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12 JULY 2015

Assessment Careers: enhancing learning pathways

"Assessment is often viewed as a series of one-off events. This means that learners do not always benefit from feedback, they lack a sense of progress and self-reliance is not encouraged. This project will reconceptualise assessment from the perspective of an assessment career and use this to transform our institution's assessment processes. Like a working career, an assessment career develops through a series of related events that join up to give a coherent and progressive pathway that is self-directed."

(Gwyneth Hughes, Research Portal, Institute of Education, University of London)

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CONTRIBUTOR

Simon Perkins
12 JULY 2015

Longitudinal and ipsative assessment

"Ipsative assessment and feedback (assessment and feedback based on comparison with previous performance) describes an approach to assessment that focuses on improvement against past performance rather than grading against set criteria. Commonly used in performance-related disciplines such as music or sport, ipsative assessment enables credit to be given for improvement regardless of achievement (Hughes, Okumoto and Crawford, 2010). Ipsative feedback in turn makes comments on how far the student has travelled from a previous level of performance, which is both more motivational for non-traditional learners and more likely to promote self-regulation in all students.

In a wide range of assessment scenarios, from professional practice (medicine for example) to distance learning, ipsative assessment and feedback could reduce the need for testing and retesting of skills. Instead of 'learning for the test', a process of continuous monitoring and self-regulation could make the acquisition of professional or vocational competences more authentic, rewarding and genuine, and enable tutors to devote more time and effort to mentoring."

(Marianne Sheppard and Ros Smith, http://jiscdesignstudio.pbworks.com)

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TAGS

assessment for learning • assessment scenarios • assessment techniquescomparison with previous performancecontinuous monitoring • continuous personal development • diagnostic assessment • Gwyneth Hugheshow far the student has travelledimprovement against past performanceipsative assessment • ipsative assessment and feedback • ipsative feedback • JISC Design Studio • Kaori Okumoto • knowledge and skills acquisition • learning and successlearning engagement • learning for the test • level of performance • Likert scale • longitudinal learning datamaking processmeasuring individual performancemeasuring instrument • Megan Crawford • motivational needs • non-traditional learners • performance-related disciplines • personal achievementpersonal bestpersonal improvement • professional competences • professional practicequality of achievementrunning score • self-regulation • student achievementstudent performance • vocational competences

CONTRIBUTOR

Simon Perkins
22 DECEMBER 2013

Global Technology Outlook 2013: Personalised Education

"An industry at the brink of transformation: The education industry is at the brink of an IT–enabled transformation. This transformation is driven by a demand for quality education that outstrips supply especially in the growth markets, misalignment between education and employment needs, and impatience with inefficiencies of education systems. For example, the government of Brazil is already funding students to go abroad because of a shortage of education infrastructure and quality educators. If growth continues to follow the existing trajectory, India will need about 800 more traditional universities than current levels today of about 350 universities.

Today, the most talked about application of technology to address these gaps is the advent of Massive Open Online Courses, or MOOC, which are growing rapidly. Several startups have emerged including Udacity, Khan Academy and Coursera, with millions of students enrolled across hundreåds of countries. Large amounts of new data are being created, which thus far is untapped for its potential.

What is Personalized Education: Education today is mainly delivered on a one size fits all basis. This is a key cause of the poor quality and inefficiencies associated with the industry. Educational institutions can learn from healthcare by drawing the parallels of doctors to educators, patients to learners, medicine/treatment to courses/learning, and payers to education loan providers. From a technology point of view, the use of electronic health data to form patient records, derive evidence, and provide patient–centric personalized healthcare can be extended to education, with the formulation of digital student records helping to inform and provide personalized learning pathways based on the capabilities of the learner and the desired outcomes.

Implications for the industry: The education industry is ripe for innovation, as new business models are instantiated on the emerging new sources of data, in particular the longitudinal learning data (tracking student information over multiple years in multiple schools). Predictive and prescriptive analytics will be applied to improve outcomes and efficiency. Clustering learners into groups, assigning new learners to existing clusters, identifying when a learner is deviating from a particular path are some possible outcomes. Prescriptive analytics would identify personalized learning pathways, track progress, and provide feedback to ultimately improve timely graduations and employability. Combined with industry demand data, supply estimates could be provided and targeted courses created with intakes tweaked to meet estimated demand. What will it take to succeed?: Ultimately there are many stakeholders who will be involved in improving education. This includes academic institutions, state education departments, students, learning management systems (LMS) and MOOC providers, government social service agencies and corporations. In order to achieve their often–shared goals, particularly to improve graduation and employment rates, they'll need to come together to create an open platform for sharing this data and insights from the analytics."

(William LaFontaine, 2013, IBM Research)

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TAGS

2013 • Alabama State Education • analyticsBrazilCourseradata integration • demand for quality education • Desire2Learn • digital student records • e-learningeducationeducation and employment • education industry • education shortage • electronic health data • healthcareIBM • IBM Global Technology Outlook (GTO) • IBM Research • IndiaKhan Academy • learning management systems • LMSlongitudinal learning dataMassive Open Online CoursesMOOCsone-size-fits-all solution • outcomes and efficiency • patient records • patient-centric • personalised educationpersonalised healthcarepersonalised learning • personalised learning pathways • predictive analytics • prescriptive analytics • Qatar Supreme Education • study abroadUdacity

CONTRIBUTOR

Simon Perkins
08 APRIL 2011

1st International Conference on Learning Analytics and Knowledge

Learning Analytics & Knowledge: February 27–March 1, 2011 in Banff, Alberta

"The growth of data surpasses the ability of organizations to make sense of it. This concern is particularly pronounced in relation to knowledge, teaching, and learning. Learning institutions and corporations make little use of the data learners 'throw off' in the process of accessing learning materials, interacting with educators and peers, and creating new content. In an age where educational institutions are under growing pressure to reduce costs and increase efficiency, analytics promises to be an important lens through which to view and plan for change at course and institutions levels. Corporations face pressure for increased competitiveness and productivity, a challenge that requires important contributions in organizational capacity building from work place and informal learning. Learning analytics can play a role in highlighting the development of employees through their learning activities."

(George Siemens, 2010–07–22)

TAGS

2011 • accessing learning materials • analytics • Banff • Canadacloud computing • cloud hosting • conference • creating new content • data mining • education data • educational institutions • electronic education data • enterprise settings • exchanging analytics • formal institutional boundaries • George Siemens • increased competitiveness • increased efficiency • increased productivity • informal learning • information flow • interacting with educators • interacting with peers • knowledge analysis • knowledge development • knowledge modeling • knowledge representation • learning activities • learning analytics • learning and knowledge work • learning institutions • longitudinal learning datamaking sense • making sense of data • myriad platforms • nascent field • networked learning • new models • novel insights • open dataorganisational capabilities • organisational capacity building • organisational effectiveness • organisational systems • pedagogical domains • personalised educationpredictive analytics • pressure to reduce costs • semantic web • serve the needs of stakeholders • social domains • social interactionssocial learning • technical complexity • workplace learning

CONTRIBUTOR

Simon Perkins
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