Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning

By Lu, Owen H. T.; Huang, Anna Y. Q. et al. | Educational Technology & Society, April 2018 | Go to article overview

Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning


Lu, Owen H. T., Huang, Anna Y. Q., Huang, Jeff C. H., Lin, Albert J. Q., Ogata, Hiroaki, Yang, Stephen J. H., Educational Technology & Society


Introduction

Blended learning, also known as hybrid learning or mixed-mode instruction, incorporates one or two learning strategies into traditional classroom teaching. In 1960, many computer programming courses relied on the Internet to deliver digital learning materials to students; for example, Programmed Logic for Automatic Teaching Operations, developed at the University of Illinois (Hart, 1995), provided teaching activities that could be conducted on a large scale to enable a single instructor to simultaneously teach a large number of students.

In recent years, blended learning has become a popular teaching strategy because of the development of data analysis and computation; for example, Ellis, Pardo, and Han (2016) integrated social networking into a one-semester course and monitored the behaviors of over 220 undergraduate engineering students. The researchers used the students' interactive records to examine how to help them succeed in a collaboratively driven course. Hong et al. (2016) adopted a web game to develop ten teaching scenarios. After 6 weeks of experimentation on 110 elementary school students, the researchers indicated that the students were highly motivated by the combination of game-based learning and traditional classroom activities. Huang, Yang, Chiang, and Su (2016) improved students' learning motivations and performance in an English course by incorporating a mobile-based vocabulary feedback application into a traditional classroom environment.

To gain benefits from blended learning, many educators have adopted the Online Assessment System (OAS) or Massive Open Online Courses (MOOCs) into their course design; for example, Awang and Zakaria (2013) integrated the OAS into an integral course for 101 college students. The results indicated that the OAS improved the students' learning performance. Lu, Huang, Huang, and Yang (2017) incorporated MOOCs into a course and the results showed evidence of a well-defined intervention strategy. The course not only facilitated the students' learning achievements but also increased their level of engagement. Although the aforementioned studies have explained the advantages of blended learning, many researchers have asserted that in blended courses, monitoring students' learning behaviors and habits is difficult because of the complex learning environment (Ellis et al., 2016; Hong et al., 2016; Huang et al., 2016). Furthermore, at- risk students cannot be identified, and thus timely interventions cannot be conducted to facilitate learning success (Tempelaar, Rienties, & Giesbers, 2015).

To help students achieve classroom success, educators in Europe and the United States have recently applied learning analytics. In 2011, Horizon Report, a report of educational trends, investigated the benefits and future trends of learning analytics (Johnson, Smith, Willis, Levine, & Haywood, 2011). The report defined learning analytics as an ideal framework to improve learning performance based on data of students' learning history. Because of the limitations of data analysis and computation, learning analytics has been considered as a conceptual framework since 2011. Because of the rise of big data technology, in 2016, a special issue of Horizon Report was released on learning analytics to highlight that the optimal time to incorporate learning analytics into classroom settings had arrived (Johnson et al., 2016).

In recent years, learning analytics has served as a conceptual framework for the analysis of course characteristics, and has included prediction of students' learning performance, educational data analysis process development (Hwang, Chu, & Yin, 2017), data collection, and timely intervention (Hwang, 2014). To develop a conceptual framework for learning analysis, many researchers have designed and implemented courses with strategies for learning analytics. Lu et al. (2017) measured student engagement in a virtual learning environment and intervened with the students' learning activities according to the engagement score. …

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