Welcome to Zotero!
View the Quick Start Guide to learn how to begin collecting, managing, citing, and sharing your research sources.
Thanks for installing Zotero.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"UCRCM6A9","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","2016-09-13 17:04:55","2016-09-13 17:04:55","2014-09-24 19:31:29","","","7","15","","","Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students","","","","","","","en","","","","","ERIC","","","Main goal of the study is to ""present hierarchical cluster analysis and visualization techniques as a useful method for the organization and pattern analysis of large sets of school and district data to aid data driven decision making (3DM).""
Cluster analysis on longitudinal student data, (188 students). Any student that was on track to graduate in the school system in the year 2006.
Use a heatmap to show the grades of students and whether they drop out, are male/female, District A or B, and if they took the ACT or not.
","","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"G4N7J8PR","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","2016-09-13 17:04:56","2016-09-13 17:04:56","2014-08-20 20:21:46","167-178","","2","13","","CBE Life Sci Educ","Understanding Classrooms through Social Network Analysis","","","","","","","en","","","","","www.lifescied.org","","","
Social Network Analysis (SNA) aims to understand the determinants, structure, and consequences of relationships between actors.
Actors, also called nodes, can be individuals, organizations, websites, or anything that can be connected to other entities. A group of actors and the connections between them is a network
The case study follows a 10-wk biology course and students were surveyed for their study partners. It then relates the first exam network with the second exam network and relates this to the score results.
Sociograph visualizations were created to show the networks of students who studied together, visualizing connections between nodes, gender, lab section, and performance on the assessment.
","","http://www.lifescied.org/content/13/2/167","Week 2","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"RPIXVEDX","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","2016-09-13 17:04:56","2016-09-13 17:04:56","2014-08-23 21:32:22","","","","","","","","","","","","","","","","","","","","","","Students should own their own data so they can create a portfolio of knowledge about themselves.
There also doesn't seem to be much use in using averages, as learners have a jagged profile and no learner is in fact average.
","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"MWN3PCJH","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","2016-09-13 17:04:56","2016-09-13 17:04:56","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"445XEHQT","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","2016-09-13 17:04:56","2016-09-13 17:04:56","2015-01-16 03:15:55","252–254","","","","","","Learning Analytics and Educational Data Mining","","","","","ACM","New York, NY, USA","","","","","","ACM Digital Library","","","9/13/2016 In Class:
Differences between EDM and LAK:
EDM has considerable focus on automation, while LAK leaves room for, and even leverages, human judgement.
LAK takes a system wide look, while EDM breaks down into smaller components.
","","","","Collaboration; educational data mining; learning analytics and knowledge","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"FKQ9NKFU","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.ezp-prod1.hul.harvard.edu/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.","2008-08-01","2016-09-13 17:04:56","2016-09-13 17:04:56","2015-01-16 16:33:56","287-314","","3","18","","User Model User-Adap Inter","","","","","","","","en","","","","","link.springer.com.ezp-prod1.hul.harvard.edu","","","Looks into the idea of students ""gaming the system"" which is when they exploit the properties of the LMS as opposed to trying to learn the material to answer correctly.
","","http://link.springer.com.ezp-prod1.hul.harvard.edu/article/10.1007/s11257-007-9045-6","","Behavior detection; Cognitive tutors; Education (general); Gaming the system; Generalizable models; Interactive learning environments; Latent response models; Machine learning; Management of Computing and Information Systems; Multimedia Information Systems; student modeling; User Interfaces and Human Computer Interaction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"GWCTN4JH","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","2016-09-13 17:04:56","2016-09-13 17:04:56","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"VDZWE796","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","2016-09-13 17:04:56","2016-09-13 17:04:56","2016-01-16 16:31:25","","","","","","","","","","","","","","","","","","","","","","
Fair Information Privacy Principles--requires data collectors to specify the purpose for which they are collecting data, and to seek informed consent for the collection and the use of this data
FERPA provides parents with the ability to opt out
""Opting out is not the answer"" these issues need to be addressed for all students, not just case by case basis
","","https://www.edsurge.com/news/2015-03-16-why-opting-out-of-student-data-collection-isn-t-the-solution","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"BP7VWIKJ","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","2016-09-13 17:04:56","2016-09-13 17:04:56","2016-01-17 18:50:57","","","","","","","","","","","","","","","","","","","YouTube","","","
Notes in Class 9/13/2016
Hard to measure learning if don't know a starting point
Can't measure all of the ways people learn
We have to simplify in order to understand, but we may ""throw away the signal and keep the noise""
","","","","Assessment; Education; educational assessment; EDUCAUSE; Higher Education; learners; Learning; Teaching and learning","","","","","","","","","","","","","","","","","","","","","470 seconds","","","","","","","","","","","","","","","","","","","","","","","","","" +"R4EJPQVA","webpage","2016","Weinersmith, Zach","Saturday Morning Breakfast Cereal","","","","","http://www.smbc-comics.com/index.php?id=3978","","2016-01-05","2016-09-13 17:04:56","2016-09-13 17:04:56","2016-01-18 18:17:09","","","","","","","","","","","","","","","","","","","","","","Clock comic--CLOCK YEAH!
","","http://www.smbc-comics.com/index.php?id=3978","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"6ARRPQJW","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","2016-09-13 17:04:56","2016-09-13 17:04:56","","49–53","","","","","","","","","","","ACM","","","","","","","","","","HUmans were used to provide information regarding data to less technical staff so that they could implement changes in learning environments
Data wrangling reports were created for each of the 7 faculties based on the data that was presented. They were able to interpret things like uniwue visits to sites, student enjoyment of activities,
This helped to ""close thefeedback loop"" which means they were able to take the data and create interventions or suggestions in order to actually implement changes as a result of the data
Interestingly, there aren't many metrics that support the effectiveness of this approach. student retention, completion, and feedback were no better, and some were wrose.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"9AASG366","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","2016-09-13 17:04:56","2016-09-13 17:04:56","2016-01-18 19:14:05","","","","","","","","","","","","","","","","","","","","","","Chan-Zuckerberg initiative is investing heavily in personalized learning
Zuck on personalized learning--""working with students to customize instruction to meet the students indibidual needs and interests""
Danger's of personalized learning:
1. Learning only their interests limits general knowledge
2. Challenges of real world may not be present in such a customized approach
3. Changes in preferences will cause issues, they aren't fixed but instead respond to environment
https://www.youtube.com/watch?v=8CpRLplmdqE\
Feature Discovery: How do we know which features actually matter?
Curse of Dimensionality: As you add more features, you need exponentially more data
So it is really nice to have less features
""Network"" data (in their purest form) consist of a square array of measurements. The rows of the array are the cases, or subjects, or observations. The columns of the array are -- and note the key difference from conventional data -- the same set of cases, subjects, or observations. In each cell of the array describes a relationship between the actors.
Nodes: Network data are defined by actors and by relations- nodes and edges.
Network analysis focuses on the relations among actors and not individual actors and their attributs
Full Network Methods: collect the information about each actors ties with ALL other actors
Analyzed student interaction with tutoring system and then tried to predict college attendance
Used ASSISTments to measure middle school mathemetics, this assessing their knowledge wile assisting their learning
Then they took data from studentclearinghouse to see if the students were enrolled in college or not
They created detectors of student affect and behavior and applied them to the dataset
Results: Students that had higher avg. knowledge estimate, avg. correct, number of first actions, avg. skip/carelessness, engagement were more likely to go to college.
Avverage gaming, average boredom, confusion and off task were all related to lower colelge going rates
Stakeholder section includes data clients and data subjects. Data clients are the beneficiaries of the LA proess who are entitled and meant to act upon the outcome (I.e. teachers) and data subjects are the suppliers of the data (i.e. learners)
ethical problems impact the reach of learning analytics data as well as other limitations like competencies in understanding the data, and legal uncertainty with regards to curation and ownership.
Also addresses some concerns about using Learning analytics ""for bad"", but specifies that Learning Analytics takes a bottom up appraoch, taking the learners needs as most important.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"ZZ27C3EP","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","2016-09-13 17:04:57","2016-09-13 17:04:57","2016-09-03 19:10:12","197-206","","","","","","","","","","","Springer Berlin Heidelberg","","en","©2006 Springer-Verlag Berlin Heidelberg","","","","link.springer.com","","","Five Teamwork Competencies: Team Leadership, Mutal performance monitoring, backup behavior, adaptability,
Team orientation
In order to realize the benefits of the five competencies, three coordinating mechanisms must be enacted:Shared Mental Models-mapping roles and how temmembers will interact, Mutual Trust-shared belief that team members will perform roles, and Closed Loop communication-exchange of info between sender and receiver
Types of Visualizations used:Activity Radar-a circle representing the range of participation and colored dots each representing an entity for which we want to compare participation levels.
Interaction Network-modeled as a unidirectional graph, consisting of a set of nodes and edges, where each node represents a user and an edge represents an interaction between the two corresponding users
Wattle Tree-
","","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"MDCZ2IZZ","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","2016-09-13 17:04:57","2016-09-13 17:04:57","2016-09-03 20:38:02","10:1–10:23","","2","22","","","Teaching Recommender Systems at Large Scale","","","","","","","","","","","","ACM Digital Library","","","
Looked at a course that taught recommender systems. These were mostly students who were male, educated, and under the age of 35, living outside of US.
The goals of the study:
1.Do students learn in this MOOC?
2. Do students who have access to MOOC material but also face to face with professor learn more?
3. What factors predict course completion
4. Differences between learning and practicing in MOOC and in traditional class?
5. Reasons for taking the MOOC, do these impact course performance?
6. Do the MOOC students retain what they've learned--measured 5 months after the course.
Results:
Intention is the best indicator of completeness, face to face students learned atleast as much as MOOCs, students of all incoming knowledge levels benefitted, programming students gained further knowledge, retention 5 months later seemed good for both MOOC and traditional classroom
How to tell which skills are necessary in order to successfully complete a course?
Learning Factor Analysis: semi-automaticall refines given skill sets.
ePIPHANY was used to automatically discover skillsets from online course data, which are the combination of the assesment item text data and student learning interaction data. The goal was to give constructive feedback to course developers to improve future courses
the epiphany model better found skills than humans, by finding ""optimal clustering parameters""
","","http://eric.ed.gov/?id=ED560513","","Automation; Comparative Analysis; Correlation; data; Formative Evaluation; models; Online Courses; Skills","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"HVBSVPB6","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","2016-09-13 17:04:57","2016-09-13 17:04:57","2016-09-04 01:23:19","","","","","","","","","","","","EUROSIS","Porto, Spain","eng","openAccess","","","","repositorium.sdum.uminho.pt","","","Early school leaving rate was 40% for portugese students 18-24. They decided to use some business intelligence techniques in the realm of Portugese education.
Questions that could be addressed:1. Who are the students taking the most credit hours?
2. WHo is the most likely to return for more classes?
3. WHat type of courses can be offered to attract more students?
4. What are the main reasons for student transfers?
They used grades and questionaires in combination to get the results.
","","http://repositorium.sdum.uminho.pt/handle/1822/8024","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","5th Annual Future Business Technology Conference","","","","","","","","","","","","","","","" +"85ISUQZ4","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","2016-09-13 17:04:57","2016-09-13 17:04:57","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"NRIDB5A2","book","2014","Baker, R","Big Data in Education","","","","","","","2014","2016-09-13 17:04:57","2016-09-13 17:04:57","","","","","","","","","","","","","","New York, NY","","","","","","","","","https://www.youtube.com/watch?v=Mr17Z0nZUQc&feature=youtu.be
visualization: displaying information in a meaningful fashion--show the data, induce the viewer tot hink about what it means, avoid distorting what the data has to say, make large datasets coherent, encourage the eye to compare different pieces of data
https://www.youtube.com/watch?v=oTiixxmh9-Q&feature=youtu.be
scatterplots don't scale well to large datasets
Heatmaps: show the density of specific groups
https://www.youtube.com/watch?v=WI1AVcpCYgk&feature=youtu.be
state space diagrams: visualizations of all the states that a learning system can have during a problem
","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"UMTNE8D6","videoRecording","2016","Georgia Tech","Cross Validation","","","","","https://www.youtube.com/watch?v=sFO2ff-gTh0","","2016-09-09","2016-09-13 17:04:57","2016-09-13 17:04:57","2016-09-09 19:37:11","","","","","","","","","","","","Youtube","","","","","","","","","","
cross validation of data-- youtube video
The goal is to generalize. It needs to be able to fit to other data that we don't necessarily know right now.
Data is supposedly representive of future data that is going to be used.
""data is independent and identically distributed""--all of the data is really coming from the same source
Use a model that is complex enough to fit the data, but also balance the generalizability.
""Folds"" can break the data into folds and then test on 3 of them and test on the 4th, alternating which are the ones used to train
","","https://www.youtube.com/watch?v=sFO2ff-gTh0","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"P9V8WM5D","webpage","","","Big Data – What It Means For The Digital Analyst | Analytics & Optimization","","","","","http://online-behavior.com/analytics/big-data","","","2016-09-19 18:34:39","2016-09-19 18:34:39","2016-09-19 18:34:39","","","","","","","","","","","","","","","","","","","","","","","C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\PWXWA6GF\big-data.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"NINRCW84","webpage","","","Passing the Privacy Test as Student Data Laws Take Effect | EdSurge News","","","","","https://www.edsurge.com/news/2016-01-12-passing-the-privacy-test-as-student-data-laws-take-effect?utm_content=bufferc0042&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer","","","2016-09-20 15:33:08","2016-09-20 15:33:08","2016-09-20 15:33:08","","","","","","","","","","","","","","","","","","","","","","Schools aren't allowed to require students to data. Is it possible to have a default 'opt in' to contributing data, but give the parents/students the ability to relatively easily opt out?
Is there really much difference between the data a student gives out on a school machine to non-school related content vs giving it to educational endeavors?
Anonymized data can't be used for advertising, interesting.
Must've been difficult to come up with a law to cover a changing/variety field of tech in education. It seems like the lawmakers's hearts are in the right place though.
","C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\KZDC3XEC\2016-01-12-passing-the-privacy-test-as-student-data-laws-take-effect.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"IJAQ24HF","webpage","","","Why Students Should Own Their Educational Data – Wired Campus - Blogs - The Chronicle of Higher Education","","","","","http://www.chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","","2016-09-27 14:57:44","2016-09-27 14:57:44","2016-09-27 14:57:44","","","","","","","","","","","","","","","","","","","","","","","C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\E9IQAEUV\54329.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"AM6WQJ4S","document","","","Assignment2 R","","","","","","","","2016-10-13 16:39:14","2016-10-13 16:42:37","","","","","","","","","","","","","","","","","","","","","","","","C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\Z22BDS3T\Assignment 2.Rmd; C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\Q3SBRE9H\RplotDC.pdf","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"QDWAHA5A","webpage","","","Cluster","","","","","https://www.cs.uic.edu/~wilkinson/Applets/cluster.html","","","2016-10-20 15:46:26","2016-10-20 15:46:26","2016-10-20 15:46:26","","","","","","","","","","","","","","","","","","","","","","k-means clustering assigns n points to k clusters. The algorithm computes iteratively until there are no changes in the averages.
If we do not know k in advance, how can we choose a value based on our data? This is circular and provides some issues. Hartigan suggests that we keep adding clusters until the sum of squares (the distance between the points squared) is negligible when adding new clusters.
There are issues because you can apply clusters to anything, even when 'clusters' aren't really there. This is displayed by the GIGO button, which applies 4 clusters to any data.
No good on categorical data, need uniform scale, doesn't like some shapes, too easy to not be critical of the strategy
","C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\CPUJ65CT\cluster.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"XVMS3F9T","webpage","","","The Grammar of Graphics","","","","","https://www.cs.uic.edu/~wilkinson/TheGrammarOfGraphics/GOG.html","","","2016-10-20 16:41:36","2016-10-20 16:41:36","2016-10-20 16:41:36","","","","","","","","","","","","","","","","","","","","","","Presents a foundation for producing many quantitative graphics found in journals, newspapares, statistical packages, and data visualization systems
","C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\7FR4HUPK\GOG.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"6PEQVE8P","webpage","","","Index. ggplot2 2.1.0","","","","","http://docs.ggplot2.org/current/","","","2016-10-20 16:41:51","2016-10-20 16:41:51","2016-10-20 16:41:51","","","","","","","","","","","","","","","","","","","","","","","C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\Z8X4DMPU\current.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"534HM2DF","document","","","Assignment 3 Instructions.Rmd","","","","","","","","2016-10-20 17:26:04","2016-10-20 17:26:04","","","","","","","","","","","","","","","","","","","","","","","","C:\Users\Cash Money Cody\Desktop\Columbia\Assignment3\Assignment 3 Instructions.Rmd; C:\Users\Cash Money Cody\Desktop\Columbia\Assignment3\Motivation_ThreeClust.pdf; C:\Users\Cash Money Cody\Desktop\Columbia\Assignment3\Motivation_TwoClust.pdf","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"7N5KXXV9","webpage","","","","","","","","https://pages.rstudio.net/Webinar-Nov9th_Registration.html?mkt_tok=eyJpIjoiTVRBME5tWTVOVFEzWkdabSIsInQiOiJaVmtmMThBQWpPRjd0Z0FqQVg3SFNxZ3RsNEpVZk1FVE1FZEhQdlhEeFFaNXNvRUhSZ1JsZkRpNG1IV2NiRFhGNVJTeHprZWYwWlMrRCtiU1p6MUdYcEgxVDEyc255aXcwUWdyMTFtMXVXVT0ifQ%3D%3D","","","2016-11-01 16:16:11","2016-11-01 16:16:11","2016-11-01 16:16:11","","","","","","","","","","","","","","","","","","","","","","","C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\8VIQ4U9Z\Webinar-Nov9th_Registration.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"7QAV5ZTJ","encyclopediaArticle","2016","","Travelling salesman problem","Wikipedia","","","","https://en.wikipedia.org/w/index.php?title=Travelling_salesman_problem&oldid=746342539","The travelling salesman problem (TSP) asks the following question: ""Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?"" It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science. TSP is a special case of the travelling purchaser problem and the vehicle routing problem. In the theory of computational complexity, the decision version of the TSP (where, given a length L, the task is to decide whether the graph has any tour shorter than L) belongs to the class of NP-complete problems. Thus, it is possible that the worst-case running time for any algorithm for the TSP increases superpolynomially (but no more than exponentially) with the number of cities. The problem was first formulated in 1930 and is one of the most intensively studied problems in optimization. It is used as a benchmark for many optimization methods. Even though the problem is computationally difficult, a large number of heuristics and exact algorithms are known, so that some instances with tens of thousands of cities can be solved completely and even problems with millions of cities can be approximated within a small fraction of 1%. The TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips. Slightly modified, it appears as a sub-problem in many areas, such as DNA sequencing. In these applications, the concept city represents, for example, customers, soldering points, or DNA fragments, and the concept distance represents travelling times or cost, or a similarity measure between DNA fragments. The TSP also appears in astronomy, as astronomers observing many sources will want to minimize the time spent moving the telescope between the sources. In many applications, additional constraints such as limited resources or time windows may be imposed.","2016-10-26","2016-11-03 16:14:50","2016-11-03 16:14:50","2016-11-03 16:14:50","","","","","","","","","","","","","","en","Creative Commons Attribution-ShareAlike License","","","","Wikipedia","","Page Version ID: 746342539","The travelling salesman problem (TSP) asks the following question: ""Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the origin city?"" It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science.
","C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\BEAHNBTB\index.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"WXRHMZUD","webpage","","","DataShop > About > FAQ","","","","","https://pslcdatashop.web.cmu.edu/about/faq.html","","","2016-11-21 18:41:48","2016-11-21 18:41:48","2016-11-21 18:41:48","","","","","","","","","","","","","","","","","","","","","","You do not need permission to view or use public data sets; they are freely accessible to any researcher in the world.""
DataShop access is free.
As long as proper IRB rules and guidelines have been followed and you have access to the data through DataShop, you may publish an analysis you have conducted on another researcher's data.
The Pittsburgh Science of Learning Center (PSLC) DataShop is the world's preeminent central repository for data on the interactions between students and educational software and a suite of tools to analyze that data. It provides secure data storage as well as an array of exploratory analysis and visualization tools available through a web-based interface.
","C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\WUM99I2X\faq.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"4QXICUNX","webpage","","","Learning Analytics Interoperability – The Big Picture in Brief - briefing-01.pdf","","","","","http://laceproject.eu/publications/briefing-01.pdf","","","2016-11-22 09:01:57","2016-11-22 09:01:57","2016-11-22 09:01:57","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"W686WAG2","webpage","","","An Introduction to corrplot Package","","","","","https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html","","","2016-11-26 04:28:19","2016-11-26 04:28:19","2016-11-26 04:28:19","","","","","","","","","","","","","","","","","","","","","","","C:\Users\Cash Money Cody\AppData\Roaming\Mozilla\Firefox\Profiles\muuoeskg.default\zotero\storage\ZH6BKK6B\corrplot-intro.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" \ No newline at end of file diff --git a/StackOverflow1.JPG b/StackOverflow1.JPG new file mode 100644 index 0000000..21208ca Binary files /dev/null and b/StackOverflow1.JPG differ diff --git a/StackOverflow2.JPG b/StackOverflow2.JPG new file mode 100644 index 0000000..9f8467d Binary files /dev/null and b/StackOverflow2.JPG differ