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This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstraction summarization via multitask learning. We show that for many task combinations, a model trained in a multitask setting outperforms a model trained only for abstractive summarization, with no additional summarization data introduced. Additionally, we do a comprehensive search and find that certain tasks (e.g. paraphrase detection) consistently benefit abstractive summarization, not only when combined with other tasks but also when using different architectures and training corpora. [This paper was published in: "Findings of the Association for Computational Linguistics: EMNLP 2021," Association for Computational Linguistics, 2021, pp. 1652-1661.]

This paper explores three simple data manipulation techniques (synthesis, augmentation, curriculum) for improving abstractive summarization models without the need for any additional data. We introduce a method of data synthesis with paraphrasing, a data augmentation technique with sample mixing, and curriculum learning with two new difficulty metrics based on specificity and abstractiveness. We conduct experiments to show that these three techniques can help improve abstractive summarization across two summarization models and two different small datasets. Furthermore, we show that these techniques can improve performance when applied in isolation and when combined. [This paper was published in: "Findings of the Association for Computational Linguistics: EMNLP 2021," Association for Computational Linguistics, 2021, pp. 2043-2052.]

These proceedings contain the papers of the International Conference on Mobile Learning 2013, which was organised by the International Association for Development of the Information Society, in Lisbon, Portugal, March 14-16, 2013. The Mobile Learning 2013 International Conference seeks to provide a forum for the presentation and discussion of mobile learning research which illustrate developments in the field. Papers in these proceedings include: (1) Common Mobile Learning Characteristics--An Analysis of Mobile Learning Models and Frameworks (Umera Imtinan, Vanessa Chang and Tomayess Issa); (2) Walking towards Instrumental Appropriation of Mobile Devices. A Comparison of Studies (Maria José Hernandez Serrano and Lingling Yang); (3) Twitter Micro-Blogging Based Mobile Learning Approach to Enhance the Agriculture Education Process (Uvasara Dissanayeke, K. P. Hewagamage, Robert Ramberg, and G. N. Wikramanayake); (4) Designing Mixed Reality Mobile Games for Crisis Management Training (Ines Di Loreto, Simone Mora, and Monica Divitini); (5) From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning (Shengquan Yu, Xianmin Yang and Gang Cheng); (6) Lingobee--Crowd-Sourced Mobile Language Learning in the Cloud (Sobah Abbas Petersen, Emma Procter-Legg and Annamaria Cacchione); (7) Media Creation and Sharing in Informal, Situated, Authentic Mobile Learning for Local Cultural Diversity Investigation (Jacek Walinski); (8) Text Messaging for Out-of-Class Communication: Impact on Affective Learning (Paul Hayes, Stephan Weibelzahl and Tim Hall); (9) Prisma-Mar: An Architecture Model for Data Visualization in Augmented Reality Mobile Devices (Mauro Alexandre Folha Gomes Costa, Bianchi Serique Meiguins, Nikolas S. Carneiro and Aruanda Simões Gonçalves Meiguins); (10) What Do Context Aware Electronic Alerts from Virtual Learning Environments Tell Us About User Time & Location? (Laura Crane and Phillip Benachour); (11) Tablet Computers on Trial: A Transformative Force in Education? (Skúlína Hlíf Kjartansdóttir and Sólveig Jakobsdóttir); (12) Development and Use of an EFL Reading Practice Application for an Android Tablet Computer (Yasushige Ishikawa, Craig Smith, Mutsumi Kondo, Ichiro Akano, Kate Maher and Norihisa Wada); (13) Mobile Learning Application Interfaces: First Steps to a Cognitive Load Aware System (Robin Deegan); (14) "Mobile Phones and Other Disturbing Objects..." (Torbjörn Ott); (15) Role of Passive Capturing in a Ubiquitous Learning Environment (Hiroaki Ogata, Bin Hou, Mengmeng Li, Noriko Uosaki and Kousuke Mouri); (16) Android Based Mobile Environment for Moodle Users (Gisela T. de Clunie, Clifton Clunie, Aris Castillo and Norman Rangel); (17) A Mobile Platform for Administering Questionnaires and Synchronizing their Answers (Maria Germana Ginardi and Giordano Lanzola); (18) Future Educators' Explaining Voices (Janaina Minelli de Oliveira, Pablo Buenestado Caballero and Mar Camacho); (19) A Framework for the Creation of Mobile Educational Games for Dyslexic Children (Juan Haladjian, Daniel Richter, Paul Muntean, Damir Ismailovic and Bernd Brügge); (20) Mobile Learning 4All (Secundino Correia, Paula Medeiros, Mafalda Mendes and Margarida Silva); (21) Location-Aware Mobile Learning of Spatial Algorithms (Ville Karavirta); (22) Learning with Smartphones: Students' Lived Experience of Using Smartphones (Nee Nee Chan, Alan Walker-Gleaves and Richard Remedios); (23) Investigation of Using Analytics in Promoting Mobile Learning Support (Videhi Visali and Niraj Swami); (24) Tablet Use within Medicine (Rebecca J. Hogue); (25) Benefits and Financial Impacts of Adopting Technology in Learning (Katri Grenman, Minna Isomursu, Maija Federley and Anu Seisto); (26) m-Learning for Qur'an Memorization and Teaching its Sciences (Ahmed Sameh); (27) Learning Potentials of the Ubiquitous Internet: Using Mobile Devices to Support the Individual, Social and Physical Context of the Learner (Christian Dalsgaard, Nicholai Friis Pedersen and Janus Holst Aaen); (28) Smartphones in Clinical Nursing Practice: A Multi-Phased Approach to Implementation and Deployment (Brad Johnson, C. J. Davison and Lisa Moralejo); (29) Transmedia Storybuilding in Sloyd (Annika Wiklund-Engblom, Kasper Hiltunen, Juha Hartvik and Mia Porko-Hudd); (30) Mobile Learning in Secondary Education: Perceptions and Acceptance of Tablets of Teachers and Pupils (Hannelore Montrieux, Cédric Courtois, Frederik De Grove, Annelies Raes, Tammy Schellens and Lieven De Marez); (31) Ondigita: A Platform for the Management and Delivery of Digital Documents (Riccardo Mazza, Andrea Baldassari and Roberto Guidi); (32) From Radio, to Satellite, to mLearning: Interactive Distance Education in Australia (Stephen Crump); (33) Flipped Approach to Mobile Assisted Language Learning (Junko Yamamoto); (34) Mobile Pedagogy (Lee Schlenker); (35) Context and Concepts in Mobile Learning (Jimmy Jaldemark); (36) m-Learning Systems Design--Technology and Pedagogy Aspects (Elissaveta Gourova, Asya Asenova and Pavlin Dulev); (37) The Potential for Mobile Learning in English as a Foreign Language and Nursing Education (C. J. Davison); (38) Promoting STEM Education through Mobile Teaching and Learning (Murali Krishnamurthi and Stephanie Richter); (39) A Learning Community Explores the Potential of Mobile Apps in Higher Education (Joan Van Duzer and Kathy D. Munoz); (40) Mobile Learning Application Based on RSS Feed Technology (Mahmoud Mohanna and Laurence Capus); (41) Mobile Learning Using Mobile Phones (Paula Vicente); (42) Suitability of m-Learning to Enhance Learning English Language (J. F. Fazeena, Y. Ekanayaka and K. P. Hewagamage); (43) Integrated Authoring Tool for Mobile Augmented Reality-Based E-Learning Applications (Marcos Fermín Lobo, Víctor Manuel Álvarez García and María del Puerto Paule Ruiz); and (44) Enhancing Mobile Working Memory Training by Using Affective Feedback (Kristina Schaaff). [Individual papers contain references. An author index is included. Luís Rodrigues is an associate editor of these proceedings.]

This paper describes a preliminary study of how computational methods allow us to know more about citizen science and its connection with education. Citizen science is a practice involving a general public in scientific tasks and generating knowledge and scientific results. Previous studies have shown that the education sector can take benefit of the knowledge and activities organized or resources generated in CS projects. Previous studies have shown that the education sector can take advantage of the knowledge and activities organized in CS projects. In this paper, we analyze three citizen science platforms (Eu.Citizen science platform, Observatorio de la ciencia ciudadana and Oficina de la ciència ciutadana) with computational analytics techniques to provide initial insights of how educators can take benefit of the analysis of large amounts of data from CS. Finally, different visualizations and dashboards have been developed as illustrative examples of tools to support educators and learners. These tools provide information about citizen science projects, an overview of scientific vocabulary, access to validated resources and examples of technology used in scientific inquiry that can be used with educational purposes. [For the full proceedings, see ED621108.]

This paper summarises the contributions to EuroCALL's CorpusCALL SIG Symposium for the year 2020. In line with this year's EuroCALL conference theme, 'CALL for widening participation', the Symposium centred around the theme of "Data-driven learning for languages other than English." This paper gives a brief overview of developments and challenges when using Data-Driven Learning (DDL) to teach French, German, Italian, and Spanish. As research suggests, a DDL approach has been effectively utilised to teach these languages. However, there are differences in available DDL resources and corpora for the respective languages that are appropriate for language teaching. The main challenges for future developments are also discussed. [For the complete volume, "CALL for Widening Participation: Short Papers from EUROCALL 2020 (28th, Online, August 20-21, 2020)," see ED610330.]

Recommender systems in educational contexts have proven effective to identify learning resources that fit the interests and needs of learners. Their usage has been of special interest in online self-learning scenarios to increase student retention and improve the learning experience. In current recommendation techniques, and in particular, in collaborative filtering recommender systems, the quality of the recommendation is largely based on the explicit or implicit information obtained about the learners. On free massive online learning platforms, however, the information available about learners may be limited and based mostly on logs from website analytics tools such as Google Analytics. In this paper, we address the challenge of recommending meaningful content with limited information from users by using rating estimation strategies from a log system. Our approach posits strategies to mine logs and generates effective ratings through the counting and temporal analysis of sessions. We evaluate different rating penalty strategies and compare the use of per-user and global metrics for rating estimation. The results show that using the average number of lessons viewed per-user is better than using global metrics with a p-value under 0.01 for 4 of our 5 hypotheses, showing statistical significance. Additionally, the results show that functions that penalize the rating to a lesser degree behave better and lead to a better recommendation. [For the full proceedings, see ED623995.]

This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces: (1) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to learner engagement; (2) two standard tasks related to predicting and ranking context-agnostic engagement in video lectures with preliminary baselines; and (3) a set of experiments that validate the usefulness of the proposed dataset. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models that are significantly performant than ones based on previous datasets, mainly attributing to the increase of training examples. VLE dataset's suitability in building models towards Computer Science/ Artificial Intelligence education focused on e-learning/MOOC use-cases is also evidenced. Further experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders. This is the largest and most diverse publicly available dataset to our knowledge that deals with learner engagement prediction tasks. The dataset, helper tools, descriptive statistics and example code snippets are available publicly. [For the full proceedings, see ED623995.]

The "2nd Annual WPI-UMASS-UPENN EDM Data Mining Challenge" required contestants to predict efficient testtaking based on log data. In this paper, we describe our theory-driven and psychometric modeling approach. For feature engineering, we employed the Log-Normal Response Time Model for estimating latent person speed, and the Generalized Partial Credit Model for estimating latent person ability. Additionally, we adopted an n-gram feature approach for event sequences. For training a multi-label classifier, we distinguished inefficient test takers who were going too fast and those who were going too slow, instead of using the provided binary target label. Our best-performing ensemble classifier comprised three sets of low-dimensional classifiers, dominated by test-taker speed. While our classifier reached moderate performance, relative to competition leaderboard, our approach makes two important contributions. First, we show how explainable classifiers could provide meaningful predictions if results can be contextualized to test administrators who wish to intervene or take action. Second, our re-engineering of test scores enabled us to incorporate person ability into the estimation. However, ability was hardly predictive of efficient behavior, leading to the conclusion that the target label's validity needs to be questioned. The paper concludes with tools that are helpful for substantively meaningful log data mining. [For the full proceedings, see ED607784.]

Data mining of course enrollment and course description records has soared as institutions of higher education begin tapping into the value of these data for academic and internal research purposes. This has led to a more than doubling of papers on course prediction tasks every year. The papers often center around a single prediction task and introduce a single novel modeling approach utilizing one or two data sources. In this paper, we provide the most comprehensive evaluation to date of data sources, models, and their performance on downstream prediction tasks. We separately incorporate syllabus, catalog description, and enrollment history data to represent courses using graph embedding, course2vec (i.e., skip-gram), and classic bag-of-words models. We evaluate these representations on the tasks of predicting course prerequisites, credit equivalencies, student next semester enrollments, and student course grades. Most notably, our results show that syllabi bag-of-words representations performed better than course descriptions in predicting prerequisite relationships, though enrollment-based graph embeddings performed substantially better still. Course descriptions provided the highest single representation accuracy in predicting course similarity, with descriptions, syllabi, and course2vec combined representations providing the highest ensembled accuracy on this task. [For the full proceedings, see ED607784.]

A comparative longitudinal data analysis between two online non-thesis master's programs--natural resource management and environmental science--in a college of natural resources to determine the relationship between student characteristics and disenrollment risks. Risks varied between the two programs, with significance found to increase the risk of disenrollment due to cumulative GPA, gender, time between degrees, and the number of terms not enrolled. [For the full proceedings, see ED648717.]

Computer-mediated social learning contexts have become increasingly popular over the last few years; yet existing models of students' cognitive-affective states have been slower to adopt dyadic interaction data for predictions. Here, we explore the possibility of capitalizing on the inherently social component of collaborative learning by using keystroke log data to make predictions across conversational partners (i.e., using person A's data to make prediction about if person B is mind wandering). Log files from 33 dyads (total N = 66) were used to examine: (a) how mind wandering (defined here as task-unrelated thought) during computer-mediated conversations is related to critical outcomes of the conversation (trust, likability, agreement); (b) if task-unrelated thought can be predicted by the keystrokes of one's partner; and (c) how much data is needed to make predictions by testing various window-sizes of data preceding task-unrelated thought reports. Results indicated a negative relationship between task-unrelated thought and perceptions of the conversation, suggesting that attention is an important factor during computer mediated chat conversations. Finally, in line with our hypothesis, results from mixed effects models showed that one's level of task-unrelated thought was predicted by the keystroke patterns of their conversational partner, but only using small window sizes (5s worth of data). [For the complete proceedings, see ED630829.]

Problem decomposition into sub-problems or subgoals and recomposition of the solutions to the subgoals into one complete solution is a common strategy to reduce difficulties in structured problem solving. In this study, we use a datadriven graph-mining-based method to decompose historical student solutions of logic-proof problems into Chunks. We design a new problem type where we present these chunks in a Parsons Problem fashion and asked students to reconstruct the complete solution from the chunks. We incorporated these problems within an intelligent logic tutor and called them Chunky Parsons Problems (CPP). These problems demonstrate the process of problem decomposition to students and require them to pay attention to the decomposed solution while they reconstruct the complete solution. The aim of introducing CPP was to improve students' problem-solving skills and performance by improving their decomposition-recomposition skills without significantly increasing training difficulty. Our analysis showed that CPPs could be as easy as Worked Examples (WE). And, students who received CPP with simple explanations attached to the chunks had marginally higher scores than those who received CPPs without explanation or did not receive them. Also, the normalized learning gain of these students shifted more towards the positive side than other students. Finally, as we looked into their proof-construction traces in posttest problems, we observed them to form identifiable chunks aligned with those found in historical solutions with higher efficiency. [For the complete proceedings, see ED630829.]

Due to the precautionary measures during the COVID-19 pandemic many universities offered unproctored take-home exams. We propose methods to detect potential collusion between students and apply our approach on event log data from take-home exams during the pandemic. We find groups of students with suspiciously similar exams. In addition, we compare our findings to a proctored comparison group. By this, we establish a rule of thumb for evaluating which cases are "outstandingly similar", i.e., suspicious cases. [For the complete proceedings, see ED630829.]

Within the last decade, different educational data mining techniques, particularly quantitative methods such as clustering, and regression analysis are widely used to analyze the data from educational games. In this research, we implemented a quantitative data mining technique (clustering) to further investigate students' feedback. Students played educational games within a week on the educational games platform, Legends of Learning and after a week, we asked them to fulfill the feedback survey about their feelings on the use of this platform. To analyze the collected data from students, firstly, we prepared clusters and selected one prototype student closest to the centroid of each cluster to interview. Interviews were held to explain the clusters more and due to time and resource limitations, we were unable to interview all (N=60) students, thus only the most representative students were interviewed. In addition to the students, we conducted an interview with the teacher as well to get her detailed feedback and observations on the usage of educational games. We also asked students to take an exam before and after the research to see the impact of games on their grades. Our results depict that though educational games can increase students' motivation, they may negatively impact some students' grades. And even though playing games made students feel interested and fun, they would not like to play them on a daily basis. Hence, using educational games for a certain duration such as subject revision weeks may positively influence students' grades and motivation. [For the complete proceedings, see ED630829.]

The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for amplifying human potential." Not all students or seekers of knowledge receive the education necessary to help them realize their full potential, be it due to a lack of resources or lack of access to high quality teaching. The dearth in high-quality educational content, teaching aids, and methodologies, and non-availability of objective feedback on how they could become better teachers, deprive our teachers from achieving their full potential. The administrators and policy makers lack tools for making optimal decisions such as optimal class sizes, class composition, and course sequencing. All these handicap the nations, particularly the economically emergent ones, who recognize the centrality of education for their growth. EDM-2023 has striven to focus on concepts, principles, and techniques mined from educational data for amplifying the potential of all the stakeholders in the education system. The spotlights of EDM-2023 include: (1) Five keynote talks by outstanding researchers of eminence; (2) A plenary Test of Time award talk and a Banquet talk; (3) Five tutorials (foundational as well as advanced); (4) Four thought provoking panels on contemporary themes; (5) Peer reviewed technical paper and poster presentations; (6) Doctoral students consortium; and (7) An enchanting cultural programme. [Individual papers are indexed in ERIC.]

Assessing students' performance in online learning could be executed not only by the traditional forms of summative assessments such as using essays, assignments, and a final exam, etc. but also by more formative assessment approaches such as interaction activities, forum posts, etc. However, it is difficult for teachers to monitor and assess students' learning activities using the log data. To provide teachers with a more comprehensive view of students' distinct learning behaviour patterns, and to supply personalized interventions and support to meet the specific needs of each learning group, this study focuses on how to automatically acquire learning logs from Manaba, a Japanese commercial LMS, and how to cluster students' learning activities using the k-means algorithm. Firstly, we developed a program using Python to scrape students' learning activity log information from the Manaba web pages. We collected 56446 lines of clickstreams log data from 121 students in two computer literacy hybrid classes in the fall semester of 2022 (2022/9-2023/1). Secondly, we convert the raw logs into a structured dataset with 33 features which represent each student's learning activities. Then we extract and select 15 features representing three perspectives: raw activity, time on task, and learning frequency. Thirdly, we grouped students' learning activity patterns with the three perspectives into 5 clusters by the k-means clustering algorithm. As a result, this study identified five distinct learning activity patterns depending on how much, how long and how often the students learned online. For example, cluster 1 seldom learned but spent time on learning whom we considered the disengaged or struggling students, and cluster 5 had more learning activities with little time on each activity whom we considered the well-self-regulated students. The results of this study contribute to how to monitor students' learning activity in online learning and how to assess and support student's learning by their learning activity patterns. [For the full proceedings, see ED636095.]

Geography is one of the defining factors in scientific collaboration. Despite the voluminous evidence for how geographical proximity shapes the formation of collaboration in research articles, it has been rarely examined in the emerging genre of data papers, one that describes research data and has enjoyed growing attention in the data‐driven paradigm of research. This poster presents preliminary findings from our project that aims to evaluate the geographical dynamics behind the production of data papers. We analyze how researchers from different countries collaborate with one another using 6,821 data papers published in Scientific Data and Data in Brief between 2014 and 2020. We found that data papers rely heavily upon domestic collaboration and the collaboration pattern largely mirrors that of research articles, although some distinctiveness was also observed. We discuss future work in conclusion, with the ultimate goal of opening a more meaningful conversation about the relationship between the data‐driven paradigm and knowledge production. [ABSTRACT FROM AUTHOR]

The bibliographic science papers with high citations rates are often used as an indication of the science impact of an observatory. These high impact papers are presented as examples of the best science being done with an observatory's data. But, is the number of citations by itself a good indicator of the scientific impact of the paper, and is impact a good indicator of the scientific impact of the observatory? In this paper we will present results from a recent study of Chandra high impact papers and suggest some alternative methods for identifying such papers. This work has been supported by NASA under contract NAS 8-03060 to the Smithsonian Astrophysical Observatory for operation of the Chandra X-ray Center. [ABSTRACT FROM AUTHOR]