• Disrupting the dissertation: linked data, enhanced publication and algorithmic culture

      Tracy, Frances; Carmichael, Patrick (SAGE Publications, 2017-09-24)
      This article explores how the three aspects of Striphas’ notion of algorithmic culture (information, crowds and algorithms) might influence and potentially disrupt established educational practices.  We draw on our experience of introducing semantic web and linked data technologies into higher education settings, focussing on extended student writing activities such as dissertations and projects, and drawing in particular on our experiences related to undergraduate archaeology dissertations. The potential for linked data to be incorporated into electronic texts, including academic publications, has already been described, but these accounts have highlighted opportunities to enhance research integrity and interactivity, rather than considering their potential creatively to disrupt existing academic practices. We discuss how the changing relationships between subject content and practices, teachers, learners and wider publics both in this particular algorithmic culture, and more generally, offer new opportunities; but also how the unpredictability of crowds, the variable nature and quality of data, and the often hidden power of algorithms, introduce new pedagogical challenges and opportunities.
    • Optimization analysis and implementation of online wisdom teaching mode in cloud classroom based on data mining and processing

      Gao, Jing; Yue, Xiao-Guang; Hao, Lulu; Crabbe, M. James C.; Manta, Otilia; Duarte, Nelson (International Journal of Emerging Technologies in Learning., 2021-01-16)
      The rapid development of Internet technology and information technology is rapidly changing the way people think, recognize, live, work and learn. In the context of Internet + education, the emerging learning form of a cloud classroom has emerged. Cloud classroom refers to the process in which learners use the network as a way to obtain learning objectives and learning resources, communicate with teachers and other learners through the network, and build their own knowledge structure. Because it breaks the boundaries of time and space, it has the characteristics of freedom, high efficiency and extensiveness, and is quickly accepted by learners of different ages and occupations. The traditional cloud classroom teaching mode has no personalized recommendation module and cannot solve an information overload problem. Therefore, this paper proposes a cloud classroom online teaching system under the personalized recommendation system. The system adopts a collaborative filtering recommendation algorithm, which helps to mine the potential preferences of users and thus complete more accurate recommendations. It not only highlights the core position of personalized curriculum recommendation in the field of online education, but also makes the cloud classroom online teaching mode more intelligent and meets the needs of intelligent teaching.