Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of e ducational datasets further catalyse this interestingness, especially in online learning. Although several EDM surveys are available in the literature, we could find only a few specific surveys on student performance analysis and prediction. These specific surveys are limited in nature and primarily focus on studies that try to identify possible predictor or model student performance. However, the previous works do not address the temporal aspect of prediction. Moreover, we could not find any such specific survey which focuses only on classroom-based education. In this paper, we present a systematic review of EDM studies on student performance in classroom learning. It focuses on identifying the predictors, methods used for such identification, time and aim of prediction. It is significantly the first systematic survey of EDM studies that consider only classroom learning and focuses on the temporal aspect as well. This paper presents a review of 140 studies in this area. The meta-analysis indicates that the researchers achieve significant prediction efficiency during the tenure of the course. However, performance prediction before course commencement needs special attention.

Student performance analysis and prediction in classroom learning: A review of educational data mining studies

Mechanisms for automatically generating a set of test questions for use in generating a test to be administered to human test takers are provided. The mechanisms ingest a document from a corpus of documents to select a potential test question answer from the document and generate a candidate test question based on the potential test question answer. The mechanisms evaluate the candidate test question using a Question and Answer (QA) system to generate a result indicative of whether the candidate test question should be maintained for test generation. The mechanisms store the candidate test question in the set of test questions in response to a result indicating that the candidate test question should be maintained for test generation.

Dynamic Identification and Validation of Test Questions from a Corpus

A syllabus is an important document for teachers, students, and institutions. It represents what and how a course will be conducted. Some authors have researched regarding the components for writing a good syllabus. However, there is not a standard format universally accepted by all educators. Even inside the same university, there are syllabuses written in different type of files like PDF, DOC, HTML, for instance. These kind of files are easily readable by humans but it is not the same by machines. On the other hand, ontologies are technologies of knowledge representation that allow setting information understandable for both humans and machines. In this paper, we present a literature review regarding the use of ontologies for syllabus representation. The objective of this paper is to know the use of ontologies to represent a syllabus semantically and to determine their score according to the five-stars of Linked Data vocabulary use scale. Our results show that some researchers have used ontologies for many purposes, widely concerning with learning objectives. Nevertheless, the ontologies created by the authors do not fulfill with the five-stars rating and do not have all components of a well-suited syllabus.

The Use of Ontologies for Syllabus Representation

The design of an assessment instrument (AI) is one of the major components of assessment process. The teachers have to generate AI aligned with the course learning objectives (LOs) within the boundary of topics defined by the syllabus of that course. When the instrument covers the LO fairly, it covers the subject matter defined by the syllabus fairly. This paper discusses an approach towards integrating both the contents and cognitive level defined in the LOs into the domain ontology for a Data structures course. This forms the first step towards building an automated system to measure the alignment of AI to course LOs. In the proposed approach, the notion of domain ontology is introduced on which the LOs are overlaid. Concepts and cognitive levels from LOs are extracted using Natural Language Processing. Mapping these to the nodes of the ontology also involves complex processes to take decisions about selection of nodes and traversal of links. For each of these, issues and challenges are discussed in detail and a solution is proposed. We have done a prototype implementation of the system to test our design. The effectiveness of this framework was tested by comparing the manually generated results by the experienced teachers to the system generated results and obtained an average accuracy of 90%. The examples are taken from Data Structures course of second year engineering curriculum.

Integrating the Learning Objectives and Syllabus into a Domain Ontology for Data structures Course

A mechanism is provided in a data processing system for determining comprehensiveness of a question paper given a syllabus of topics. An answer and evidence generator of a question answering system executing on the data processing system finds one or more answers based on the syllabus of topics for each question in the question paper. The answer and evidence generator identifies evidence for the one or more answers in the syllabus for each question in the question paper. A concept identifier of the question answering system identifies a set of concepts in the syllabus corresponding to the evidence for each question in the question paper to form a plurality of sets of concepts. The mechanism determines a value for a comprehensiveness metric for the question paper with respect to the syllabus of topics based on the plurality of sets of concepts.

Determining Comprehensiveness of Question Paper Given Syllabus

Examination, being the lone measure of competence in the current education system of our country, has a decisive role in building student's career. Hence, utmost care has to be taken in framing the Question paper (QP). However, setting up a good QP for assessment is a challenging task. Fairness, accuracy, consistency and elimination of bias are very important while selecting questions. In this paper, we address the issue of providing an automated framework to judge the fairness of a QP with respect to a given syllabus. We explored the possibility of using the semantic web technology in general and ontology in particular in addressing the issue of syllabus fairness. We introduce the notion of syllabus ontology that forms the semantically connected network of concepts from the domain. We identify various syllabus fairness issues and proposed an initial model for measuring the syllabus fairness of a QP. Initial results show that the model is feasible.

Ontology driven framework for assessing the syllabus fairness of a question paper

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Ontology driven framework for assessing the syllabus fairness of a question paper