Proceedings of the 9th International Conference on Learning Analytics & Knowledge最新文献

{"title":"Are You Talking to Me?: Multi-Dimensional Language Analysis of Explanations during Reading","authors":"L. Allen, Caitlin Mills, Cecile A. Perret, D. McNamara","doi":"10.1145/3303772.3303835","DOIUrl":"https://doi.org/10.1145/3303772.3303835","url":null,"abstract":"This study examines the extent to which instructions to self-explain vs. other-explain a text lead readers to produce different forms of explanations. Natural language processing was used to examine the content and characteristics of the explanations produced as a function of instruction condition. Undergraduate students (n = 146) typed either self-explanations or other-explanations while reading a science text. The linguistic properties of these explanations were calculated using three automated text analysis tools. Machine learning classifiers in combination with the features were used to predict instruction condition (i.e., self- or other-explanation). The best machine learning model performed at rates above chance (kappa = .247; accuracy = 63%). Follow-up analyses indicated that students in the self-explanation condition generated explanations that were more cohesive and that contained words that were more related to social order (e.g., ethics). Overall, the results suggest that natural language processing techniques can be used to detect subtle differences in students' processing of complex texts.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"265 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123478528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliable Deep Grade Prediction with Uncertainty Estimation","authors":"Qian Hu, H. Rangwala","doi":"10.1145/3303772.3303802","DOIUrl":"https://doi.org/10.1145/3303772.3303802","url":null,"abstract":"Currently, college-going students are taking longer to graduate than their parental generations. Further, in the United States, the six-year graduation rate has been 59% for decades. Improving the educational quality by training better-prepared students who can successfully graduate in a timely manner is critical. Accurately predicting students' grades in future courses has attracted much attention as it can help identify at-risk students early so that personalized feedback can be provided to them on time by advisors. Prior research on students' grade prediction include shallow linear models; however, students' learning is a highly complex process that involves the accumulation of knowledge across a sequence of courses that can not be sufficiently modeled by these linear models. In addition to that, prior approaches focus on prediction accuracy without considering prediction uncertainty, which is essential for advising and decision making. In this work, we present two types of Bayesian deep learning models for grade prediction under a course-specific framework: i)Multilayer Perceptron (MLP) and ii) Recurrent Neural Network (RNN). These course-specific models are based on the assumption that prior courses can provide students with knowledge for future courses so that grades of prior courses can be used to predict grades in a future course. The MLP ignores the temporal dynamics of students' knowledge evolution. Hence, we propose RNN for students' performance prediction. To evaluate the performance of the proposed models, we performed extensive experiments on data collected from a large public university. The experimental results show that the proposed models achieve better performance than prior state-of-the-art approaches. Besides more accurate results, Bayesian deep learning models estimate uncertainty associated with the predictions. We explore how uncertainty estimation can be applied towards developing a reliable educational early warning system. In addition to uncertainty, we also develop an approach to explain the prediction results, which is useful for advisors to provide personalized feedback to students.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114553238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Goal-based Course Recommendation","authors":"Weijie Jiang, Z. Pardos, Q. Wei","doi":"10.1145/3303772.3303814","DOIUrl":"https://doi.org/10.1145/3303772.3303814","url":null,"abstract":"With cross-disciplinary academic interests increasing and academic advising resources over capacity, the importance of exploring data-assisted methods to support student decision making has never been higher. We build on the findings and methodologies of a quickly developing literature around prediction and recommendation in higher education and develop a novel recurrent neural network-based recommendation system for suggesting courses to help students prepare for target courses of interest, personalized to their estimated prior knowledge background and zone of proximal development. We validate the model using tests of grade prediction and the ability to recover prerequisite relationships articulated by the university. In the third validation, we run the fully personalized recommendation for students the semester before taking a historically difficult course and observe differential overlap with our would-be suggestions. While not proof of causal effectiveness, these three evaluation perspectives on the performance of the goal-based model build confidence and bring us one step closer to deployment of this personalized course preparation affordance in the wild.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130070406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Value-Sensitive Learning Analytics Design","authors":"Bodong Chen, Haiyi Zhu","doi":"10.1145/3303772.3303798","DOIUrl":"https://doi.org/10.1145/3303772.3303798","url":null,"abstract":"To support ethical considerations and system integrity in learning analytics, this paper introduces two cases of applying the Value Sensitive Design methodology to learning analytics design. The first study applied two methods of Value Sensitive Design, namely stakeholder analysis and value analysis, to a conceptual investigation of an existing learning analytics tool. This investigation uncovered a number of values and value tensions, leading to design trade-offs to be considered in future tool refinements. The second study holistically applied Value Sensitive Design to the design of a recommendation system for the Wikipedia WikiProjects. To proactively consider values among stakeholders, we derived a multi-stage design process that included literature analysis, empirical investigations, prototype development, community engagement, iterative testing and refinement, and continuous evaluation. By reporting on these two cases, this paper responds to a need of practical means to support ethical considerations and human values in learning analytics systems. These two cases demonstrate that Value Sensitive Design could be a viable approach for balancing a wide range of human values, which tend to encompass and surpass ethical issues, in learning analytics design.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124889539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Detailed Access Trajectories for Learning Behavior Analysis","authors":"Yanbang Wang, N. Law, Erik Hemberg, Una-May O’Reilly","doi":"10.1145/3303772.3303781","DOIUrl":"https://doi.org/10.1145/3303772.3303781","url":null,"abstract":"Student learning activity in MOOCs can be viewed from multiple perspectives. We present a new organization of MOOC learner activity data at a resolution that is in between the fine granularity of the clickstream and coarse organizations that count activities, aggregate students or use long duration time units. A detailed access trajectory (DAT) consists of binary values and is two dimensional with one axis that is a time series, and the other that is a chronologically ordered list of a MOOC component type's instances, videos in instructional order, for example. Most popular MOOC platforms generate data that can be organized as detailed access trajectories (DATs). We explore the value of DATs by conducting four empirical mini-studies. Our studies suggest DATs contain rich information about students' learning behaviors and facilitate MOOC learning analyses.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127250849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses","authors":"Mucong Ding, Kai Yang, D. Yeung, T. Pong","doi":"10.1145/3303772.3303795","DOIUrl":"https://doi.org/10.1145/3303772.3303795","url":null,"abstract":"The effectiveness of learning in massive open online courses (MOOCs) can be significantly enhanced by introducing personalized intervention schemes which rely on building predictive models of student learning behaviors such as some engagement or performance indicators. A major challenge that has to be addressed when building such models is to design handcrafted features that are effective for the prediction task at hand. In this paper, we make the first attempt to solve the feature learning problem by taking the unsupervised learning approach to learn a compact representation of the raw features with a large degree of redundancy. Specifically, in order to capture the underlying learning patterns in the content domain and the temporal nature of the clickstream data, we train a modified auto-encoder (AE) combined with the long short-term memory (LSTM) network to obtain a fixed-length embedding for each input sequence. When compared with the original features, the new features that correspond to the embedding obtained by the modified LSTM-AE are not only more parsimonious but also more discriminative for our prediction task. Using simple supervised learning models, the learned features can improve the prediction accuracy by up to 17% compared with the supervised neural networks and reduce overfitting to the dominant low-performing group of students, specifically in the task of predicting students' performance. Our approach is generic in the sense that it is not restricted to a specific supervised learning model nor a specific prediction task for MOOC learning analytics.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116542530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer Learning using Representation Learning in Massive Open Online Courses","authors":"Mucong Ding, Yanbang Wang, Erik Hemberg, Una-May O’Reilly","doi":"10.1145/3303772.3303794","DOIUrl":"https://doi.org/10.1145/3303772.3303794","url":null,"abstract":"In a Massive Open Online Course (MOOC), predictive models of student behavior can support multiple aspects of learning, including instructor feedback and timely intervention. Ongoing courses, when the student outcomes are yet unknown, must rely on models trained from the historical data of previously offered courses. It is possible to transfer models, but they often have poor prediction performance. One reason is features that inadequately represent predictive attributes common to both courses. We present an automated transductive transfer learning approach that addresses this issue. It relies on problem-agnostic, temporal organization of the MOOC clickstream data, where, for each student, for multiple courses, a set of specific MOOC event types is expressed for each time unit. It consists of two alternative transfer methods based on representation learning with auto-encoders: a passive approach using transductive principal component analysis and an active approach that uses a correlation alignment loss term. With these methods, we investigate the transferability of dropout prediction across similar and dissimilar MOOCs and compare with known methods. Results show improved model transferability and suggest that the methods are capable of automatically learning a feature representation that expresses common predictive characteristics of MOOCs.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123235227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments","authors":"T. NeCamp, Josh Gardner, Christopher A. Brooks","doi":"10.1145/3303772.3303812","DOIUrl":"https://doi.org/10.1145/3303772.3303812","url":null,"abstract":"Randomized experiments ensure robust causal inference that is critical to effective learning analytics research and practice. However, traditional randomized experiments, like A/B tests, are limiting in large scale digital learning environments. While traditional experiments can accurately compare two treatment options, they are less able to inform how to adapt interventions to continually meet learners' diverse needs. In this work, we introduce a trial design for developing adaptive interventions in scaled digital learning environments -- the sequential randomized trial (SRT). With the goal of improving learner experience and developing interventions that benefit all learners at all times, SRTs inform how to sequence, time, and personalize interventions. In this paper, we provide an overview of SRTs, and we illustrate the advantages they hold compared to traditional experiments. We describe a novel SRT run in a large scale data science MOOC. The trial results contextualize how learner engagement can be addressed through culturally-targeted reminder emails. We also provide practical advice for researchers who aim to run their own SRTs to develop adaptive interventions in scaled digital learning environments.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128927538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","authors":"D. Gašević, G. Lynch, S. Dawson, H. Drachsler, C. Rosé","doi":"10.1145/3303772","DOIUrl":"https://doi.org/10.1145/3303772","url":null,"abstract":"We are pleased to welcome you to the 6th International Conference on Learning Analytics and Knowledge (LAK16). This year's conference is held in the beautiful city of Edinburgh, Scotland, April 25-29. For the first time, the international Learning Analytics and Knowledge conference is co-located with ACM Learning @ Scale 2016. The LAK16 conference is organized by the Society for Learning Analytics Research (SoLAR), and this year is hosted by the University of Edinburgh, a university with a long and rich history of innovation and research in teaching, learning and technologies. Building on the momentum generated from previous LAK conferences, we have extend invitations to practitioners, researchers, administrators, government and industry groups alike, interested in the field of learning analytics and related disciplines. This annual conference provides a multidisciplinary forum for addressing the critical issues and challenges confronting the education sector today. A particular emphasis of this year's program is enhancing our impact through synergistic connections with other related research communities. \u0000 \u0000The field of learning analytics is rapidly growing in all facets of its research, application into practice and theoretical contributions. The theme for the 6th International Learning Analytics and Knowledge (LAK16) conference aims to explore the multidisciplinary connections that effectively illustrate how learning analytics can provide critical insights into the individual and collective learning process. This year's theme particularly highlights the multidisciplinary nature of the field and embraces the convergence of these disciplines to provide theoretical and practical insights that will further advance the field - through research, adoption and implementation and ultimately provide a foundation for informing government and institutional policy. We invite research and practice papers that address the \"convergence of communities\" in LAK and bring a novel perspective and approach for reflecting on the field. This theme is reflected in the workshops, papers, posters, panels, and especially our keynote talks. The conference will culminate with a leadership panel featuring leaders from a spectrum of research societies dedicated to advancing technology in service of education.","PeriodicalId":382957,"journal":{"name":"Proceedings of the 9th International Conference on Learning Analytics & Knowledge","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114787765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}