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Learning Analytics

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Analytics have always been used in the commercial sector to identify trends in consumerism but learning analytics has become a major technology for the education sector (see Campbell et al, 2007, Colvin et al, 2015, Cooper, 2012a). Some researchers have viewed learning analytics as ‘big data’ being applied to education. Many reports have indicated that learning analytics as a terminology owes its name to analytics as applied in business (see also Dawson et al, 2014, Dyckhoff, 2013).

Learning analytics is essentially data-driven. So the interest in learning analytics is actually interest in ‘Big Data’. Big data refers to the collection of complex and large data sets that are difficult to process utilising traditional data processers (see also Dawson et al, 2014). This is about information generated from and for educational settings. Over the years, there has been an increase in the use of analytics in diverse sectors to solve problems and provide solutions, especially the determination of trends in different businesses. Businesses want to get ideas about purchasing patterns in order to realign their marketing campaigns and make them more effective. Some businesses want to target the spending patterns of individuals and know how to manage their stock levels (Campbell et al, 2007).

As an approach, Learning Analytics applies data analytics principles to student learning (Showers 2014). The goal is to offer actionable and accurate insights into the learning process through the aggregation, modelling and exploration of essential data sources and to offer evidence that demonstrates that learning has been enhanced (Ferguson et al 2017).

Learning analytics makes use of data from diverse sources for its research. Data can be collected from students’ attendance, students’ use of the library, students’ information systems, students’ participation in forums online and even students’ biometric information as well as data from how students interact in virtual learning environment (see Ferguson 2012).

VLEs or virtual learning environments are important sources of data for learning analytics. Popular VLEs or online platforms such as Sakai, Moodle and Blackboard have become enormous sources of data for learning analytics. VLEs contain huge resources for tutors and teachers. Through VLEs students’ access past examination question papers, notes, books and other learning resources. VLEs are very important platform and have become the mainstay of education worldwide.

VLEs have therefore become important mines for sources of useable data for learning analytics researchers. VLEs provide information on how students access resources, what resources the access, the time students spend on the resources and the resources students use more often. Thus, VLEs provide information on the learning behaviour patterns of students and points how the pattern is being developed or can be developed.

With the help of learning analytics, lecturers have valuable information and insights into the resources that their students are accessing, how they use the information and how active the students are online. Students too can have insights into how engaged they are in their studies in comparison to their course mates. Learning analytics offer information in real time. In this way, both lecturers and students can receive timely information and act based on the information early if necessary.

Comparing information on students learning behaviour styles to information on students grades, learning analytics researches are able to identify which activity patterns are more useful, effective, leads to deep learning and offers the best outcome for students (see Lockyer et al 2013). Researchers using learning analytics are also able to identify learning behaviour patterns that are not helpful to students and leads to failure or withdrawal from the course of study. With the information from learning analytics, lecturers can tell which students are unlikely to succeed and so they can intervene early enough to assist the students to change their learning behaviour styles thus avoiding bad consequences.

Learning analytics offer lecturers and students information that can be useful in identifying potential problems and recommends ways of avoiding failure (see Ferguson et al 2012). Students become aware of effective learning behaviour styles and lecturers have insights and can guide students to use patterns that will likely lead to academic success.

Learning analytics can also be used as pastoral tools. Information from learning analytics platforms can be useful in discovering students who may have financial, social, medical, emotional or personal problems. Staff who use learning analytics will be able to provide helpful intervention support to students who have personal or emotional needs.

Learning analytics is a great tool for the provision of insights and answers to questions that may never have answers except with data. Lecturers and even students want to take effective decisions and deal decisively with issues (see Ferguson et al 2015). Using learning analytics provide quantifiable information which can assist in the strategic decision making. Learning analytics may not provide all the answers but it could be great in offering strategies and information that will deepen and enhance learning.

E-learning at HMS Schools, Kaduna, Nigeria, where I have worked as Head of Schools, started in September, 2017. It was developed jointly by our in-house staff and a local IT company. We use a Moodle LMS site. We have been able to upload about ten courses on the site for students. The essence of the Moodle site is to enhance and support the face-to-face instruction our students receive in the traditional classrooms. A computer laboratory was built specifically to support e-learning and teaching. Many students and staff like to work in the computer lab but access to Moodle can be from any digital device on or off campus. Thus, both staff and students have welcome Moodle LMS as a new and useful online learning environment. With the design on Moodle, students and staff can access the courses by clicking on the ‘My Courses’ block on the website. ‘My Courses’ tab displays a list of all the ten courses when it is clicked.

Knowing or understanding the behaviour of students in an online learning environment such as Moodle can be a huge challenge. However, if there is a need to provide an eLearning experience that provides help and support for students to achieve their goals and objectives. Students should benefit optimally from all courses offer in school, especially when you are using the blended learning approach (see Scheffel et al 2014).

The figure (1) below is a screenshot display of the Learning Analytics Enhanced Rubric environment.

To achieve the aim of getting students to learn effectively, one of the most efficient ways is to collect data. Learning analytics is an effective approach for data collection and analyses. To effectively incorporate the approach of learning analytics we have used a plugin for the Moodle LMS called Learning Analytics Enhanced Rubric. Learning Analytics Enhanced Rubric is an example of a tool for descriptive learning analytics. As a tool, Learning Analytics Enhanced Rubric uses a range of student data that provide evaluation support for the teacher. Teachers are able to evaluate the performance of students in diverse learning and assessment tasks.

Learning Analytics Enhanced Rubric generates reports that displays the performance patterns of all students individually. Teachers can efficiently and effectively evaluate students holistically using sundry data. Teachers are able to use data to assess performance indicators (see Scanlon et al (2013).

Using the Moodle plugin, Learning Analytics Enhanced Rubric, we have been able to collect data, analyse the data and generate information which has become quite useful in our quest to engage students meaningfully in eLearning experience. With this rubric, information is reported. With the rubric, we have recorded important pieces of data. These have included students’ score in test and activities done on the Moodle. It has become much easier to ascertain the progress that students are making on the courses. We can see how many times they log in and how they have participated in the discussion boards on Moodle. Teachers have a comprehensive overview on how students are performing and can immediately decide if they need to give students extra or additional support. Teachers can also tell how students are progressing and if they are likely going to pass the course. This rubric makes educational analysis and predictions a lot easier. It has become easier for our teachers to tell which learning materials are more appropriate, relevant or useful. Learning Analytics Enhanced Rubric provides data in the area of learners’ skill set, interest, level and performance (see Macfadyen and Dawson (2012).

One benefit of the Learning Analytics Enhanced Rubric as a learning analytics technology is that it offers rich insights into learners past, present and future performance. This helps teachers to plan and personalise teaching in such a way that lessons are tailored more creatively to support individual students on the course. Using this rubric technology this past year, our teachers have been able to determine the type of supplementary learning materials to use. This has led to higher grades and much more meaningful learning experience.

In the past year, our teachers in HMS Schools have gained expertise in the area of personalised lessons. To use our in-house language, teachers are now teaching individuals instead of teaching classes so lessons are custom tailored to provide rich learning experiences to each individual learner. When there is an indication that students are having challenges or struggling, customised eLearning resources and educational tools are provided immediately by the teachers to forestall dismal performance. This could be a provision of useful websites, video or books. With the use of this learning technology, our teachers demonstrate that every learning experience is useful and must meet the needs of specific individuals.

We have reduced failure rates drastically as a result of using Learning Analytics Enhanced Rubric as a plugin within Moodle. The performance of our students have been enhanced because of the intervention that teachers are now able to perform. Although we are in northern Nigeria which is regarded as an educationally less developed region, we are not having drop outs like many other schools. There is a decrease in the dropout rate.

The rubric has helped our teachers generally in terms of pedagogy, lesson planning, and schemes of work and curriculum design. Teachers are able to easily adjust based on information generated about areas of difficulty for students. The goal is always to make learning materials and lessons more impactful and powerful. With this plugin within Moodle, our teachers have an in depth knowledge of how students acquire information, which aspects are successful as well as how students’ learning resources are being used. Teachers then can choose to change, improve or modify the learning materials (Long and Siemens 2011).

Thus, learning analytics has changed the world of learning in HMS Schools, Kaduna, Nigeria. With the data collected and the information generated using our learning analytics technology, our teachers are highly professional educational services and many stakeholders have observed that our teaching and learning is unparalleled in this region.

Learning analytics as a process involves employing data to improve teaching and learning (see Slade and Prinsloo 2014). Learning analytics is, therefore, used to report on learners’ progress and the environment in which learning happens based on data collected, measured and analysed.

In Kavod Institute of Professional Studies, Nigeria, Learning analytics had been used to improve my teaching while I taught Use of English and Communication Studies to some students on a professional course. I made use of Moodle to deliver the course. One of the first things I realised was that the value of dashboards for facilitating my lectures and course provision was immensely helpful. Using Moodle with the plugin, Learning Analytics Enhanced Rubric was a big innovations. It led to improvements in my students’ performance. Some of my students even reported that they went on to perform better in future courses.

The use of learning analytics furnished me with information on quality and substantial educational activities and content on Moodle. I also got exposed to the assessment and teaching processes on Moodle.

It was also easy for me to collect data on my students’ potential experiences. This helped me to identify and deal with many leaner issues such as the absence of feedback. The relationship between my students became better as a result of this intervention made possible by the use of learner analytics technology. I also used the information generated from learner analytics to monitor students’ performance and this helped me to adapt my tutoring to meet students’ needs.

Learning analytics had a broad institutional impact on my teaching and the support I provided for my students. Using the rubrics in Moodle, I developed an analytics mind-set which was of immense help. My decisions became increasingly based on evidence. In a short while, I became part of a culture of data-driven professionals.

As a student with Open University UK, I have interacted with the university online library and I have used the Open University (OU) virtual learning environment for tutorials, submission of assignments, forum discussions and access to course materials/resources. Based on what I know understand from this course, I know I have always left behind my digital footprint.

Learning analytics has had a huge impact on my learning. It has enabled me to take control of my own learning (see Norris and Offerman (2009). Since I became a student of the OU, it has been easy for me to track my progress and to know what I need to do to meet my educational goals. I know the course options available for me to take before I complete my MA ODE and I have received ideas for career choices. The grades for past modules are accessible and I know the pathway I am on.

As an OU student, I have had a very positive experience using learning analytics. Analytics have provided me with an opportunity to take control of my own learning. My experience with OpenLearn courses too have been tremendous. Instant quiz results have been great.

The dashboard on OpenLearn and FutureLearn courses have always given me signals about my performance. This indications of my current progress have been expedient.

Learning analytics has changed the manner in which educational outcomes and impact are measured in all learning contexts. Educational providers are using learning analytics to develop new ways of achieving success in education. Students now have a great opportunity to achieve more in education.

Although learning analytics is at its infant developmental stage, it is a growing phenomenon and various research findings are confirming its validity, usability and authenticity (see Ferguson et al 2017). There is a growing consensus that learning analytics will raise the standard of education delivery and consistently provide tools and information that schools and colleges will always need for overall improvement.

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