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Type: Project | Country:

JISC is a UK-based body that supports HE and other post-16 education institutions in the use of digital technologies. JISC's Effective Learning Analytics project is a significant initiative over two years to July 2016, with the goal of "helping further and higher education organisations to analyse and understand their data."

The aim is to "provide a set of basic learning analytics tools drawing from a range of data sources and using proven metrics", with pilot solutions and the commissioning of a basic learning analytics 'freemium' solution to build on.

Advice and guidance for the sector about learning analytics has been developed, including a review of the state of play in UK higher and further education in 2014, a literature review on the ethical and legal issues, and a Code of Practice for Learning Analytics.

Type: Project

Apereo is a network of educational institutions supporting learning worldwide, chiefly through open/community source software, which grew out of the Sakai Foundation.

Apereo is doing significant work on learning analytics through its Learning Analytics Initiative, including work on the Open Academic Analytics Initiative (OAAI), on its Open Analytics Platform, and with the JISC Learning Analytics infrastructure:

"The Apereo Learning Analytics Initiative (LAI) aims to accelerate the operationalization of Learning Analytics software and frameworks, support the validation of analytics pilots across institutions, and work together so as to avoid duplication where possible.
Work is underway across the learning analytics domain to realize an open analytics infrastructure. Institutions and companies are partnering to build a solid learning analytics foundation, in order to enable institutions to ask and answer strategic questions about learners – and take action on those insights."

 

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The Knowledge Media Institute of the Open University, UK developed OU Analyse, a software that predicts students at risk. OU Analyse builds upon two previous projects (Retain and the OU-Microsoft Research Cambridge project). OU analyse uses machine learning techniques to develop predictive models based on demographics and VLE usage data.

The software provides a dashboard reporting the aggregated prediction value of several models for all students of a module. Furthermore, the tool discloses the underlying reasoning for its prediction. Currently, the institute develops an activity recommender that can recommend activities to students to improve their performance. Course chairs, module teams, and student support teams use the predictions of OU Analyse to contact and support students.

[This synopsis originally coded by the LAEP project - Learning Analytics for European educational Policy https://laepanalytics.wordpress.com/]

Type: Project | Country:

Tribal, based in the UK, is a global provider of software solutions specialising in products supporting the management of education.

Tribal's Student Insights is a software that is currently developed to predict student performance and 'at-risk' students from data available in student information systems, such as academic performance at entrance, demographics, or assessment results, but also activity data, such as student interaction, VLE Usage, or library usage.

The software generates predictive models about student's likelihood to pass a module. The software provides dashboards of this information on student and module level. Managers of universities can use this information, for example, to provide individual student support, or to monitor modules with regard to their predicted performance.

[This synopsis originally coded by the LAEP project - Learning Analytics for European educational Policy https://laepanalytics.wordpress.com/]

Type: Project | Country:

FFT, the Fischer Family Trust, is an UK non-profit organisation that provides services for UK based education, such as the National Pupil Database for the DFE and school analyses.

The software FFT Aspire is a data analysis and reporting tool for schools. It provides several dashboards showing facets of school performance, such as past attainment, progression, attendance, or future performance estimates. It targets several users groups, such as teachers, subject leaders, department heads, senior school leaders, advisors, local authorities, and governors.

The range of dashboards includes an overall school dashboard, a subject dashboard for department heads, subject leaders, and teachers, a governor dashboard (helping schools to share information with governors), a student explorer dashboard, a collaboration dashboard (compare school performance with other schools), and a target setting dashboard (school performance targets). Furthermore, the tool allows to create custom analyses and dashboards such as a 3 year dashboard, SEN pupils only dashboard, high attainers dashboard, etc.

[This synopsis originally coded by the LAEP project - Learning Analytics for European educational Policy https://laepanalytics.wordpress.com/]

Resource Link: https://fftaspire.org/

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Cognitive tutor is an intelligent tutoring software provided by the US company Carnegie Learning. This web-based software is mostly used to teach math to 9-12 grader students. The software provides personalised learning activities and customised feedback for several prepared math courses based on a domain, tutoring, and student skill models.

Two learning analytics relevant components of this software are the 'Skillometer' and the teacher reports. The 'Skillometer' is a visual indicator of students' progress in mastering skills. It gives the student an indicator of skill mastery for each achievable skill of a learning unit. The level of mastery shown by the tool expresses a prediction about the ability to demonstrate this skill in future again. The data for this visualisation stem from the tracking of the interaction of the student with the software.
Teachers are supported with several reports that are generated by the software. The class progress report shows unit by unit the amount of active students. The class skill alert report shows for each skill the skill mastery level for each student. The student detailed report shows for each student the amount of mastered skills, time spent, amount of completed units, sections, and problems. The detail by section report shows information for each student on a unit by unit level. Another report shows aggregated data for each unit. The student skill alert report shows units of underperformance. The class assessment reports allow to compare pre-test with post-test results by topic, or by problem on class level. The student assessment reports show pre-test and post-tests results by topic, or by problem on student level. These reports aim at supporting teachers with their instructional decision-making.

[This synopsis originally coded by the LAEP project - Learning Analytics for European educational Policy https://laepanalytics.wordpress.com/]

Type: Project | Country:

CourseSmart Analytics is available to teachers whose institutions participate in an integration between the institution's LMS and CourseSmart's eTextbooks; the integration is effected using IMS Global’s Learning Tools Interoperability standard (LTI). CourseSmart’s analytics dashboard presents a measurement of students’ engagement with digital course materials. A centerpiece of this dashboard is the CourseSmart Engagement Index Technology™, a proprietary algorithm that evaluates standard usage data such as number of pages read, number of times a student opened/interacted with the digital textbook, number of days the student used their textbook, time spent reading, number of highlights, number of bookmarks, and number of notes, and assimilates them into an overall assessment of students' engagement with the material. Junco & Clem (2015) carried out a study of 236 students using CourseSmart in the Spring 2013 semester. They found that CourseSmart Engagement Index “was a significant predictor of course grades across disciplines, instructors, and course sections”. However “the number of days students spent reading was a more powerful predictor of course outcomes. This suggests that the calculated Engagement Index does not yet capture the important factors related to engagement with the textbook”. Juno & Clem conclude that the “CourseSmart Engagement Index needs to be refined” and this may be happening. CourseSmart were acquired by VitalSource in early 2014, and press-releases issued in October 2015 announced “the upcoming re-launch of our analytics product”, however, there have been no further announcements to date.
The analytics are intended to give teachers insights into their students’ engagement with and patterns of usage of e-books, with a view to enabling teachers make interventions based on this data.

[This synopsis originally coded by the LAEP project - Learning Analytics for European educational Policy https://laepanalytics.wordpress.com/]

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Schoolzilla provides a data warehouse and associated data dashboard targeted at the K-12 US market. It provides ‘connectors’ which allow data to be integrated into the system through nightly updates from multiple sources, e.g. assessment, behaviour, enrolment , grade, observation,and student information databases. Schoolzilla provides multiple views of this integrated data through a dashboard library. Representations for teachers, school leaders, school district leaders and system administrators are provided in the library, and these may be customised by system administrators through use of Tableau’s data visualisation products. Teachers can use dashboards such as the ‘Early warning signs’ report to identify at-risk students. For example, this particular dashboard brings together data on attendance, behaviour, and grades, and allows users to view data for schools as a whole, to compare schools (for district leaders) and to drill-down to view data about individuals. The quality of the data within the system may be monitored by system administrators using dashboards which present the results of data audits e.g. automatic checks for missing or malformed data.

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Developed for K-12 classrooms, itslearning is a LMS with functionality for course management and delivery, curriculum management, and reporting and analytics. The reporting and analytics features incorporate functionality for standards mastery reporting (enabling teachers to see the percentage of students who have mastered each course standard), and a content recommendation engine which “provides remediation and enrichment activities based on student performance against learning objectives, and their individual learning styles”. This enable the identification of students who are struggling to meet learning objectives and assign them activities for reinforcement. The itslearning recommendation engine can automate “most” of the process of “identification of students who are struggling to meet learning objectives and assign them activities for reinforcement” www.itslearning.co.uk/Websites/.../Personalized_Learning_Recipe.pdf.
The reporting features enable students, teachers, administrators, mentors and parents to view student aspects of students progress via their personalized dashboard. Teachers and administrators can filter views of how classes have performed with respect to specific learning objectives by date, or by status (e.g. to show only the students who have exceeded a particular learning objective) (see https://vimeo.com/118518649). A parents’ dashboard enables parents to see their child’s progress on tasks, grades, towards learning objectives, their , individual learning plans, behaviour and attendance.

[This synopsis originally coded by the LAEP project - Learning Analytics for European educational Policy https://laepanalytics.wordpress.com/]

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Student Success Plan (SSP) is software to support case management of student support: counselling, coaching, pastoral care, etc. It has light-weight data analytics, principally focussed on the management and enhancement of student support services, but is being adopted to support action-taking in relation to predictive analytics.

SSP is designed to improve retention, academic performance, persistence, graduation rates, and time to completion. Through counseling, web-based support systems, and proactive intervention techniques, students are identified, supported, and monitored. The software provides case management tools for: handling staff, student, and student-services communications, action planning, planning academic choices, alerting, student self-assessment, and progress monitoring.

SSP is not a single “out of the box” solution, but a set of configurable components adopting an open architecture such that they can be integrated into a variety of system landscapes. An Open Source Software edition is available, overseen by the Apereo Foundation.

[This synopsis originally coded by the LAEP project - Learning Analytics for European educational Policy https://laepanalytics.wordpress.com/]