diff --git a/hudk4050-references.csv b/hudk4050-references.csv new file mode 100644 index 0000000..1f234c5 --- /dev/null +++ b/hudk4050-references.csv @@ -0,0 +1,34 @@ +"Key","Item Type","Publication Year","Author","Title","Publication Title","ISBN","ISSN","DOI","Url","Abstract Note","Date","Date Added","Date Modified","Access Date","Pages","Num Pages","Issue","Volume","Number Of Volumes","Journal Abbreviation","Short Title","Series","Series Number","Series Text","Series Title","Publisher","Place","Language","Rights","Type","Archive","Archive Location","Library Catalog","Call Number","Extra","Notes","File Attachments","Link Attachments","Manual Tags","Automatic Tags","Editor","Series Editor","Translator","Contributor","Attorney Agent","Book Author","Cast Member","Commenter","Composer","Cosponsor","Counsel","Interviewer","Producer","Recipient","Reviewed Author","Scriptwriter","Words By","Guest","Number","Edition","Running Time","Scale","Medium","Artwork Size","Filing Date","Application Number","Assignee","Issuing Authority","Country","Meeting Name","Conference Name","Court","References","Reporter","Legal Status","Priority Numbers","Programming Language","Version","System","Code","Code Number","Section","Session","Committee","History","Legislative Body" +"I6X3DB52","bookSection","2017","Bergner, Yoav","Measurement and its Uses in Learning Analytics","The Handbook of Learning Analytics","978-0-9952408-0-3","","","http://solaresearch.org/hla-17/hla17-chapter1","Psychological measurement is a process for making warranted claims about states of mind. As such, it typically comprises the following: de ning a construct; specifying a measurement model and (developing) a reliable instrument; analyzing and accounting for various sources of error (including operator error); and framing a valid argument for particular uses of the outcome. Measurement of latent variables is, after all, a noisy endeavor that can neverthe- less have high-stakes consequences for individuals and groups. This chapter is intended to serve as an introduction to educational and psychological measurement for practitioners in learning analytics and educational data mining. It is organized thematically rather than historically, from more conceptual material about constructs, instruments, and sources of measurement error toward increasing technical detail about particular measurement models and their uses. Some of the philosophical differences between explanatory and predictive modelling are explored toward the end.","2017","2019-11-19 22:12:56","2019-11-19 22:12:56","","34-48","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"6W49FSE3","bookSection","2017","Prinsloo, Paul; Slade, Sharon","Ethics and Learning Analytics: Charting the (Un)Charted","The Handbook of Learning Analytics","978-0-9952408-0-3","","","http://solaresearch.org/hla-17/hla17-chapter1","As the field of learning analytics matures, and discourses surrounding the scope, de nition, challenges, and opportunities of learning analytics become more nuanced, there is bene t both in reviewing how far we have come in considering associated ethical issues and in looking ahead. This chapter provides an overview of how our own thinking has developed and maps our journey against broader developments in the eld. Against a backdrop of technological advances and increasing concerns around pervasive surveillance and the role and unintended consequences of algorithms, the development of research in learning analytics as an ethical and moral practice provides a rich picture of fears and realities. More importantly, we begin to see ethics and privacy as crucial enablers within learning analytics. The chapter brie y locates ethics in learning analytics in the broader context of the forces shaping higher education and the roles of data and evidence before tracking our personal research journey, highlighting current work in the eld, and concluding by mapping future issues for consideration.","2017","2019-11-19 22:12:56","2019-11-19 22:12:56","","49-57","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"I2ZGYI2D","bookSection","2017","Brooks, Christopher; Thompson, Craig","Predictive Modelling in Teaching and Learning","The Handbook of Learning Analytics","978-0-9952408-0-3","","","http://solaresearch.org/hla-17/hla17-chapter1","This article describes the process, practice, and challenges of using predictive modelling in teaching and learning. In both the elds of educational data mining (EDM) and learning analytics (LA) predictive modelling has become a core practice of researchers, largely with a focus on predicting student success as operationalized by academic achievement. In this chapter, we provide a general overview of considerations when using predictive modelling, the steps that an educational data scientist must consider when engaging in the process, and a brief overview of the most popular techniques in the eld.","2017","2019-11-19 22:12:56","2019-11-19 22:12:56","","61-68","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"HEE57PTJ","bookSection","2017","Liu, Ren; Koedinger, Kenneth","Going Beyond Better Data Prediction to Create Explanatory Models of Educational Data","The Handbook of Learning Analytics","978-0-9952408-0-3","","","http://solaresearch.org/hla-17/hla17-chapter1","In the statistical modelling of educational data, approaches vary depending on whether the goal is to build a predictive or an explanatory model. Predictive models aim to nd a combination of features that best predict outcomes; they are typically assessed by their accuracy in predicting held-out data. Explanatory models seek to identify interpretable causal relationships between constructs that can be either observed or inferred from the data. The vast majority of educational data mining research has focused on achieving pre- dictive accuracy, but we argue that the eld could bene t from more focus on developing explanatory models. We review examples of educational data mining efforts that have pro- duced explanatory models and led to improvements to learning outcomes and/or learning theory. We also summarize some of the common characteristics of explanatory models, such as having parameters that map to interpretable constructs, having fewer parameters overall, and involving human input early in the model development process.","2017","2019-11-19 22:12:56","2019-11-19 22:12:56","","69-76","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"F89PPDKY","journalArticle","2011","Gelman, A; Niemi, J","Statistical graphics: making information clear – and beautiful","Significance","","","","","","2011-09","2019-11-19 22:12:56","2019-11-19 22:12:56","","134-136","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"D5QUJPZG","journalArticle","2012","Gelman, A; Unwin, A","Infovis and Statistical Graphics: Different Goals, Different Looks (with discussion)","","","","","","","2012","2019-11-19 22:12:56","2019-11-19 22:12:56","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"E2TZ7ITS","blogPost","2014","Fung, K","Junkcharts Trifecta Checkup: The Definitive Guide","Junkcharts","","","","http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html","","2014","2019-11-19 22:12:57","2019-11-19 22:12:57","","","","","","","","","","","","","","","","","Blog","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"7PNHBTMG","journalArticle","2010","Bowers, Alex J.","Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students: Grades, Data Driven Decision Making, Dropping out and Hierarchical Cluster Analysis","Practical Assessment, Research & Evaluation","","1531-7714","","","School personnel currently lack an effective method to pattern and visually interpret disaggregated achievement data collected on students as a means to help inform decision making. This study, through the examination of longitudinal K-12 teacher assigned grading histories for entire cohorts of students from a school district (n=188), demonstrates a novel application of hierarchical cluster analysis and pattern visualization in which all data points collected on every student in a cohort can be patterned, visualized and interpreted to aid in data driven decision making by teachers and administrators. Additionally, as a proof-of-concept study, overall schooling outcomes, such as student dropout or taking a college entrance exam, are identified from the data patterns and compared to past methods of dropout identification as one example of the usefulness of the method. Hierarchical cluster analysis correctly identified over 80% of the students who dropped out using the entire student grade history patterns from either K-12 or K-8. (Contains 5 figures.)","2010-05","2019-11-19 22:12:57","2019-11-19 22:12:57","2014-09-24 19:31:29","","","7","15","","","Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students","","","","","","","en","","","","","ERIC","","","","","http://eric.ed.gov/?id=EJ933686","","data; data analysis; Decision Making; Dropouts; Elementary School Students; Grades (Scholastic); Identification; MULTIVARIATE analysis; School Districts; Secondary School Students","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"9EZ4NMRQ","journalArticle","2014","Grunspan, Daniel Z.; Wiggins, Benjamin L.; Goodreau, Steven M.","Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research","CBE-Life Sciences Education","",", 1931-7913","10.1187/cbe.13-08-0162","http://www.lifescied.org/content/13/2/167","Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA) provides the necessary tool kit for investigating questions involving relational data. We introduce basic concepts in SNA, along with methods for data collection, data processing, and data analysis, using a previously collected example study on an undergraduate biology classroom as a tutorial. We conduct descriptive analyses of the structure of the network of costudying relationships. We explore generative processes that create observed study networks between students and also test for an association between network position and success on exams. We also cover practical issues, such as the unique aspects of human subjects review for network studies. Our aims are to convince readers that using SNA in classroom environments allows rich and informative analyses to take place and to provide some initial tools for doing so, in the process inspiring future educational studies incorporating relational data.","2014-06-20","2019-11-19 22:12:57","2019-11-19 22:12:57","2014-08-20 20:21:46","167-178","","2","13","","CBE Life Sci Educ","Understanding Classrooms through Social Network Analysis","","","","","","","en","","","","","www.lifescied.org","","","","","http://www.lifescied.org/content/13/2/167","Week 2","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"3HXTR255","blogPost","2014","Young, Jeffrey R.","Why Students Should Own Their Educational Data","The Chronicle of Higher Education Blogs: Wired Campus","","","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","2014-08-21","2019-11-19 22:12:57","2019-11-19 22:12:57","2014-08-23 21:32:22","","","","","","","","","","","","","","","","","","","","","","","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"N9QF4RL8","journalArticle","1994","Corbett, Albert T.; Anderson, John R.","Knowledge tracing: Modeling the acquisition of procedural knowledge","User Modeling and User-Adapted Interaction","","0924-1868, 1573-1391","10.1007/BF01099821","http://link.springer.com.ezp-prod1.hul.harvard.edu/article/10.1007/BF01099821","This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called theideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process calledknowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has ‘mastered’ each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.","1994-12-01","2019-11-19 22:12:57","2019-11-19 22:12:57","2013-04-21 21:21:19","253-278","","4","4","","User Model User-Adap Inter","Knowledge tracing","","","","","","","en","","","","","link.springer.com.ezp-prod1.hul.harvard.edu","","","","","","","Education (general); empirical validity; individual differences; intelligent tutoring systems; Learning; Management of Computing and Information Systems; mastery learning; Multimedia Information Systems; procedural knowledge; Psychology, general; student modeling; User Interfaces and Human Computer Interaction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"2KJ95BMM","conferencePaper","2012","Siemens, George; Baker, Ryan S. J. d.","Learning Analytics and Educational Data Mining: Towards Communication and Collaboration","Proceedings of the 2Nd International Conference on Learning Analytics and Knowledge","978-1-4503-1111-3","","10.1145/2330601.2330661","http://doi.acm.org/10.1145/2330601.2330661","Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.","2012","2019-11-19 22:12:58","2019-11-19 22:12:58","2015-01-16 03:15:55","252–254","","","","","","Learning Analytics and Educational Data Mining","LAK '12","","","","ACM","New York, NY, USA","","","","","","ACM Digital Library","","","","","","","Collaboration; educational data mining; learning analytics and knowledge","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"IMBRTNFV","book","2015","Zheng, Alice","Evaluating Machine Learning Models","","","","","http://www.oreilly.com/data/free/evaluating-machine-learning-models.csp?intcmp=il-data-free-lp-lgen_free_reports_page","Data science today is a lot like the Wild West: there’s endless opportunity and excitement, but also a lot of chaos and confusion. If you’re new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming...","2015-09","2019-11-19 22:12:58","2019-11-19 22:12:58","2015-12-15 18:26:39","","","","","","","","","","","","O'Reily Media","Sebastopol, CA","","","","","","","","","","","http://www.oreilly.com/data/free/evaluating-machine-learning-models.csp?intcmp=il-data-free-lp-lgen_free_reports_page","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"GC6L596A","blogPost","2015","Leong, B; Polonetsky, J","Why Opting Out of Student Data Collection Isn’t the Solution","EdSurge","","","","https://www.edsurge.com/news/2015-03-16-why-opting-out-of-student-data-collection-isn-t-the-solution","In every privacy debate across every industry, the same questions arise about the rights of individuals to “opt-out” of their data being collected or used. So it should come as no surprise that the “when” and “how” of parent and student opt-outs of education data collection or use has become a robust","2015-03-16","2019-11-19 22:12:58","2019-11-19 22:12:58","2016-01-16 16:31:25","","","","","","","","","","","","","","","","","","","","","","","","https://www.edsurge.com/news/2015-03-16-why-opting-out-of-student-data-collection-isn-t-the-solution","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"N5DGMSSG","videoRecording","2015","Educause","Why Is Measuring Learning So Difficult?","","","","","https://www.youtube.com/watch?v=_iv8A1pHNYA","Several higher education learning and assessment professionals discuss the difficulties of measuring learning.","2015-08-17","2019-11-19 22:12:59","2019-11-19 22:12:59","2016-01-17 18:50:57","","","","","","","","","","","","","","","","","","","YouTube","","","","","","","Assessment; Education; educational assessment; EDUCAUSE; Higher Education; learners; Learning; Teaching and learning","","","","","","","","","","","","","","","","","","","","","470 seconds","","","","","","","","","","","","","","","","","","","","","","","","","" +"ZTB8X3U7","webpage","2016","Weinersmith, Zach","Saturday Morning Breakfast Cereal","","","","","http://www.smbc-comics.com/index.php?id=3978","","2016-01-05","2019-11-19 22:12:59","2019-11-19 22:12:59","2016-01-18 18:17:09","","","","","","","","","","","","","","","","","","","","","","","","http://www.smbc-comics.com/index.php?id=3978","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"UU6HW7PL","conferencePaper","2014","Clow, Doug","Data wranglers: human interpreters to help close the feedback loop","Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","","","","","","2014","2019-11-19 22:12:59","2019-11-19 22:12:59","","49–53","","","","","","","","","","","ACM","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"WEN336HG","magazineArticle","2015","Kucirkova, Natalia; FitzGerald, Elizabeth","Zuckerberg is ploughing billions into 'personalised learning' – why?","The Conversation","","","","http://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940","Zuckerburg wants to plough billions into personalised learning, but his way may not be the right way.","2015-12-09","2019-11-19 22:12:59","2019-11-19 22:12:59","2016-01-18 19:14:05","","","","","","","","","","","","","","","","","","","","","","","","https://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"5LS7DG36","videoRecording","2015","Georgia Tech","Feature Selection","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","2015-02-23","2019-11-19 22:12:59","2019-11-19 22:12:59","2016-01-18 19:18:06","","","","","","","","","","","","Youtube","","","","","","","","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","","","","","","","","","","","","","","","","","","","","","","3:13","","","","","","","","","","","","","","","","","","","","","","","","","" +"NTKYEBE3","bookSection","2016","Hanneman, R.A.; Riddle, M.","Chapter 1: Social Network Data","Introduction to Social Network Methods","","","","http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html","","2016-01-18","2019-11-19 22:13:00","2019-11-19 22:13:00","2016-01-18 20:17:24","","","","","","","","","","","","","","","","","","","","","","","","http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"WHZXHC7B","webpage","2014","Groelmund, Garrett","RStudio Cheat Sheets","RStudio","","","","https://www.rstudio.com/resources/cheatsheets/","","2014-08-01","2019-11-19 22:13:00","2019-11-19 22:13:00","2016-01-19 21:17:28","","","","","","","","","","","","","","","","","","","","","","","","http://shiny.rstudio.com/articles/rm-cheatsheet.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"QFHBVD78","conferencePaper","2013","san Pedro, Maria Ofelia; Baker, Ryan; Bowers, Alex; Heffernan, Neil","Predicting college enrollment from student interaction with an intelligent tutoring system in middle school","Educational Data Mining 2013","","","","","","2013","2019-11-19 22:13:00","2019-11-19 22:13:00","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"7GPF2FHC","journalArticle","2012","Greller, Wolfgang; Drachsler, Hendrik","Translating Learning into Numbers: A Generic Framework for Learning Analytics","Journal of Educational Technology & Society","","1176-3647","","http://www.jstor.org/stable/jeductechsoci.15.3.42","ABSTRACT With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning and Knowledge Analytics as well as the consequences for learning and teaching are still far from being understood. In this paper, we explore the key dimensions of Learning Analytics (LA), the critical problem zones, and some potential dangers to the beneficial exploitation of educational data. We propose and discuss a generic design framework that can act as a useful guide for setting up Learning Analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. Furthermore, the presented article intends to inform about soft barriers and limitations of Learning Analytics. We identify the required skills and competences that make meaningful use of Learning Analytics data possible to overcome gaps in interpretation literacy among educational stakeholders. We also discuss privacy and ethical issues and suggest ways in which these issues can be addressed through policy guidelines and best practice examples.","2012","2019-11-19 22:13:00","2019-11-19 22:13:00","2016-09-03 18:55:41","42-57","","3","15","","Journal of Educational Technology & Society","Translating Learning into Numbers","","","","","","","","","","","","JSTOR","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"GBM5ETB3","bookSection","2006","Kay, Judy; Maisonneuve, Nicolas; Yacef, Kalina; Reimann, Peter","The Big Five and Visualisations of Team Work Activity","Intelligent Tutoring Systems","978-3-540-35159-7 978-3-540-35160-3","","","http://link.springer.com/chapter/10.1007/11774303_20","We have created a set of novel visualisations of group activity: they mirror activity of individuals and their interactions, based upon readily available authentic data. We evaluated these visualisations in the context of a semester long software development project course. We give a theoretical analysis of the design of our visualizations using the framework from the “Big 5” theory of team work as well as a qualitative study of the visualisations and the students’ reflective reports. We conclude that these visualisations provide a powerful and valuable mirroring role with potential, when well used, to help groups learn to improve their effectiveness.","2006-06-26","2019-11-19 22:13:00","2019-11-19 22:13:00","2016-09-03 19:10:12","197-206","","","","","","","Lecture Notes in Computer Science","4053","","","Springer Berlin Heidelberg","","en","©2006 Springer-Verlag Berlin Heidelberg","","","","link.springer.com","","DOI: 10.1007/11774303_20","","","https://link.springer.com/chapter/10.1007/11774303_20","","Artificial Intelligence (incl. Robotics); Computers and Education; Information Systems Applications (incl. Internet); Multimedia Information Systems; User Interfaces and Human Computer Interaction","Ikeda, Mitsuru; Ashley, Kevin D.; Chan, Tak-Wai","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"Q5PMIAAT","journalArticle","2015","Konstan, Joseph A.; Walker, J. D.; Brooks, D. Christopher; Brown, Keith; Ekstrand, Michael D.","Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC","ACM Trans. Comput.-Hum. Interact.","","1073-0516","10.1145/2728171","http://doi.acm.org/10.1145/2728171","","2015-04","2019-11-19 22:13:01","2019-11-19 22:13:01","2016-09-03 20:38:02","10:1–10:23","","2","22","","","Teaching Recommender Systems at Large Scale","","","","","","","","","","","","ACM Digital Library","","","","","","","learning assessment; Massively Online Open Course (MOOC)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"4M7C67ZQ","bookSection","2013","Desmarais, Michel C.; Naceur, Rhouma","A Matrix Factorization Method for Mapping Items to Skills and for Enhancing Expert-Based Q-Matrices","Artificial Intelligence in Education","978-3-642-39111-8 978-3-642-39112-5","","","http://link.springer.com/chapter/10.1007/978-3-642-39112-5_45","Uncovering the right skills behind question items is a difficult task. It requires a thorough understanding of the subject matter and of the cognitive factors that determine student performance. The skills definition, and the mapping of item to skills, require the involvement of experts. We investigate means to assist experts for this task by using a data driven, matrix factorization approach. The two mappings of items to skills, the expert on one side and the matrix factorization on the other, are compared in terms of discrepancies, and in terms of their performance when used in a linear model of skills assessment and item outcome prediction. Visual analysis shows a relatively similar pattern between the expert and the factorized mappings, although differences arise. The prediction comparison shows the factorization approach performs slightly better than the original expert Q-matrix, giving supporting evidence to the belief that the factorization mapping is valid. Implications for the use of the factorization to design better item to skills mapping are discussed.","2013-07-09","2019-11-19 22:13:01","2019-11-19 22:13:01","2016-09-03 20:44:11","441-450","","","","","","","Lecture Notes in Computer Science","7926","","","Springer Berlin Heidelberg","","en","©2013 Springer-Verlag Berlin Heidelberg","","","","link.springer.com","","DOI: 10.1007/978-3-642-39112-5_45","

 

 

The two mappings of items to skills, the expert on one side and the matrix factorization on the other, are compared in terms of discrepancies, and in terms of their performance when used in a linear model of skills assessment and item outcome prediction.

","","https://link.springer.com/chapter/10.1007/978-3-642-39112-5_45","","alternating least squares matrix factorization; Artificial Intelligence (incl. Robotics); Cognitive modeling; Computers and Education; Educational Technology; Information Systems Applications (incl. Internet); latent skills; Pedagogic Psychology; skills assessment; Student models; User Interfaces and Human Computer Interaction","Lane, H. Chad; Yacef, Kalina; Mostow, Jack; Pavlik, Philip","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"YIJ9DHR3","book","2015","Matsuda, Noboru; Furukawa, Tadanobu; Bier, Norman; Faloutsos, Christos","Machine Beats Experts: Automatic Discovery of Skill Models for Data-Driven Online Course Refinement","","","","","http://eric.ed.gov/?id=ED560513","How can we automatically determine which skills must be mastered for the successful completion of an online course? Large-scale online courses (e.g., MOOCs) often contain a broad range of contents frequently intended to be a semester's worth of materials; this breadth often makes it difficult to articulate an accurate set of skills and knowledge (i.e., a skill model, or the QMatrix). We have developed an innovative method to discover skill models from the data of online courses. Our method assumes that online courses have a pre-defined skill map for which skills are associated with formative assessment items embedded throughout the online course. Our method carefully exploits correlations between various parts of student performance, as well as in the text of assessment items, to build a superior statistical model that even outperforms human experts. To evaluate our method, we compare our method with existing methods (LFA) and human engineered skill models on three Open Learning Initiative (OLI) courses at Carnegie Mellon University. The results show that (1) our method outperforms human-engineered skill models, (2) skill models discovered by our method are interpretable, and (3) our method is remarkably faster than existing methods. These results suggest that our method provides a significant contribution to the evidence-based, iterative refinement of online courses with a promising scalability. [For complete proceedings, see ED560503.]","2015-06","2019-11-19 22:13:01","2019-11-19 22:13:01","2016-09-03 20:48:57","","","","","","","Machine Beats Experts","","","","","International Educational Data Mining Society","","en","","","","","ERIC","","","","","http://eric.ed.gov/?id=ED560513","","Automation; Comparative Analysis; Correlation; data; Formative Evaluation; models; Online Courses; Skills","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"2893AKCA","conferencePaper","2008","Cortez, Paulo; Silva, Alice Maria Gonçalves","Using data mining to predict secondary school student performance","Proceedings of 5th Annual Future Business Technology Conference","978-90-77381-39-7","","","http://repositorium.sdum.uminho.pt/handle/1822/8024","Although the educational level of the Portuguese population has improved in the last decades, the statistics keep Portugal at Europe’s tail end due to its high student failure rates. In particular, lack of success in the core classes of Mathematics and the Portuguese language is extremely serious. On the other hand, the fields of Business Intelligence (BI)/Data Mining (DM), which aim at extracting high-level knowledge from raw data, offer interesting automated tools that can aid the education domain. The present work intends to approach student achievement in secondary education using BI/DM techniques. Recent real-world data (e.g. student grades, demographic, social and school related features) was collected by using school reports and questionnaires. The two core classes (i.e. Mathematics and Portuguese) were modeled under binary/five-level classification and regression tasks. Also, four DM models (i.e. Decision Trees, Random Forest, Neural Networks and Support Vector Machines) and three input selections (e.g. with and without previous grades) were tested. The results show that a good predictive accuracy can be achieved, provided that the first and/or second school period grades are available. Although student achievement is highly influenced by past evaluations, an explanatory analysis has shown that there are also other relevant features (e.g. number of absences, parent’s job and education, alcohol consumption). As a direct outcome of this research, more efficient student prediction tools can be be developed, improving the quality of education and enhancing school resource management.","2008-04","2019-11-19 22:13:02","2019-11-19 22:13:02","2016-09-04 01:23:19","","","","","","","","","","","","EUROSIS","Porto, Spain","eng","openAccess","","","","repositorium.sdum.uminho.pt","","","","","http://repositorium.sdum.uminho.pt/handle/1822/8024","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","5th Annual Future Business Technology Conference","","","","","","","","","","","","","","","" +"7IBCLUEK","journalArticle","2008","Baker, Ryan S. J. d; Corbett, Albert T.; Roll, Ido; Koedinger, Kenneth R.","Developing a generalizable detector of when students game the system","User Modeling and User-Adapted Interaction","","0924-1868, 1573-1391","10.1007/s11257-007-9045-6","http://link.springer.com/article/10.1007/s11257-007-9045-6","Some students, when working in interactive learning environments, attempt to “game the system”, attempting to succeed in the environment by exploiting properties of the system rather than by learning the material and trying to use that knowledge to answer correctly. In this paper, we present a system that can accurately detect whether a student is gaming the system, within a Cognitive Tutor mathematics curricula. Our detector also distinguishes between two distinct types of gaming which are associated with different learning outcomes. We explore this detector’s generalizability, and find that it transfers successfully to both new students and new tutor lessons.","2008-01-23","2019-11-19 22:13:02","2019-11-19 22:13:02","2016-09-04 01:34:49","287-314","","3","18","","User Model User-Adap Inter","","","","","","","","en","","","","","link.springer.com","","","","","https://link.springer.com/article/10.1007/s11257-007-9045-6","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"FC2ZMEQR","book","2014","Baker, R","Big Data in Education","","","","","","","2014","2019-11-19 22:13:02","2019-11-19 22:13:02","","","","","","","","","","","","","","New York, NY","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"52EF6FYK","videoRecording","2016","Georgia Tech","Cross Validation","","","","","https://www.youtube.com/watch?v=sFO2ff-gTh0","","2016-09-09","2019-11-19 22:13:02","2019-11-19 22:13:02","2016-09-09 19:37:11","","","","","","","","","","","","Youtube","","","","","","","","","","","","https://www.youtube.com/watch?v=sFO2ff-gTh0","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"VU3GMCSS","book","2019","Grolemund, Garrett","Hands-On Programming with R","","","","","https://rstudio-education.github.io/hopr/","This book will teach you how to program in R, with hands-on examples. I wrote it for non-programmers to provide a friendly introduction to the R language. You’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools. Throughout the book, you’ll use your newfound skills to solve practical data science problems.","2019-09-12","2019-11-19 22:13:02","2019-11-19 22:13:02","2019-09-12 17:12:28","","","","","","","","","","","","","","","","","","","rstudio-education.github.io","","","","","https://rstudio-education.github.io/hopr/","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"L5KALNRU","webpage","2019","Powell, V; Lehe, L","Principal Component Analysis explained visually","Explained Visually","","","","http://setosa.io/ev/principal-component-analysis/","","2019-09-12","2019-11-19 22:13:03","2019-11-19 22:13:03","2019-09-12 17:17:07","","","","","","","","","","","","","","","","","","","","","","","","http://setosa.io/ev/principal-component-analysis/","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" \ No newline at end of file