Psychological measurement Composition: Define a construct; specify measurement models and develop reliable instruments; analyze and explain various sources of error, including operator errors; and build valid justifications for specific uses of the results. Construction Construction can be used interchangeably with latent variables, and features are used to imply that construction is stable over time. (For example, a tape measure provides a scale)Psychological constructions, such as mathematical abilities and extraversion, make constructs equivalent to the scores of the instruments used to measure them.We can infer an extremely local list of structures related to learning analysis from the analysis tools that have been developed for metric learning. Measuring Instruments Tests, questionnaires, items or indicators. Errors Models used Latent Category and Potential Hybrid Models Project Response Theory Interpretation and / or prediction Learning analytics has been described as an intermediate space between learning science and analysis, so the field may benefit from an understanding of the nuances of the two perspectives.
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"NPV5KCHE","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","2019-12-20 02:41:28","2019-12-20 02:41:28","","49-57","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","Ethics Persuasive surveillance, algorithmic effects and unintended consequences. Ethics and privacy are key drivers in learning analytics. Will people inevitably realize the importance of ethics after a series of misconducts and even catastrophic consequences? Just like in other fields? As in the financial field, the CFA certificate occupies a large proportion of ethics and aims to solve the ethics in the financial field. Although Edtech can adopt some similar methods, he said that “inventing” Certificate exams emphasize the importance of ethics, but in the real world, because transparency and market regulations increase trust in data users. The question is, who and which entity has the right to regulate? How can we deal with the monopoly of large companies or any unnecessary intervention by the state, or the unavoidable controversy, better than nothing?It attracted me, and even the right to draft and draw up a code of ethics, suggests that there is some kind of sovereignty and power relationship in the field?
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"F9Z8WNLL","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","2019-12-20 02:41:28","2019-12-20 02:41:28","","61-68","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","Predict students' academic achievements. Predictive models in education. Unlike the interpretation model, this model aims to provide an explanation of the results and implies causality. The purpose of modeling is to create a model that predicts new data values based on observations. Intuitively, training data can be used to predict the value of new data. And implementation in education, including identifying vulnerable students in areas such as learning outcomes. Problem identification, Data collection, Classification and regression, Feature selection, Model construction (linear regression, logistic regression, KNN, decision tree, naive Bayes classifier, Bayesian network, support vector machine, neutral network, set method) 6. Model evaluation
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"4EX72J5W","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","2019-12-20 02:41:28","2019-12-20 02:41:28","","69-76","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","1. Educational data mining research focuses on developing two types of models: statistical models and cognitive models. The statistical model drives the outer loop of the intelligent tutoring system based on the observable characteristics of students' performance during learning (VanLehn, 2006). Cognitive models are representations of the knowledge space (facts, concepts, skills, etc.) in a particular education field. 2. Difficult factor assessment (DFA; for example, Koedinger & Nathan, 2004) goes beyond the intuition of experts by using a data-driven knowledge decomposition process to identify problematic elements of a defined task. In other words, when a task is much harder than a closely related task, the difference means an understanding of the harder tasks that do not exist.
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"WV7N264H","journalArticle","2011","Gelman, A; Niemi, J","Statistical graphics: making information clear – and beautiful","Significance","","","","","","2011-09","2019-12-20 02:41:28","2019-12-20 02:41:28","","134-136","","","","","","","","","","","","","","","","","","","","","Build more beautiful and useful data and inference summaries: Who is your target audience? What do you want to display? Avoid distractions. Use information colors to visually associate elements. keeps the graphics simple (and therefore explainable). Keep the x-axis and y-axis ratios the same. Consistency between plots.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"5WLMKDAU","journalArticle","2012","Gelman, A; Unwin, A","Infovis and Statistical Graphics: Different Goals, Different Looks (with discussion)","","","","","","","2012","2019-12-20 02:41:28","2019-12-20 02:41:28","","","","","","","","","","","","","","","","","","","","","","","One of the main differences between the two approaches is that Infovis rewards unique, unique displays, while statisticians have been trying to develop generics that have similar appearances in various applications method. . Few statisticians try to develop new things. They are using standard trial tools. Infovis attaches great importance to creativity and diversity, while statistics are centered on objectivity and reproducibility. Another important difference is the intended audience. Statisticians think that their audience is already interested and want to provide structured information, often a carefully prepared argument. For statisticians, graphics are part of the explanation. Even exploratory analysis often has a clear structure. Instead, Infovis designers want to draw attention to their graphics, and thus to the subject matter. For them, the graphics are more like a door opener.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"NUY9UAJD","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","2019-12-20 02:41:28","2019-12-20 02:41:28","","","","","","","","","","","","","","","","","Blog","","","","","","Trifecta Checkup is a universal framework for data visualization criticism. It documents the way people like to organize thinking behind the critique of their work. It's a clear framework that shows whether the charts in question, data, and visualization are correct and well constructed.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"TI5KVXH6","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","2019-12-20 02:41:28","2019-12-20 02:41:28","2014-09-24 19:31:29","","","7","15","","","Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students","","","","","","","en","","","","","ERIC","","","I should read this at the beginning of this semester, as most of the information covered in this article is relevant to what we have learned this semester. I'm glad to read a real case when analyzing education data for k-12 schools to help reform education policies and understand the situation of students before deciding whether to drop out. After completing all tasks and familiarizing myself with all the code, I am now familiar with the diagrams mentioned in this article and all the analysis methods used by researchers. Although many questions still need to be answered in the future development of educational data analysis, the current development trend marks a bright future in this field.
","","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"Q6FBFNLN","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","2019-12-20 02:41:29","2019-12-20 02:41:29","2014-08-20 20:21:46","167-178","","2","13","","CBE Life Sci Educ","Understanding Classrooms through Social Network Analysis","","","","","","","en","","","","","www.lifescied.org","","","Living in an age where people cannot live without social media. Researchers are very cautious to take into account changes in relationships over time. I think this research is very valuable to both teachers and students. For teachers, they can help students form study groups to help them get the best learning results. For students, with the help of this research, they can find partners who can best help them.
","","http://www.lifescied.org/content/13/2/167","Week 2","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"86JLBMU2","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","2019-12-20 02:41:29","2019-12-20 02:41:29","2014-08-23 21:32:22","","","","","","","","","","","","","","","","","","","","","","In my opinion, based on extensive research, the most effective teaching method is still based on your own pace and individual guidance strategy (ie apprenticeship or tutoring mode). However, this model is too expensive and impractical for all models except 1%. Perhaps with enough data and design, the model can be replicated through technology, and no teachers are needed at this time.
","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"SA5G525Z","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","2019-12-20 02:41:29","2019-12-20 02:41:29","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","","","In this article, the Bayesian cognitive model in the APT tutoring system is introduced. This model allows the tutor to interact with the students during the learning process and judge the quality and progress of the students. As described herein, the system has high internal and external effectiveness.
","","","","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"MSAZ758Y","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","2019-12-20 02:41:29","2019-12-20 02:41:29","2015-01-16 03:15:55","252–254","","","","","","Learning Analytics and Educational Data Mining","LAK '12","","","","ACM","New York, NY, USA","","","","","","ACM Digital Library","","","This article make me understand learning analytics
;This article summarizes the similarities and differences between educational data mining and learning analysis. The author's point is that researchers should learn from each other and improve their research in various fields.
","","","","Collaboration; educational data mining; learning analytics and knowledge","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"E6IFZ9UQ","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","2019-12-20 02:41:29","2019-12-20 02:41:29","2015-12-15 18:26:39","","","","","","","","","","","","O'Reily Media","Sebastopol, CA","","","","","","","","","He introduced diagnostic measures for three types of algorithms in data mining, including classification, ranking, and regression. The author points out that each type has its advantages and disadvantages, and recommends selecting the type for the appropriate purpose.
","","http://www.oreilly.com/data/free/evaluating-machine-learning-models.csp?intcmp=il-data-free-lp-lgen_free_reports_page","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"JLPRBH9U","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","2019-12-20 02:41:29","2019-12-20 02:41:29","2016-01-16 16:31:25","","","","","","","","","","","","","","","","","","","","","","Use the data to understand how students perform over time, how the school system provides services to different populations, and how successful different education strategies have been.
","","https://www.edsurge.com/news/2015-03-16-why-opting-out-of-student-data-collection-isn-t-the-solution","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"L89Q5Z57","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","2019-12-20 02:41:29","2019-12-20 02:41:29","2016-01-17 18:50:57","","","","","","","","","","","","","","","","","","","YouTube","","","In this video, they point out that despite measuring learning difficulties, they still measure learning well, such as social structure. Finding reliable measurements is important for the measurement process. Through the longitudinal model, students can measure the learning progress of educational achievements.
","","","","Learning; Assessment; Education; educational assessment; EDUCAUSE; Higher Education; learners; Teaching and learning","","","","","","","","","","","","","","","","","","","","","470 seconds","","","","","","","","","","","","","","","","","","","","","","","","","" +"SUTKKJ3N","webpage","2016","Weinersmith, Zach","Saturday Morning Breakfast Cereal","","","","","http://www.smbc-comics.com/index.php?id=3978","","2016-01-05","2019-12-20 02:41:29","2019-12-20 02:41:29","2016-01-18 18:17:09","","","","","","","","","","","","","","","","","","","","","","Comics suggest a phenomenon in which current education focuses more on results than on process and education itself. It is still doubtful whether educational achievements such as grades are more important than the improvement of learning ability.
","","http://www.smbc-comics.com/index.php?id=3978","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"633MC2YH","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","2019-12-20 02:41:30","2019-12-20 02:41:30","","49–53","","","","","","","","","","","ACM","","","","","","","","","","This article highlights the point that data brawlers are very helpful in interpreting educational data. I believe that when educators make education policies, data brawlers are very important to interpret educational data.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"ZKRX8BNJ","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","2019-12-20 02:41:30","2019-12-20 02:41:30","2016-01-18 19:14:05","","","","","","","","","","","","","","","","","","","","","","in this article, the author introduces Zuckerberg's personalized learning plan, and discusses the status and dangers of personalized learning. They suspect the potential for personalized learning development due to current conditions. First, it is difficult to determine whether personalized learning is truly personalized. Second, the high cost of personalization does not really help improve the quality of education for all.
","","https://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"MUYIQ95D","videoRecording","2015","Georgia Tech","Feature Selection","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","2015-02-23","2019-12-20 02:41:30","2019-12-20 02:41:30","2016-01-18 19:18:06","","","","","","","","","","","","Youtube","","","","","","","","","","A short introduction to this feature selection discusses two main reasons why we performed this process. One reason is interpretation and insight (knowledge discovery), and the other is solving the curse of dimensionality.
","","https://www.youtube.com/watch?v=8CpRLplmdqE","","","","","","","","","","","","","","","","","","","","","","","3:13","","","","","","","","","","","","","","","","","","","","","","","","","" +"R2B5MKI8","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","2019-12-20 02:41:30","2019-12-20 02:41:30","2016-01-18 20:17:24","","","","","","","","","","","","","","","","","","","","","","Network data and regular data </ p> <p> Unlike conventional data that emphasizes participants and attributes, network data focuses more on participants and relationships, which leads to the combination of nodes and edges in network data. A key point in the emergence of network data is that we are interested in both participants and found different participants The relationship between them. Therefore, when we design a sample to address the population we are interested in, we usually cannot include a single observation, that is, if an observation is selected, the relevant ""neighbors"" and their relationships should also be included in Data is being collected.
","","http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"ZD27NY2C","webpage","2014","Groelmund, Garrett","RStudio Cheat Sheets","RStudio","","","","https://www.rstudio.com/resources/cheatsheets/","","2014-08-01","2019-12-20 02:41:30","2019-12-20 02:41:30","2016-01-19 21:17:28","","","","","","","","","","","","","","","","","","","","","","The most useful information in this cheat sheet relates to the shiny R and format. I used to know about Rmarkdown, but it was limited to basic commands and formats. This cheat sheet tells me how to generate stories in Rmarkdown and generate Latex formatted text. This is very useful for people who may need to produce clean mathematical equations in a dissertation.
","","http://shiny.rstudio.com/articles/rm-cheatsheet.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"JW8SCA42","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","2019-12-20 02:41:30","2019-12-20 02:41:30","","","","","","","","","","","","","","","","","","","","","","","Predicting coellege enrollment is a good way in 21 century, but it is not accurt all the time.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"AP4MIWEH","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","2019-12-20 02:41:30","2019-12-20 02:41:30","2016-09-03 18:55:41","42-57","","3","15","","Journal of Educational Technology & Society","Translating Learning into Numbers","","","","","","","","","","","","JSTOR","","","This article provides a general framework for learning analytics, which includes six dimensions, such as data, stakeholders, and goals. The significance of this framework is that it connects interested parties in education and illustrates how these parties are linked in the field of learning analytics. As the author mentioned, the framework is not intended to be perfect and applicable to every situation in education, but rather it can be modified by educators to make them useful for their specific purpose.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"PZFDDRSI","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","2019-12-20 02:41:30","2019-12-20 02:41:30","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","In methods that support online (learning) teams, we track student interactions
Behavior in these dimensions and provides a visual mirror
To groups.
This is a comprehensive study of MOOC recommendation system courses at the Department of Computer Science at the University of Minnesota. The PCA analysis was limited to Section 4.4 ""Preliminary Analysis"", in which the author explored the reasons why students enrolled in this course and found four factors with eigenvalues exceeding 1. </ p> <p> But this article is not limited to PCA. It has performed many regression analyses in an attempt to find variables that significantly predict outcomes such as final grade and retention (five months after the course ends). The authors also compared face-to-face and online students, with the former generally prevailing in their learning outcomes. However, the authors caution that the small number of students in the previous group may make the results statistically unconvincing. </ p> <p> This article not only provides detailed information for studying PCA or even MOOC courses. It also discusses student interaction, the qualitative part. Some students apply what they have learned to their own business in a class or work immediately. This shows the usefulness of this recommendation system course.
","","","","learning assessment; Massively Online Open Course (MOOC)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"X6SGY6JI","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","2019-12-20 02:41:31","2019-12-20 02:41:31","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","Linear models of skills are important. Weighted sums of individual score items broken down by topic skill.
","","https://link.springer.com/chapter/10.1007/978-3-642-39112-5_45","","User Interfaces and Human Computer Interaction; Artificial Intelligence (incl. Robotics); Computers and Education; Information Systems Applications (incl. Internet); alternating least squares matrix factorization; Cognitive modeling; Educational Technology; latent skills; Pedagogic Psychology; skills assessment; Student models","Lane, H. Chad; Yacef, Kalina; Mostow, Jack; Pavlik, Philip","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"JQSS4MKS","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","2019-12-20 02:41:31","2019-12-20 02:41:31","2016-09-03 20:48:57","","","","","","","Machine Beats Experts","","","","","International Educational Data Mining Society","","en","","","","","ERIC","","","Skill models for online courses to better evaluate and improve courses. ""Our method assumes that online courses have a pre-defined skill map that is relevant to the formative assessment items embedded throughout the online course </ p> <p> The limitation of interpretability is that ""The obvious limitation of current technology is its reliance on manual inspections. "" The article introduces the analysis of eEPIPHANY's performance better than humans. A sophisticated skill model,"" eEPIPHANY is an efficient, practical and fast method that can be automatically discovered from online course data without human intervention Skill model. ""</ P>
","","http://eric.ed.gov/?id=ED560513","","data; Automation; Comparative Analysis; Correlation; Formative Evaluation; models; Online Courses; Skills","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"9SDTJM8J","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","2019-12-20 02:41:31","2019-12-20 02:41:31","2016-09-04 01:23:19","","","","","","","","","","","","EUROSIS","Porto, Spain","eng","openAccess","","","","repositorium.sdum.uminho.pt","","","Although there is a trend
Increase IT investment
From the government, most Portuguese
Public school information systems are very poor, relying mainly on paper
In this article, we will discuss the work of detecting games in cognitive mentors.
Cognitive tutor learning environments are designed to promote learning by doing.
In the cognitive mentor environment discussed in this article, each student
Complete the math problem alone. Cognitive tutor environment
Break down each mathematical problem into steps for the process used to solve it
Questions that make students' ideas clearly visible.
Big data is useful in education.
","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"TBNQ4KIV","videoRecording","2016","Georgia Tech","Cross Validation","","","","","https://www.youtube.com/watch?v=sFO2ff-gTh0","","2016-09-09","2019-12-20 02:41:32","2019-12-20 02:41:32","2016-09-09 19:37:11","","","","","","","","","","","","Youtube","","","","","","","","","","I don't understand this video.
","","https://www.youtube.com/watch?v=sFO2ff-gTh0","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"FRPEIWQJ","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","2019-12-20 02:41:32","2019-12-20 02:41:32","2019-09-12 17:12:28","","","","","","","","","","","","","","","","","","","rstudio-education.github.io","","","By reading this article, I know how to programming by R.
","","https://rstudio-education.github.io/hopr/","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"RMQPVINR","webpage","2019","Powell, V; Lehe, L","Principal Component Analysis explained visually","Explained Visually","","","","http://setosa.io/ev/principal-component-analysis/","","2019-09-12","2019-12-20 02:41:32","2019-12-20 02:41:32","2019-09-12 17:17:07","","","","","","","","","","","","","","","","","","","","","","I don't understand what the different between 3D and 2D....
","","http://setosa.io/ev/principal-component-analysis/","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" \ No newline at end of file diff --git a/Screen Shot.png b/Screen Shot.png new file mode 100644 index 0000000..4769348 Binary files /dev/null and b/Screen Shot.png differ