iRead will develop and evaluate new ways for electronic publishers and libraries to select appropriate learning materials.
Digital libraries of texts and e-books offer new opportunities for access and informal learning. However, they also pose challenges in terms of how material is chosen and presented to the student. The selection of teaching materials that are appropriate for a given student group is crucial to supporting the effectiveness of a teaching intervention. Materials given to students, especially texts, need to correspond to their learning needs and language skills, in order to provide them with the opportunity to improve their learning and enhance motivation and self-confidence. An appropriate text needs to be challenging enough to stimulate students cognitively, but it also needs to include information that can be handled easily to assist comprehension and foster students’ sense of accomplishment and self-confidence.
iRead will develop a ‘content classification component’ that operates when students or teachers are accessing a large library of texts and books to match texts to the linguistic knowledge of the student. Current algorithms for choosing learning materials are often limited to measuring characters per word, words per sentence, sentence and paragraph statistics. iRead will advance previous metrics by developing novel metrics that consider syntax and linguistic characteristics corresponding to individual students’ skills (as encoded in user-models) to provide ranked recommendations of reading materials.
iRead will develop new adaptive algorithms that respond to the learner’s interactions in the apps and teacher tools to orchestrate the use of the apps (games, e-books and e-Reader app)
The games will include an adaptive mechanism. The mechanism will suggest specific game activities and content that are suitable for each student. The mechanism will take into account the student’s skill level, their performance, usage history, preferences, and the particular learning objectives for a lesson and for a given student.
We will design digital teacher tools for the formative assessment of reading. The tools will help teachers take an orchestrated approach to using the different learning applications allowing them to create and formatively evaluate different learning journeys across the iRead apps. Three teaching use cases will drive our research: (1) provide teachers with fine grained data about reading processes to foster individualised formative assessment (2) provide teachers with holistic data about the reading process and the conditions to learning to inform their lesson planning (3) provide teachers with dynamic data on student progress for making decisions on the fly during a classroom session.
iRead will integrate three distinct learning apps (games, e-reader app, e-books) designed to reinforce the same literacy skills in different ways and offer unique opportunities to support the development of differentiated skills. A model of the student that personalises and adapts learning across apps will provide an integrative learning experience. Designed learning trajectories will also support a connected pedagogy across apps that will be managed through digital teacher tools for formative assessment.
Link with industry: The outcomes of this activity will inform Knowble‘s adaptive language solution