Text Processing

Previous research seems to support the assumption that students need instructional guidance to activate and correct their preconceptions. Such an instructional strategy is the CONTACT strategy, characterised by continuous, computer-assisted activation of the conceptions of individual learners. Our previous study showed that the CONTACT strategy was effective in promoting conceptual change in text processing (domain: physical geography) because students (fifth-and sixth-graders, primary education) constructed more correct conceptions. However, students mainly seemed to focus on the central concepts from the training texts, disregarding other information. Therefore, the strategy was adapted to solve this problem of selective attention and to increase its effectiveness. Subjects (74 fifth-and sixth-graders) were assigned to three instructional conditions (original CONTACT condition, revised CONTACT-2 condition and control condition NO ACTIVATION). A mixed between-within-subjects design was used with 2 between-subjects factors (instructional condition and students' familiarity with the central concepts from the 7 texts used). Dependent variables concerned quality of conceptions and learning performance. Students from the CONTACT-2 condition constructed better conceptions and achieved higher learning performance scores than students from the other two conditions. Moreover, the effectiveness of the CONTACT-2 strategy appeared not to depend on the degree of conceptual resemblance between the performance test questions and the central concepts from the texts and on the moment of testing. Additional research should shed some light on the instructional conditions required to teach students how they themselves can initiate and perform learning activities aimed at conceptual change.

The inspiration for this research paper was the natural bias in university paper checking. When a paper is checked it is either checked by a professor who teaches the subject or someone who has no knowledge of the subject. When checked by the latter type, the answers cannot be appropriately marked unless obviously highlighted. This paper aims to check long answers without human intervention using artificial intelligence and regular expressions. It checks student or examinee written digital form answer by comparing it to an answer key which is to be provided by the exam host. The proposed methodology allows doing so by combining two techniques to get a faster and more accurate system to check long answers. The long answers will be evaluated by breaking them to simplest form of sentences and then encoding them to high density vectors using a Deep Averaging Network (DAN) to analyses the semantic similarity of the examinees answer to the provided answer key. This system does not look for only keywords in the content of the answer but looks at the sentence as a whole and if it evaluates similarly to the content in the answer key. This research relies on the availability of an answer key to check answers and does not check the relevance of content written by the examinee, meaning as long as examinee writes points mentioned in the answer key, he/she will be marked correct. This system of evaluation doesn't cut marks for wrong point (meaning no negative marking).

Students are often provided with instructions that are intended to influence their attention to particular sections or elements of their reading materials. To date, the bulk of the work on such prereading instructions has focused on drawing reader attention to relevant text information. In the current project, we examined whether instructions might also be useful in helping readers ignore irrelevant (albeit inherently interesting) information in text. In two experiments, prereading instructions asked readers to (a) focus on specific relevant text segments, (b) ignore specific irrelevant text segments, (c) maintain an awareness that the text contained irrelevant segments without specifically identifying them, or (d) read without warnings. Participants generally exhibited longer reading times and enhanced recall for irrelevant segments compared to base content, except in cases for which general instructions warned about but did not specifically identify those irrelevant elements. The implications of these findings for research on seductive details and text processing, as well practical applications for the design of reading instruction, are discussed. 1 Relevance can be instantiated for readers in a variety of ways, such as based on the inherent semantic associations between text content and the global topic of a text, as well as through task instructions that suggest the need for attending to text information (e.g., whether elements of a text are fodder for an exam). To date, studies of relevance instructions have investigated the combined effects of these conditions, and the current project does so as well, for a more powerful investigation of instructional impacts on reader attention and memory.

This paper is a theoretical analysis of formal annotation and ontology for the expression of the semantics of document. They are found wanting in this respect, not only for technical reasons, but because they embody a fundamentally misunderstood model of the process of signification. We propose an alternative model in which the interpretation context plays a fundamental role in the definition of an activity game that includes all actions performed on a document, including accessing external data. We briefly discuss it and its current technical embodiment.