center for learning analytics currently being developed at Plan Ceibal. This initiative is focused on exploiting these datasets and conducting advanced analytics to support the educational system. To this aim, a 360 degrees profile will be built including information characterizing the user’s online behavior as well as a set of technology enhanced learning factors. These profiles will be studied both at user (e.g., student or teacher) and larger scale levels (e.g., per school or school system), addressing both the need of understanding how technology is being used for learning as well as to provide accurate feedback to support evidence based educational policies.

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