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How Are Learning Analytics Shaping the Roles of Teachers And/or Learners?

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Essay Title: How are learning analytics shaping the roles of teachers and/or learners?


Data is omnipresent and it is increasingly becoming indispensible in a growing number of fields. Education is no exception, in fact with the advent of technology to aid interaction between key stakeholders such as peers and educators allows for the opportunity to unravel various phenomenons occurring within the learning process. Experts have argued that it is due to the availability of data that education sector will be able to achieve sustained improvements by reflecting on the teaching and learning process through data analytics (Wright, 2014). However, there are also obstacles to realising the potential of data analytics. To illustrate, heads of education institutions will need to be vary of the sophisticated domain evolving around the idea of analytics. In the sense, they need to be able to select the approach and methods, apply them correctly in order to determine how learning process is taking place, how it can be further improved. Data analytics has tremendous potential in education, in the way that it can inform educators and students with fast feedbacks about the different pedagogical approaches. Due to this capacity, analytics is believed to be a disruptive force in education capable of redesigning models of modern pedagogy. At the same time, the precise role of data analytics will need to be identified in order to fully supplement current and future pedagogical models. This essay will seek to critically evaluate how data analytics is changing the role of teachers and students in education. We will attempt to illustrate the significance of data informed teaching methods, as well as current initiatives emerging in the field of analytics which could transform education. The essay concludes with a description of analytics models and initiatives in secondary education and also future possibility for analytics models in education.

Realising the Potential of Data analytics

The idea of using data for informing pedagogy and influence learning isn’t entirely new. In fact, data has been long used within education. However, its strategic application for gaining a better understanding how the learning process works and using that understanding to develop scalable and customised models for teaching has largely been either neglected or have been ineffective (Wasson, 2012). To illustrate, while there has been big amounts of data for coming up with metrics of student performance at both international and national levels, yet the understanding gained from this data is largely neglected in everyday pedagogical practises within schools. Until now, the role of data was restricted to informing policy making decisions within education sector. While the data informed policy decision has helped improve education sector, its direct effect on teachers and students are somewhat minimal.

However, in the last decade education sector has seen a shift in the use of data, from compliance and policy making purposes to its emerging role in continuous improvement. This aberration can be attributed to the evidence-led informed decision making approach on part of both policy makers and heads of education institutions. Data driven decision making entails collecting and analysing data in order to offer valuable insights with the potential to enhance learning and teaching process (Jackson and Mandinach, 2012). A key problem with data analytics faced across different organisation is not to do with the availability of data, rather how that is interpreted and presented to key stakeholders to help make informed decisions. Education sector in this regard is no different. To illustrate, education institutions in addition to requiring data sets on benchmarking assessments in their own institution are faced with the challenge of comparing it with the wider education community (other schools at their level). Being able to develop analytics to overcome this hurdle is critical to assessing the impact of teaching and learning strategies.

Challenges in application of data analytics within Education

Data analytics application in pedagogical approaches, peer assessment, developing curriculum, and corrective interventions combined with cost effective technical infrastructure become an education institutions resources for gathering valuable insights for informing institution’s future strategy and policy decisions. Likewise, educators will require data linked with student performance, effectiveness of learning and curriculum to improve their pedagogical approaches.

While there is an increasing support for data led strategies in education, there remains a widely accepted notion that educators will follow their past experiences and largely depend upon their intuition (Seimen et al 2011). This is to suggest that educators are insightful through their years of gained experience. They are capable to spotting and quickly reacting to situation in classroom relating to misconceptions or understanding of taught content. Arguably, this ability is gained and honed after years of teaching experience. The above ideas cannot be dismissed or discredited. However, applying data analytics to the years of experience can only enhance the educator’s pedagogical abilities and have a significant impact on the learning process. Proponents of data analytics argue that becoming an effective and good educator should not come at the cost of years of teaching experience (Romero et al, 2010). Applying data analytics and comprehending how learning process manifests via curriculum strategy are critical skills required by the modern day educators.

In spite of empirical evidence suggesting that a growing number of educators should include data analytics into their pedagogical approach, the evidence suggests that teaching as a profession is not yet fully capable of utilising the potential of data analytics techniques available today (Mandinach, 2012). The key advantage of using data analytics is the readiness of the solutions it offers to a given set of problems or issues. The critical challenge for data analytics application in education lies in the fact that data used is often lag data. This could perhaps explain the reluctance of educators in using it. In an administrative capacity however, the data collected for long term strategy for an educational institution could be considered appropriate and fit for purpose. At the same time, when dealing with student learning progression, that data may no longer be valid. It may be too late for the data to remain apt. In the process of evaluating peer self-learning there is a critical need to be able to quantify the qualitative and personalised feedback. Thus, enhancing the quality of the data collected for analysis.



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