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Deep Learning

Posted on 18 May 2017

A previous post in this blog provided a brief introduction to Artificial Neural Networks, which describe adaptive, nonlinear information processing systems which attempt to loosely model the information processing capabilities of the brain by combining multiple processing units together with self-adapting, self-organising, and real-time learning behaviours (Ding, Li, Su, Yu, & Jin, 2013; Rojas, 2013).

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Using data analysis to inform learning and educational practice

Posted on 21 December 2016

Educational organisations worldwide are increasingly expected to understand student performance at an individual level so that they can intervene before a student falls behind or is at risk of dropping out prior to concluding their schooling. Legislative accountability requirements in many countries, such as the No Child Left Behind (NCLB) policy in the United States and initiatives implemented in Western Australia such as the School Improvement and Accountability Framework and the Western Australian Monitoring Standards in Education (WAMSE), have further exacerbated this trend.

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Robotics and STEM education

Posted on 26 October 2016

Over the last decade, the use of robotics in education has attracted significant interest from both teachers and researchers as a viable means by which to develop students’ cognitive and social skills and support learning across a number of areas (Alimisis, 2013; Bredenfield, Hofmann, & Steinbauer, 2010). Robots can serve as entertaining and engaging platforms from which students can learn about computers, mechanical engineering, electronics, and languages in a socially interactive learning context (Mubin, Stevens, Shahid, Al Mahmud, & Dong, 2013).

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Artificial neural networks

Posted on 31 August 2016

Artificial Neural Networks (ANN) are adaptive nonlinear information processing systems which attempt to loosely model the information processing capabilities of the brain by combining multiple processing units together with self-adapting, self-organising, and real-time learning behaviours (Ding, Li, Su, Yu, & Jin, 2013; Rojas, 2013).

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Comparing human analysis to autonomous natural language processing in the classification of learning outcomes

Posted on 27 July 2016

A previous entry in this blog provided an introduction to the Instructional Activity Matrix, which is a two-dimensional conceptual framework used to classify instructional statements based on the cognitive processes and types of knowledge they involve. The matrix structure provides a hierarchy of classifications across a Cognitive-Process and Knowledge Dimension, progressing from concrete to abstract, and lower order to higher order thinking respectively. This provides 30 possible individual classifications which can be applied to instructional statements describing learning outcomes, learning tasks, or assessment tasks.

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Games-based Learning

Posted on 17 June 2016

Games-based learning refers to learning that is facilitated through gameplay by combining the fun and playability of games with dedicated instructional elements to promote active, situated, and experiential learning (Benson, 2014, Tang, Hanneghan, & El Rhalibi, 2009). Here, the purpose of the activity is to engage and maintain the attention of learners by encouraging them to actively participate in gameplay that addresses a particular learning outcome (Razak, Connolly, & Hainey, 2012).

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Machine Learning

Posted on 12 May 2016

Machine Learning describes the application of autonomous computing processes for the purposes of learning a task based on logical or binary operations (Michie, Spiegelhalter, Taylor, Campbell, 1994). Computer systems which utilise machine learning approaches are designed to improve with experience through a process of inference, model fitting, or learning from examples, where useful information is automatically extracted from a body of data through the construction of probabilistic models (Ayodele, 2010).

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Learning Analytics

Posted on 28 April 2016

The emergence of new forms of educational media combined with advances in computer technology have provided expanded opportunities for improving learning processes through interrogation of the very large data sets which describe student interaction within online learning environments (Clow, 2013; Siemens & Baker, 2012).

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Sentiment Analysis

Posted on 11 April 2016

Sentiment analysis refers to the field of study concerned with analysing people’s opinions, evaluations, sentiments, appraisals, emotions, and attitudes towards specific entities such as products, services, issues, events, topics, and organisations, including their particular attributes, using computational approaches (Liu, 2012).

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TALIS – The OECD Teaching and Learning International Survey

Posted on 22 March 2016

The Teaching and Learning International Survey (TALIS) provides an evaluation of the working conditions and learning environments in schools situated in OECD countries. The survey queries both teachers and schools in relation to initial teacher education and professional development, the appraisals and feedback that teachers receive regarding their performance, the school climate and leadership, and teachers’ pedagogical practices and instructional beliefs.

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PISA 2016 report on low-performing students

Posted on 9 March 2016

Previous entries in this blog have reported on the Programme for International Student Assessment (PISA), which provides a snapshot of the global state of education amongst Organisation for Economic Co-operation and Development (OECD) nations. PISA administers a test every three years to a global sample of 15-year olds to assess their proficiency in maths, science, and reading.

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