I believe that the use of learning analysis tools through design-or accidental-is always consistent with the evaluation system, which is based on epistemological assumptions and teaching practice. Therefore, fundamentally, we believe that the deployment of a specific learning analysis tool expresses a commitment to a specific educational worldview aimed at cultivating specific types of learners. We summarize some key problems in the development of learning analysis technology, and put forward the purpose and hypothesis of learning analysis. We suggest that the use of ""claim analysis""-an implicit or clear position taken in the design and deployment of analytical techniques-is a productive people-centred approach to these key issues, and we provide some examples of methods applied to these provocation.
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"ZGJ3XPE5","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","2019-12-16 23:48:34","2019-12-16 23:48:34","","49-57","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","I believe that the deployment of a specific learning analysis tool expresses a commitment to a specific educational worldview aimed at cultivating specific types of learners. We summarize some key problems in the development of learning analysis technology, and put forward the purpose and hypothesis of learning analysis.
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"GGJZ5ANC","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","2019-12-16 23:48:34","2019-12-16 23:48:34","","69-76","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","I think the fragments of opinion on specific data graphics are often seen as a stream of conscience. If the author can't say why, declaring a chart ""shocking"" or ""the worst of the century"" is worthless. David's criticism has not developed further, but has only formed a set of rules of thumb.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"EY9EXW85","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","2019-12-16 23:48:34","2019-12-16 23:48:34","2014-09-24 19:31:29","","","7","15","","","Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students","","","","","","","en","","","","","ERIC","","","
The social interaction among the students is an important part of the undergraduate education and is not fully explored. To know how to form the learning relationship in the college students' class, and the influence of these relationships on the learning result, we can provide information to the educators in a unique way and improve the education reform. The Social Network Analysis (SNA) provides the necessary toolkit for the investigation of issues related to relational data. We introduced the basic concepts of the SNA, as well as the methods of data collection, data processing and data analysis. We analyzed the structure of the cooperative relationship network
","","http://www.lifescied.org/content/13/2/167","Week 2","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"M2CZU3LX","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","2019-12-16 23:48:35","2019-12-16 23:48:35","2014-08-23 21:32:22","","","","","","","","","","","","","","","","","","","","","","Because our science textbooks assume that every child reads at the grade level, we are in trouble. "" ""for her, science is first and foremost a reading test, and it is questionable whether we can see her true ability.""
","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"I6F7ISNG","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","2019-12-16 23:48:35","2019-12-16 23:48:35","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","","","I think the modeling math, we could track knowledge process.
","","","","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"ZD86M8FA","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","2019-12-16 23:48:35","2019-12-16 23:48:35","2015-01-16 03:15:55","252–254","","","","","","Learning Analytics and Educational Data Mining","LAK '12","","","","ACM","New York, NY, USA","","","","","","ACM Digital Library","","","I think the growing interest in data and analysis in education, teaching and learning has increased the priority of high-quality research on the model, methodology, technology and impact of analysis.
Understand the stages involved in the development of machine learning models for software applications, understand the metrics for supervised learning models, including classification, regression, and ranking walking through evaluation mechanisms, such as persistence verification, cross-validation, and guidance to explore superparameter optimization, and find out why it is difficult to understand the defects of A / B testing. And examine a promising option: multi-armed bandits for further reading advice, as well as useful software packages.
","","http://www.oreilly.com/data/free/evaluating-machine-learning-models.csp?intcmp=il-data-free-lp-lgen_free_reports_page","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"B7IL8NZP","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","2019-12-16 23:48:35","2019-12-16 23:48:35","2016-01-16 16:31:25","","","","","","","","","","","","","","","","","","","","","","Great video. I want to know what this means to the RyanoBaker and his students, especially on any group of interested scholars, and is also an exciting victory for the Carnegie Mellon's OlI and the most important funding for acrobatics.
","","","","Learning; Assessment; Education; educational assessment; EDUCAUSE; Higher Education; learners; Teaching and learning","","","","","","","","","","","","","","","","","","","","","470 seconds","","","","","","","","","","","","","","","","","","","","","","","","","" +"9B7FGFTL","webpage","2016","Weinersmith, Zach","Saturday Morning Breakfast Cereal","","","","","http://www.smbc-comics.com/index.php?id=3978","","2016-01-05","2019-12-16 23:48:35","2019-12-16 23:48:35","2016-01-18 18:17:09","","","","","","","","","","","","","","","","","","","","","","students have more opportunities to get the their hours personally.
","","http://www.smbc-comics.com/index.php?id=3978","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"K2X6EZWJ","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","2019-12-16 23:48:35","2019-12-16 23:48:35","","49–53","","","","","","","","","","","ACM","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"RY89UKV2","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","2019-12-16 23:48:35","2019-12-16 23:48:35","2016-01-18 19:14:05","","","","","","","","","","","","","","","","","","","","","","Many in the education believe that individual learning is what all the great teachers do, of course, to modify learning materials and teaching styles to adapt to the different ways of student learning. Others think it is the antidote to the top-down central school bureaucracy, the term ""personalized"" and the individual, and the terms used by the learner as a center or custom can be used interchangeably. It is clear that teachers should support this personalized learning with a personalized resource based on each student, and should not be at the expense of doing so.
","","https://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"PN39I632","videoRecording","2015","Georgia Tech","Feature Selection","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","2015-02-23","2019-12-16 23:48:36","2019-12-16 23:48:36","2016-01-18 19:18:06","","","","","","","","","","","","Youtube","","","","","","","","","","It is useful and getting more information.
","","http://shiny.rstudio.com/articles/rm-cheatsheet.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"UMXBIYZN","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","2019-12-16 23:48:36","2019-12-16 23:48:36","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"P4BZMNSY","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","2019-12-16 23:48:36","2019-12-16 23:48:36","2016-09-03 18:55:41","42-57","","3","15","","Journal of Educational Technology & Society","Translating Learning into Numbers","","","","","","","","","","","","JSTOR","","","If the platform or the result of the present invention can be used to produce a large amount of material, or can be used as a raw material for the production of a large amount of material, the raw material and the like can be prepared by the method of the invention, and the method of the invention can be used for solving the problem that the method of the invention comprises the following steps of: (1) the method of the invention comprises the following steps of: At present, some of the main research and development platforms on the platform are also analyzed and studied on the basis of the analysis and study of the large amount of food and materials. A set of research processes based on Learning Analytics.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"PTP88SFD","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","2019-12-16 23:48:36","2019-12-16 23:48:36","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","The students have achieved significant knowledge outcomes at all levels of existing knowledge and in all categories of population. The main prediction of knowledge gain is the effort spent in the course. After the course, students also have important intellectual retention. Both of these results are limited to the selection of the sample of the students who have completed our knowledge tests.
","","","","learning assessment; Massively Online Open Course (MOOC)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"MD2Z677K","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","2019-12-16 23:48:36","2019-12-16 23:48:36","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","Our approach assumes that the online course has a predefined skill map, where the skills are associated with the formative assessment project and embedded in the entire online course. Our approach has carefully utilized the correlation between the various parts of the student's performance, as well as the text of the evaluation project, and the establishment of an excellent statistical model that is even better than the human expert.
","","http://eric.ed.gov/?id=ED560513","","data; Automation; Comparative Analysis; Correlation; Formative Evaluation; models; Online Courses; Skills","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"EYSYSJJF","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","2019-12-16 23:48:37","2019-12-16 23:48:37","2016-09-04 01:23:19","","","","","","","","","","","","EUROSIS","Porto, Spain","eng","openAccess","","","","repositorium.sdum.uminho.pt","","","The purpose of this work is to explore the academic achievements of middle school students by using BI/DM technology. Recent real-world data (such as student performance, population, social and school-related characteristics) were collected using school reports and questionnaires. These two core classes (mathematics and Portuguese) are modeled as binary / five-level classification and regression tasks.
","","http://repositorium.sdum.uminho.pt/handle/1822/8024","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","5th Annual Future Business Technology Conference","","","","","","","","","","","","","","","" +"4C8ND63N","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","2019-12-16 23:48:37","2019-12-16 23:48:37","2016-09-04 01:34:49","287-314","","3","18","","User Model User-Adap Inter","","","","","","","","en","","","","","link.springer.com","","","i learned how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all R programming tools. Throughout the book, you will use your newly discovered skills to solve practical data science problems.
","","https://rstudio-education.github.io/hopr/","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"HK7883X3","webpage","2019","Powell, V; Lehe, L","Principal Component Analysis explained visually","Explained Visually","","","","http://setosa.io/ev/principal-component-analysis/","","2019-09-12","2019-12-16 23:48:37","2019-12-16 23:48:37","2019-09-12 17:17:07","","","","","","","","","","","","","","","","","","","","","","
The visual effect was great. It's a bit. Is there no physical meaning in the shaft? I think it depends, in effect, on the situation, isn't it? For example, in chemical engineering, many ""dimensionless"" are used to simplify the understanding of the physical phenomena. These include the number of Prandtl (the ratio of the momentum diffusion rate to the thermal diffusivity), the Reynolds number (the ratio of the inertial force to the force of the fluid in the fluid), the number of Schmidt (the momentum diffusion rate (viscosity), and the mass diffusion rate).
","","http://setosa.io/ev/principal-component-analysis/","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" \ No newline at end of file