The Equality Act 2010 consolidated and simplified UK anti-discrimination legislation in a number of important areas including race, age, religious belief and disability under the principle that in every aspect of life, everyone should have equal chance to use the same facilities, participate in the same activities and enjoy the same experiences.
In education, whether face-to-face classroom-based or online, inclusion is a significant matter and an essential factor in learners’ success. Despite the research predominantly relating to face-to-face classroom-based delivery, some educational specialists such as Banathy (1996) believe that the traditional system of education is not effective as it encourages passive learning and disregards inclusion, individual differences and learners’ needs. A major challenge for educators across all sectors is how to address the latter of these criticisms.
Even within a traditional face-to-face classroom-based education system, informally collecting and analysing even limited amounts of data is a long established practice of good teaching and can assist greatly in ensuring every student is viewed as an individual and consideration given to their needs. With the rapid increase electronic device use for educational purposes, the opportunities to collect this previously informal information have changed and both tutors and management teams need to look for other ways to obtain this information.
Romeo and Ventura (2007:137) quote many data sources including “traditional databases (with a student’s information, educator’s information, class and schedule information, etc.), online information (online web pages and course content pages), multimedia databases, etc.” and note that there is a growing interest in the automatic analysis of learner interaction data with web-based learning environments. Automatically analysing and meaningfully exploring this vast amount of data using a range of techniques and tools often referred to as ‘data mining’ can, according to Pelletier (2015), explain, predict and improve the learning process for students.
In the same way as with other educational establishments, FE colleges have large quantities of data available through the college database which can be combined with information from other sources, including informal information from tutors and support staff, in order to create a learning profile for each learner thereby offering an effective way to monitor progress, establish goals and highlight underachieving learners (NFER, 2005). Although care always has to be taken when making decisions about a student’s learning process, tracking progress and setting appropriate targets enables tutors to apply differentiation strategies and provide the right resources resulting in higher levels of engagement and inclusion.
Two key areas in achieving this objective are adhering to learners’ learning preferences and promoting social interaction between learners.
Learners are not homogenous and each react differently to different stimuli however the subject of learning preferences, or styles, remains controversial with some seeing them as narrow, almost fixed, preferences whereas others consider them on wide continuum along which learners move over time and as a result of environmental factors. Reid (2005:54) notes that “adhering to learning preferences… can prevent children from failing… [and is] important in an inclusive educational context where it can be very challenging to cater for the needs of all”. Analysing data on the number and length of interactions with different media types on both an institution-wide and individual basis will provide evidence of the effectiveness of different delivery strategies.
One of the many advantages of technology is the ease with which peer-to-peer interaction and support can occur; Koster et al (2009:117) conclude that “interaction between pupils (with and without special needs) is generally considered an important aspect of inclusion.” As each interaction leaves a digital trail this can be analysed to identify, and act upon, trends within the data relating to the cohesion of a group of learners.
References:
Banathy, B. (1996). Designing social systems in a changing world. New York: Plenum Press.
Koster, M., Nakken, H., Pijl, S. and van Houten, E. (2009). Being part of the peer group: a literature study focusing on the social dimension of inclusion in education. International Journal of Inclusive Education, 13(2), pp.117-140.
NFER, (2005). Schools’ Use of Data in Teaching and Learning. [online] Available at: http://webarchive.nationalarchives.gov.uk/20130401151715/http://www.education.gov.uk/publications/eOrderingDownload/RR671.pdf [Accessed 2 Jun. 2016].
Pelletier, S. (2015). Taming “Big Data”: Using Data Analytics for Student Success and Institutional Intelligence. [online] AGB. Available at: http://agb.org/trusteeship/2015/taming-big-data-using-data-analytics-for-student-success-and-institutional [Accessed 1 Jun. 2016].
Reid, G. (2005). Learning styles and inclusion. London: Paul Chapman Pub. Available at: Leeds University Library online
Romero, C. and Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), pp.135-146. Available at: Leeds University Library online
Digital Learning in Practice 