ETR&D

Although there seemed to be an optimistic consensus about the use of Information and Communication Technologies to support teaching and learning, the negative effects of introducing technology into the classroom have become apparent in recent years. Evidence suggests that instead of using technological devices for the purpose for which they were introduced into the classroom, students are distracted by simultaneous multimedia activities. This is known as the multimedia multitasking tendency. This is not the only negative consequence of digital media in education. In line with this phenomenon, some authors have found that reading comprehension is lower when reading digitally (on screen) than when reading analogue (on paper). The aim of the present study is to investigate whether Multimedia Multitasking Tendency in the educational context plays a relevant role in the effect of the reading medium (analogue or digital) on reading comprehension. To this end, the responses of 97 participants in whom Multimedia Multitasking Tendency was measured, as well as their reading comprehension, were analyzed. Half of them took the reading comprehension test in an analogue medium (i.e. on paper; n = 50), while the other half took it in a digital medium (i.e. on a computer or mobile phone; n = 47). The results suggest that reading comprehension accuracy is lower in a digital medium than in an analogue medium. The results of this study also suggest that Multimedia Multitasking Tendency may play a substantial role in the effect of the reading medium on reading comprehension.

With the rapid development of artificial intelligence, digital technology-based error correction significantly improves the efficiency of error correction by automatically identifying and clustering students’ errors and then analyzing the types of errors. However, in China’s whole class teaching, teachers still rely on experience to randomly select students with representative errors for error correction, making it difficult to ensure that all students’ errors are corrected in a timely and effective manner. In addition, in Chinese primary classrooms, high-achieving students and low-achieving students learn together in the same class, making it difficult to ensure that each student achieves the learning goals. Therefore, this study proposes a peer teaching-based digital error correction approach, focusing on its effects on primary school students’ learning performance, learning engagement, and perceptions of error correction. A total of 63 primary school students were recruited for the study, with 31 in the experimental group using the PT-DEC approach and 32 in the control group using the E-DEC approach. The results showed that students using the PT-DEC approach performed better than the control group in terms of learning performance and learning engagement. The results of this study validate the effectiveness of peer teaching in digital error correction and provide valuable insights and guidance for exploring more efficient error correction in the future.

Increasingly ubiquitous access to Generative Artificial Intelligence (GenAI) presents many challenges, but also opportunities. The fundamental capacity of GenAI to mimic and augment human cognitive functioning, sets it aside from the myriad of previous technological ‘cognitive tool’ innovations that have been promoted as supporting human thinking, problem solving and knowledge construction. Indeed, GenAI has the potential to play a far more substantive and interactive role in knowledge building, founded on real-time dialogic discourse between humans and GenAI working in symbiotic knowledge building partnerships. This article draws on Scardamalia and Bereiter’s early work on human knowledge building communities and Krathwohl’s revision of Bloom’s Cognitive Domain, reconceptualising these to theorise how humans and GenAI might partner in processes of collaborative, joint knowledge construction. It presents a unique model identifying three flexible ‘Zones’, representing different but overlapping components of knowledge building, aligned with Bloom’s cognitive dimensions. It identifies a possible ‘division of labour’ within and across Zones, but argues the primacy of innately human capabilities operating in the Judgement Zone, as crucial to reasoned decision making and accurate knowledge building. The model and its discussion provide new insights into how human-GenAI knowledge building partnerships might be established and sustained.