Nevertheless, it shows how such architecture can foster Proposes a service-oriented flexible and interoperable architecture for futuristicĮ-assessment systems. The importance of flexible and interoperable e-assessment. To be utilised in order to facilitate this integration. Moreover, flexible and interoperable e-assessment architecture has Have to have a flexible design to be integrated within the context of theĮ-assessment system architectures taking advantages of software-as-a-serviceĪrchitecture. However, utilising free-text answers assessment tools separatelyįorm the learning environment may increase the staff workload and increase theĬomplexity of the assessment process. The rationales behind using computers to assist learningĪssessment are mainly to save time and cost, as well as to reduce staff Underpinned techniques has caused those tools to be designed and developed as Several free-text answers assessment tools underpinned byĭifferent techniques have been developed. Following this approach, we are targeting to cope with one of the main challenges in current massive open online courses, which are expected to provide personalised education to an increasing number of students without the continuous involvement of educators in supporting learners during their course interactions.įree-text answers assessment has been a field of interest during the These applications show benefits for learners and educators. To illustrate guidelines actual utility, generality and flexibility, the paper describes their applicability to design educational recommendations in two different contexts, which in total involved 125 educators and 595 learners.
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These guidelines integrate three different methodologies: i) user centred design as defined by ISO 9241-210, ii) the e-learning life cycle of personalised educational systems, and iii) the layered evaluation of adaptation features. This paper compiles practical guidelines to produce personalised recommendations that are meant to foster active learning in online courses. They have to be both based on educators' experience and perceived as adequate by learners. There is a need for designing educationally oriented recommendations that deal with educational goals as well as learners' preferences and context in a personalised way. Overall, our results on a real-world corpus demonstrate that 1) leveraging student and question models to the conventional answer-based approach can greatly enhance the performance of ASAG, and 2) deep learning models such as DBN can be productively applied to the task of ASAG. In this work, we explore the effectiveness of a deep learning model, deep belief networks (DBN), to the task of ASAG.
While classic machine learning methods have been widely employed to ASAG, as far as we know, deep learning models have not been applied to it probably because the lexical features from short answers provide limited information. In recent years, various deep learning models have gained great popularity in a wide range of domains. ASAG is the process of applying natural language processing techniques to assess student-authored short answers, and conventional ASAG systems often mainly focus upon student answers, referred as answer-based.
In this work, we leverage the classic student and domain/question models that are widely used in the field of intelligent tutoring systems to the task of automatic short-answer grading (ASAG). As various educational technologies have rapidly become more powerful and more prevalent, especially from the 2010s onward, the demand of automated grading natural language responses has become a major area of research.
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