Concept Graph Learning from Educational Data

This paper addresses an open challenge in educational data mining, i.e., the problem of using observed prerequisite re-lations among courses to learn a directed universal con-cept graph, and using the induced graph to predict un-observed prerequisite relations among a broader range of courses. This is particularly useful to induce prerequisite relations among courses from different providers (universi-ties, MOOCs, etc.). We propose a new framework for in-ference within and across two graphs—at the course level and at the induced concept level—which we call Concept Graph Learning (CGL). In the training phase, our system projects the course-level links onto the concept space to in-duce directed concept links; in the testing phase, the concept links are used to predict (unobserved) prerequisite links for test-set courses within the same institution or across insti-tutions. The dual mappings enable our system to perform an interlingua-style transfer learning, e.g. treating the con-cept graph as the interlingua, and inducing prerequisite links in a transferable manner across different universities. Ex-periments on our newly collected data sets of courses fro

Annotation Protocol for Textbook Enrichment with Prerequisite Knowledge Graph

Extracting and formally representing the knowledge embedded in textbooks, such as the concepts explained and the relations between them, can support the provision of advanced knowledge-based services for learning environments and digital libraries. In this paper, we consider a specific type of relation in textbooks referred to as prerequisite relations (PR). PRs represent precedence relations between concepts aimed to provide the reader with the knowledge needed to understand a further concept(s). Their annotation in educational texts produces datasets that can be represented as a graph of concepts connected by PRs. However, building good-quality and reliable datasets of PRs from a textbook is still an open issue, not just for automated annotation methods but even for manual annotation. In turn, the lack of good-quality datasets and well-defined criteria to identify PRs affect the development and validation of automated methods for prerequisite identification. As a contribution to this issue, in this paper, we propose PREAP, a protocol for the annotation of prerequisite relations in textbooks aimed at obtaining reliable annotated data that can be shared, compared, and reused in the research community. PREAP defines a novel textbook-driven annotation method aimed to capture the structure of prerequisites underlying the text. The protocol has been evaluated against baseline methods for manual and automatic annotation. The findings show that PREAP enables the creation of prerequisite knowledge graphs that have higher inter-annotator agreement, accuracy, and alignment with text than the baseline methods. This suggests that the protocol is able to accurately capture the PRs expressed in the text. Furthermore, the findings show that the time required to complete the annotation using PREAP are significantly shorter than with the other manual baseline methods. The paper includes also guidelines for using PREAP in three annotation scenarios, experimentally tested. We also provide example datasets and a user interface that we developed to support prerequisite annotation

Principles and Applications of Data Science

Data science is an emerging multidisciplinary field which lies at the intersection of computer science, statistics, and mathematics, with different applications and related to data mining, deep learning, and big data. This Special Issue on “Principles and Applications of Data Science” focuses on the latest developments in the theories, techniques, and applications of data science. The topics include data cleansing, data mining, machine learning, deep learning, and the applications of medical and healthcare, as well as social media