It is expected that such technology-enhanced assessment systems meet a number of specific requirements, such as (a) adaptability to different subject domains, (b) flexibility for experimental as well as learning and teaching settings, (c) management of huge amounts of data, (d) rapid analysis of complex and unstructured data, (e) immediate feedback for learners and educators, as well as (f) generation of automated reports of results for educational decision-making. Other scenarios of technology-enhanced assessments include analyses of a learners’ decisions and interactions during game-based learning (Bellotti, Kapralos, Lee, Moreno-Ger, & Berta, 2013 Kim & Ifenthaler, 2019), scaffolding for dynamic task selection including related feedback (Corbalan, Kester, & van Merriënboer, 2009), remote asynchronous expert feedback on collaborative problem-solving tasks (Rissanen et al., 2008), or semantic rich and personalized feedback as well as adaptive prompts for reflection through data-driven assessments (Ifenthaler & Greiff, 2021 Schumacher & Ifenthaler, 2021). For example, technology-enhanced assessments may involve a pedagogical agent for providing feedback during a learning process (Johnson & Lester, 2016). While much effort in institutional and national systems is focused on harnessing the power of technology-enhanced assessment approaches in order to reduce costs and increase efficiency (Bennett, 2015), a range of different technology-enhanced assessment scenarios have been the focus of educational research and development, however, often at small scale (Stödberg, 2012). Technology-enhanced assessment systems enriched standard or paper-based assessment approaches, some of which hold much promise for supporting learning processes and learning outcomes (Webb, Gibson, & Forkosh-Baruch, 2013 Webb & Ifenthaler, 2018). However, evaluating, grading, and providing feedback on written essays are time consuming, labor intensive, and possibly biased by an unfair human rater.įor more than 50 years, the concept of developing and implementing computer-based systems, which may support automated assessment and feedback of written prose, has been discussed (Page, 1966). Valenti, Neri, and Cucchiarelli ( 2003) as well as Zupanc and Bosnic ( 2015) note that written essays provide learners the opportunity to demonstrate higher order thinking skills and in-depth understanding of a subject matter. Essays are scholarly analytical or interpretative compositions with a specific focus on a phenomenon in question. However, using natural language (e.g., written prose or essays) is regarded as the most useful and valid technique for assessing higher-order learning processes and learning outcomes (Flower & Hayes, 1981). Various forms of assessments exist, including single- and multiple-choice, selection/association, hot spot, knowledge mapping, or visual identification. KeywordsĮducational assessment is a systematic method of gathering information or artifacts about a learner and learning processes to draw inferences of the persons’ dispositions (E. This chapter describes the evolution and features of automated scoring systems, discusses their limitations, and concludes with future directions for research and practice. As a result, automated essay scoring systems generate a single score or detailed evaluation of predefined assessment features. Measurements of written text include observable components such as content, style, organization, and mechanics. Advances in automated assessment systems may facilitate the feasibility, objectivity, reliability, and validity of the evaluation of written prose as well as providing instant feedback during learning processes. They provide learners the opportunity to demonstrate in-depth understanding of a subject matter however, evaluating, grading, and providing feedback on written essays are time consuming and labor intensive. Essays are scholarly compositions with a specific focus on a phenomenon in question.
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