Data Analytics in improvement of learning is often referred as learning analytics.
It basically is the collection and analysis of student and teacher data for the purposes of improving learning and teaching. It’s about capturing, analyzing and representing a variety of data to support students, teachers and institutions in adapting their practice/behaviors in order to be more effective.
The development of computerized learning modules enables assessment of students in systematic, real-time ways. Data mining and data analytic software can provide immediate feedback to students and teachers about academic performance. That approach can analyze underlying patterns in order to predict student outcomes such as dropping out, needing extra help, or being capable of more demanding assignments. It can identify pedagogic approaches that seem most effective with particular students.
With Data Analytics in education, providing feedback is a consistent practice. Feedback plays an important role in improvement of students’ learning. With the right feedback students can improve their results and know where exactly they need to put that extra effort. Data analytics is of utmost importance considering the importance of feedback.
With the advent of computerized instruction, scholars argue that the specific types of feedback are crucial for improving learning. In a report by Darrell M. West on Big Data for Education: Data Mining, Data Analytics, and Web Dashboards, he mentions of the following arguments by some of the practicing scholars:
For example, “David Nicol and Debra MacFarlane-Dick outline seven principles of effective feedback. They include clarifying what good performance is, facilitating self-assessment in learning, delivering high quality information to students, promoting peer dialogue around learning, encouraging positive motivations, showing how to close gaps between current and desired performance, and providing information to teachers on effective feedback.
It is possible to take these principles and evaluate learning in more detailed ways. Vincent Aleven and his colleagues at Carnegie Mellon University run controlled experiments through Intelligent Tutoring Systems. These experiments provide tools through which professors can develop online tutorials in areas such as chemistry and physics, and compile pre-test and post-test assessments plus detailed records of interactions between students and electronic tutors.
These types of computer tutorials can evaluate problem-solving approaches and provide feedback along the instructional path. The system sends error messages if the student follows an incorrect approach and provides answer hints if requested by the student. Instructors can get a detailed analysis not just of whether the student reached the final answer correctly, but how they solved the problem.
Research by James Theroux of the University of Massachusetts at Amherst found that embedded assessment “engages and satisfies students at a higher level than do average courses and presents a more realistic and integrated view of business decision making.” A clear majority of pupils preferred the online over a traditional approach and felt the course materials were very applicable to real life. The cases helped faculty assess the degree to which students grasped management principles and gave them an opportunity to apply student feedback based on actual corporate experiences.”
Pointers below mentions of potential of big data education and how it provides a variety of opportunities to improve student learning:
– Individualizing a student’s path to content mastery, through adaptive learning or competency-based education
– Better learning as a result of faster and more in-depth diagnosis of learning needs or course trouble spots, including assessment of skills such as systems thinking, collaboration, and problem solving in the context of deep, authentic subject-area knowledge assessments
– Targeted interventions to improve student success and to reduce overall costs to students and institutions
– Using game-based environments for learning and assessment, where learning is situated in complex information and decision-making situations
Also, bringing teachers into the “big data” discussion is crucial because they are the ones, along with parents and students, who will benefit from advances in research and analysis. Projects that let teachers know which pedagogic techniques are most effective or how students vary in their style of learning enable instructors to do a better job. Tailoring education to the individual student is one of the greatest benefits of technology and big data help teachers personalize learning.
Other Resources You Must Check:
The Predictive Learning Analytics Revolution Leveraging Learning Data for Student Success by ECAR Working Group Paper. ECAR working groups are where EDUCAUSE members come together to create solutions to today’s problems and provide insight into higher education IT’s tomorrow.
How data and analytics can improve education: George Siemens on the applications and challenges of education data.
Why Learning Analytics Are So Important To Improve eLearning: This mentions of 5 key reasons on why learning analytics are important to have better education delivery and results.
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