Differences
This shows you the differences between two versions of the page.
|
projs:edanalytics:home [2013/07/06 19:12] 43107780 |
projs:edanalytics:home [2013/07/10 11:16] (current) 43107780 |
||
|---|---|---|---|
| Line 49: | Line 49: | ||
| - administrators analyze student performance, correctly identify which investments are working, and forecast needs for staffing, programs, and courses | - administrators analyze student performance, correctly identify which investments are working, and forecast needs for staffing, programs, and courses | ||
| - **Improving Academic Outcomes**. Forward-thinking colleges and universities are already using our products to achieve big goals in course completion, persistence, and other areas of student success. We are committed to building this community of schools and learning together to find out which decisions and practices work best to improve student learning — not anecdotally, but based on the richest data available within and across institutions. | - **Improving Academic Outcomes**. Forward-thinking colleges and universities are already using our products to achieve big goals in course completion, persistence, and other areas of student success. We are committed to building this community of schools and learning together to find out which decisions and practices work best to improve student learning — not anecdotally, but based on the richest data available within and across institutions. | ||
| + | ==== Junyo ==== | ||
| + | - url http://junyo.com/ | ||
| + | - Introduction: Junyo leverage the Power of Data and Analytics to Improve the Education Sector. Junyo develops innovative technology solutions that make sense of the data available to educational suppliers, schools, teachers and students. Our premise is straightforward: we’re committed to improving the educational marketplace and ultimately the individual learning process. | ||
| + | - Big Data and Real-Time Learning Analytics: We deeply believe real-time data analytics must play a critical role in 21st century education. The products we are developing will enable participants across the educational sector to develop better solutions that improve the overall educational experience. Our experience building blended-learning platforms for leading K5 schools provides us unique teacher and classroom insights. Our EdLights market-intelligence platforms empowers publishers and educational suppliers to deliver the right solution to the right schools at the right time. Our next products under development will harness the enormous amount of data now available to students and help them make sense of it. The result will be measurable classroom improvement, higher motivation and greater market efficiency. | ||
| + | - Junyo Believes Data Analytics Will: | ||
| + | - Deliver real-time feedback across the educational sector to help deliver solutions that will positively impact learning | ||
| + | - Empower suppliers, administrators, and schools to evaluate the effectiveness of their solutions and stay focused on improving student learning results | ||
| + | - Determine opportunities and pinpoint areas of needed change and guide resource allocation | ||
| + | - Confirm or discredit assumptions about districts, schools, school practices and student results | ||
| + | - Avoid one-size-fits-all and quick solutions | ||
| + | ==== Knewton ==== | ||
| + | - url http://www.knewton.com/about/ | ||
| + | - Introduction: Knewton is an infrastructure platform that makes it possible for anyone to build the world’s most powerful adaptive learning applications. Knewton technology can be added to new or existing digital learning products. Every Knewton-enhanced product provides practical analytics for instructors. Knewton recommendations can be implemented to accommodate a variety of course flows. | ||
| + | - Functionalities: | ||
| + | - Analytics Only: Knewton analytics provides insight into each student's performance and the quality of educational content. | ||
| + | - Adaptive Practice: In addition to analytics, Knewton provides students with targeted recommendations for extra help assignments and assessments to supplement primary coursework. | ||
| + | - Fully Adaptive Coursework: Knewton recommendations determine each student's flow through course material. Particularly well-suited for fully online courses, this implementation includes analytics and emphasizes true competency-based learning. | ||
| + | ==== AUS Eadvisor ==== | ||
| + | - url https://eadvisor.asu.edu/ | ||
| + | - Functionalities: | ||
| + | - Choosing a major | ||
| + | - search a major/ by name, relevant words | ||
| + | - compare majors | ||
| + | - support transfer students show how their current community college courses will fit into the degree requirements of any ASU degree | ||
| + | - offers five exploratory majors | ||
| + | - Choosing Courses | ||
| + | - keep up to date course information | ||
| + | - the departments described the sequences of courses required for each major and identified the ones that are critical in diagnosing who is unlikely to succeed in. if students do bad in important class, the system will flag that individual for additional academic support and offer advice on choosing a different program. | ||
| + | - identify the best course sequence to match their skills | ||
| + | - If students want to study subjects that are unrelated to their majors, the system makes them confront the conflict. If a student’s performance does not predict success, the advisor, using the system, can identify other courses of study for which the individual is better suited, based on the experience of many other students. | ||
| + | - constantly updating information, ensuring that each student’s record matches the criteria for success, and alerting advisors that students need personal attention exactly when they need it. So rather than waiting for a cumulative judgment at the end of their sophomore year, students continuously know whether they are on their way to success in their chosen major or whether they should reevaluate their goals. | ||
| + | - advisor can focus on improving the student's chances of success. | ||
| + | - Advisor tools | ||
| + | - Online tools and reports enable advisors to monitor the progress of each student assigned to them and to focus their attention on those who require help. In this way, they can effectively support a large number of advisees | ||
| + | - Scheduling Courses | ||
| + | - The system has complete information on every student’s major, courses completed, and courses needed. It knows which courses will be required for the next semester for all students, as well as which of these are critical and must be provided on time. | ||
| + | - It also calculates how many seats are needed the next semester in every critical course. | ||
| + | - Monitoring programs, students, and advisors | ||
| + | - manage degree map | ||
| + | - The eAdvisor tools also allow the university to monitor the work of the academic advisors. | ||
| + | - The university also monitors dashboard usage. Whenever new computer systems are introduced, some employees continue to rely on obsolete paper processes rather than learn the new routines. | ||
| + | - Supporting Students | ||
| + | - consolidates all student information includes my ASU usage | ||
| + | ===== Related Material ===== | ||
| + | - http://elearning-analytics.blogspot.hk/ | ||
| + | - http://hackeducation.com/ | ||
| + | |||
| + | ===== Related Tools ===== | ||
| + | - Keel | ||
| + | - url:http://www.keel.es/ | ||
| + | - Introduction: KEEL is an open source (GPLv3) Java software tool to assess evolutionary algorithms for Data Mining problems including regression, classification, clustering, pattern mining and so on. It contains a big collection of classical knowledge extraction algorithms, preprocessing techniques (training set selection, feature selection, discretization, imputation methods for missing values, etc.), Computational Intelligence based learning algorithms, including evolutionary rule learning algorithms based on different approaches (Pittsburgh, Michigan and IRL, ...), and hybrid models such as genetic fuzzy systems, evolutionary neural networks, etc. It allows us to perform a complete analysis of any learning model in comparison to existing ones, including a statistical test module for comparison. Moreover, KEEL has been designed with a double goal: research and educational. For a detailed description, see the section 'Description' on the left menu. | ||
