Abstract
The theme of this issue of JETS is active learning. Active learning is a constructivist approach that differs from traditional teaching in that students gain control over their own learning, engaging the learning materials through writing, reflecting, discourse with others, and problem-solving activities. As noted in the National Research Council’s (2000) How People Learn, active learning is closely aligned with metacognitive approaches to learning that include sense-making, self-assessment, and reflection. A meta-analysis published in the Proceedings of the National Academy of Sciences (Freeman et al., 2014) indicates that active learning effectively increases exam scores and reduces the chances of failing in undergraduate STEM (Science, Technology, Engineering, and Mathematics) courses.
One popular way of introducing active learning into a course is to have students interact with a virtual world. In the first paper, the authors tested a Virtual Hybrid Learning model, where students are represented as avatars in a 3D world. The paper looks at interactions in the classroom and in the virtual world. A group of undergraduate computer science students were asked to build and then engage with educational virtual worlds. Observations of the students’ interactions, both inside the classroom and within the virtual environment, were recorded and analyzed. Overall, the authors discovered that this student interaction with virtual worlds enhanced student interactions both in the classroom and in the virtual world.
Beyond virtual worlds, virtual reality has become a popular medium for active learning. However, the literature on virtual reality includes very little guidance on how to incorporate this into a learning situation successfully. The second paper in this issue addresses that need. The authors looked at 35 papers describing learning applications using virtual reality and then categorized the underlying pedagogies apparent in those papers. This paper also suggests evaluation strategies which will be of use to educators.
Another common approach to active learning is to have students work collaboratively on group projects. Yet this raises the issue of how to form effective teams. The authors of the third paper used the approach of grouping together students who have similar personal learning styles, but different domains of expertise. Although classification of the students is done by the instructor, an algorithm developed by the authors sorts the students into appropriate groups. The methodology was tested with a group of undergraduate students learning Java.
Active learning can also be promoted by having students work on mobile devices in the classroom. As noted in the fourth paper, there are eight to ten million iPads in schools today. The purpose of this study is to examine teacher perceptions of this technology in K-5 classrooms in the first year of adoption. Using a mixed methods approach, the authors were able to identify both the problems encountered by the teachers and factors that led to success.
Related to the issue of active learning is active assessment. The last two papers in this issue focus on this. The fifth paper in this issue describes an intervention in which graduate students working on their theses were advised by professors using VoiceThread. The authors found that the students preferred the audio feedback and made good progress on their thesis work while using the technology.
The final paper summarizes the literature on data-enabled formative assessments. In particular, the authors categorized papers based on (a) the types of data being captured, (b) the methods used to process the data, and (c) how the findings are reported back to the student or teacher. Some very specific categories emerged. As a result, our readers can decide what formative assessments will best serve their own students.