Adapting Learning Resources YAML

Overview

By using learning analytics, course content and learning resource provision can be adjusted during a session in order to better support student learning. Learning analytics can also inform effective learning design and arrangements in subsequent subject offerings. Content expertise alone is not enough to ensure student engagement and success. Having a good idea of what areas your students are mastering or what areas they may be struggling with, and at which point in the subject, are very important for teaching online. Data analytics can help provide you with these kinds of insights to prompt appropriate feedback, inform active interventions and make responsive adaptations to learning resources.

Engagement

As a primary function, this strategy seeks to improve learner engagement with subject content by seeking to measure its effectiveness and adapting the resources. Using analytics allows a student-centric understanding of the value of your digital content (e.g., What content is being used by students? How are they using the content?, etc.). This practice can also assist in developing learner-teacher engagement by providing data that can put you in touch with learners’ activities and progress. This may well lead you to make informed during-session changes that improve the effectiveness of content or learning activities for learners.

In Practice

Subject

PSC102 Botany

Teaching Staff

John HarperGeoff Burrows

Motivation

PSC102 Botany is a common subject in a number of Bachelor of Agriculture courses and provides a range of resources that, if fully utilised, will help students succeed in the subject. Information and insights on how students actually use the learning resources provided, and what resources students found to be useful is of immense value towards the development of a more personalised learning approach in a core subject.

Implementation

Data analytics has provided helpful insights into how students can be helped to succeed in the subject. Providing more personalised learning content, resources and support, has allowed a more responsive teaching practice and appropriately targeted academic interventions that focus on unengaged students, especially in the first couple weeks of teaching session, or for known problem areas of subject.

Guide

In simple terms, learning analytics is about better understanding of the online learning and teaching process, and interpreting student data to improve learning experiences and success. Analytics can be presented as summaries, visualisations and analyses of student data that could improve learning in multiple ways (Cooper, 2012; Long & Siemens, 2011). Student data is the trace data or data trails of students recorded within a number of online systems used at CSU, like Interact2, CSU Replay, Adobe Connect and Smart Sparrow.

Content analytics uses collected data for analysing different forms of learning content in order to understand or improve learning resources or activities. Data provides detailed visibility into courses (e.g. subject structure, digital content used) and can be used to discover student use and engagement patterns for content areas and core learning resources (Lockyer, Heathcote & Dawson, 2013, p. 1441). Any actionable insights then form the basis for making adjustments to learning resources and support (e.g. scaffolding) during a current session, and to inform learning resources and support provision in subsequent subject offerings. The end result is learner pathways that are more responsive to concept learning difficulties or to knowledge enhancement.

Analytics data can be augmented with information from students gathered, for example, from a subject feedback forum, or later in the Subject Experience Survey. Quantitative data, combined with qualitative information from students as to what areas they struggled with, allow teachers to make timely adjustments during a session and plan for more effective learning design arrangements in future subject offerings.

By way of an example, using Interact2 Site Analytics will provide you with self-service access to Content Access Statistics. This is one of five standard Site Analytics reports with a focus on learning, teaching and student engagement within a subject site. The Site Analytics reports are not static end-of-session historic reports, but instead enable a close-to-real time view of learner engagement and performance.

Adopting a data-informed adaptive approach to teaching can be realised in a variety of ways:

Tools

Data analytics are available in a number of systems used at CSU:

Additional Resources

Cooper, A. (2012). What is Analytics? Definition and Essential Characteristics, Vol. 1, No. 5. CETIS Analytics Series. Retrieved from http://publications.cetis.ac.uk/2012/521

Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439 –1459. doi: American Behavioral Scientist, 57(10) 1439 –1459. doi: http://dx.doi.org/10.18608/jla.2015.23.3

Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. Educause Review, September/October, 2011. Retrieved from http://er.educause.edu/~/media/files/article-downloads/erm1151.pdf

Toetenel, L. & Rienties, B. (2016). Analysing 157 learning designs using learning analytic approaches as a means to evaluate the impact of pedagogical decision making. British Journal of Educational Technology, 47(5), 981–992. doi: 10.1111/bjet.12423