Assessment has been identified as one of the major challenges faced by Higher Education Institutions (Whitelock, et al, 2007). As a response to the challenge, in a project funded by the Joint Information Systems Committee (JISC) Open Mentor (OM) was developed as a learning support tool for tutors to help them reflect on the quality of feedback given to their students on assignments submitted electronically. Its development was based on the fundamental theory that there was convincing evidence of systematic connections between different types of tutor comments and the level of attainment in an assignment (Whitelock, et al 2004). OM analyses, filters, and classifies tutor comments through an algorithm based on Bale’s Interaction Process. As a result, tutor’s feedback comments are classified into four categories namely: Positive reactions, Teaching points, Questions and Negative reactions. The feedback provided is analysed against an ideal number of feedback comments that an assignment given a mark of a specific band should have. Reports are provided in OM to support tutors in the task of reflecting on their feedback structure, content and style.udThe JISC-funded Open Mentor technology transfer (OMtetra) project is continuing the work initiated by the Open University implementing OM at the University of Southampton and King’s College London. OMtetra aims at taking up OM and extending its use by developing the system further and ultimately offering better support to tutors and students in the assessment process. A group of tutors from the University of Southampton and Kings’ College are at present using OM in their teaching and assessment. In this paper, we explore potential improvements to OM in three aspects: user interface, technology implementation and analysis algorithm design.udFor the user experience aspect suggested additions to OM include the creation of a simple entry form where tutors may validate the results of the analysis of the feedback comments. In addition, enhancements to OM will facilitate uploading of students and modules information into the system. Presently, OM utilises a built-in database of users that needs to be maintained separately from institutional systems. Improvements for this system feature include a more flexible authentication module which would simplify the deployment of the system in new environments and thus promote uptake by a larger number of institutions. In order to reach this goal, the system will be migrated to an open source framework which provides out-of-the-box integration with various authentication systems. The last to improve is the analysis algorithm. Currently, OM classifies tutors’ comments into four categories by applying an underlying text matching algorithm. This method could be improved if tutors are allowed to confirm comments’ classification through the OM interface and a free-text classification algorithm. As the number of users grow, so will the algorithm and analysis process, making it more comprehensive and intelligent as the keywords used during analysis are dynamically expanded. udOMtetra is an on-going project with a lot of potential. We believe that the outcomes from the development and trial implementations of OM will contribute highly to the area of assessment in higher education.ud
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机译:评估已被确定为高等教育机构面临的主要挑战之一(Whitelock等,2007)。为了应对这一挑战,在联合信息系统委员会(JISC)资助的一个项目中,开发了开放导师(OM),作为导师的学习支持工具,以帮助导师反思所提交作业对学生的反馈质量电子地。它的发展基于这样一个基本理论,即令人信服的证据表明,不同类型的导师评论与作业的完成水平之间存在系统的联系(Whitelock等,2004)。 OM通过基于Bale交互过程的算法对教师评论进行分析,过滤和分类。因此,导师的反馈意见可分为四个类别:正面反应,教学要点,问题和负面反应。针对给定特定频段标记的分配,应针对理想数量的反馈意见对提供的反馈进行分析。 OM中提供了报告,以支持导师思考他们的反馈结构,内容和风格。 ud由JISC资助的开放导师技术转让(OMtetra)项目正在继续由开放大学发起的在大学实施OM的工作。南安普敦和伦敦国王学院。 OMtetra旨在通过进一步开发该系统并最终为评估过程中的导师和学生提供更好的支持来使用OM并扩展其使用范围。目前,南安普敦大学和国王学院的一群导师正在使用OM进行教学和评估。在本文中,我们将从三个方面探讨对OM的潜在改进:用户界面,技术实现和分析算法设计。 ud对于用户体验方面,建议对OM进行的补充包括创建一个简单的输入表单,在此表单中,导师可以验证该结果。分析反馈意见。此外,OM的增强将有助于将学生和模块信息上传到系统中。当前,OM使用需要与机构系统分开维护的内置用户数据库。此系统功能的改进包括更灵活的身份验证模块,该模块将简化在新环境中的系统部署,从而促进更多机构的采用。为了实现此目标,系统将被迁移到一个开源框架,该框架提供了与各种身份验证系统的现成集成。最后要改进的是分析算法。目前,OM通过应用基础文本匹配算法将教师的评论分为四类。如果允许教师通过OM界面和自由文本分类算法来确认评论的分类,则可以改进这种方法。随着用户数量的增加,算法和分析过程也将随之增加,随着分析过程中使用的关键字的动态扩展,算法和分析过程将变得更加全面和智能。 udOMtetra是一个正在进行的项目,具有很大的潜力。我们认为,OM的开发和试用实施所产生的结果将为高等教育评估领域做出巨大贡献。 ud
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