Communication of Social Support in Computer-Mediated Groups for People with Disabilities

This study documented the types and extent of social support messages exchanged by persons with disabilities who participated in a computer-based support group. A modified version of Cutrona and Suhr’s (1992) social support category system was used to code 1,472 support messages. The largest percentage of these messages offered emotional and informational support, whereas network support and tangible assistance were least frequently offered. It appeared that many of the support messages directly redressed limitations and challenges associated with disability-related mobility, socialization, and self-care. Results are discussed in terms of the generalizability of existing category systems for coding support to this mediated context, the relative importance of different types of support in the communication of support group members, and the unique features of social support in mediated environments. The implications of this study for social support researchers, persons with disabilities, and human services professionals are also discussed

Maintaining Reading Experience Continuity Across E-Book Revisions

E-book reader supports users to create digital learning footprints in many forms like highlighting sentences or taking memos. Nowadays, it also allows an instructor to update their e-books in the e-book reader. However, e-book users often face problems when trying to find learning footprints they made in a new version e-book. Thus, users’ reading experience continuity across e-book revisions is hard to be maintained and seems to become a shortcoming within the e-book system. In this paper, in order to maintain users’ reading experience continuity, we deal with the transfer of learning footprints such as a marker, memo, and bookmark across e-book revisions on an e-book reader in a coursework scenario. We first give introduction and related works to demonstrate how researchers dedicated on the problem mentioned in this paper and page similarity comparison. Then, we compare three page similarity comparison methods using similarity computing models to compute page pairwise similarity in image level, text level, and image & text level. In the analysis, for each level, we analyze the performance of transferring learning footprint across e-book revisions and also the optimal threshold for similar page determination. After that, we give the analysis results to show the performances of three methods in image level, text level, and image & text level, and then, the error analysis is presented to specify the error types that occur in the results. We then propose page image & text similarity comparison as the optimal method to automatically transfer learning footprints across e-book revisions based on the analysis results and error analysis among three compared methods. Finally, the discussion and conclusions are shown in the end of this paper.PubMedScopu