When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction

Abstract

Adaptive learning systems personalize instruction to students' individual learning needs and abilities. Such systems have shown positive impacts on learning. Many schools in the United States have adopted adaptive learning systems, and the rate of adoption in China is accelerating, reaching almost 2 million unique users for one product alone in the past 3 years. Given such rapid adoption in China, it is useful to examine the efficacy of adaptive learning within that country's educational system. This study aimed to compare the learning impacts of individualized adaptive learning courseware to two common instructional approaches in China: large-group and small-group classroom instruction. This paper describes the results of two efficacy studies of one of China's first adaptive learning systems, Squirrel AI Learning. One study compares classroom-based individualized adaptive learning instruction to largegroup instruction, and another to small-group instruction. Chinese eighth-grade students from two provinces randomly assigned to use Squirrel AI Learning showed greater gains on a mathematics test than those randomly assigned to whole-class or small-group instruction led by expert teachers. Findings provide a basis for further research into the selection, use, and impact of adaptive learning systems in Chinese education.

Learning Performance in Adaptive Learning Systems: A Case Study of Web Programming Learning Recommendations

“…Adaptive instruction can be defined as adjusting instructional plans provided by instructors in their instructional process according to students' learning differences. In other words, adaptive instruction can provide a personalized learning process according to students' needs (Wang et al, 2020). When using adaptive instruction, instructors have to design and revise their instructional plans according to students' differences and learning progress and modify their instructional modes to cope with the diverse needs of individual students.…”

Integrating instructional designs of personalized learning through the lens of universal design for learning

“…For example, two studies examined how mobile learning systems were designed to help activate learners' relevant prior knowledge (Song et al, 2012) or automatically link to pre-requisite information (CP3.1; Hwang, Tsai, et al, 2012). Aligned to CP3.2, some adaptive learning systems were able to provide functions such as underlining, drawing on figures (Arroyo et al, 2014), and highlighting text with varying colours (Siddique et al, 2018) Abawi, 2015;Admiraal et al, 2020;Alcoholado et al, 2011;Basham et al, 2016;Kim et al, 2019;Looi et al, 2009;Martín-SanJosé et al, 2014;McCarthy, Liu, & Schauer, 2020;Phan, 2020;Phillips et al, 2020;Schmid & Petko, 2019;Song et al, 2012;Tsybulsky, 2020); Provide autonomy in improvisation to create meaningful contexts (Looi et al, 2009); Allow student to choose which mathematical task topics based on interest (Clinton & Walkington, 2019;Høgheim & Reber, 2015); Incorporate learner-selected vocabulary into learning content (Choi & Ma, 2015); Allow for customization of online learning texts with choice background colour, picture, font style (Ertem, 2013) Siddique et al, 2018;Standen et al, 2020;Su et al, 2006;Troussas et al, 2020;Tseng et al, 2008;Wang et al, 2020); Learning systems automatically return exercises that are slightly more difficult than the student's current understanding level (Cornelisz & van Klaveren, 2018) or items/learning activities that students need additional practices (Wang, 2014;Wongwatkit et al, 2020;You et al, 2019) Virtual Reality: Customize learning path with which students can interact constantly (Bhattacharjee et al, 20...…”

Ready, trainer … one*! discovering the entanglement of adaptive learning with virtual reality in industrial training: a case study

“…Next to technological developments, novel educational approaches are readily available to further enhance industrial training. Recently the educational technological research community highlighted technology-enhanced adaptive learning to be a key learning paradigm (Kenny & Pahl, 2009;Wang et al, 2020;Xie et al, 2019) as it offers the opportunity to first determine user strengths and weaknesses and consequently adapt the amount of instruction the user needs to receive (Martinetti et al, 2020).…”