Abstract
In this article, we introduce a dataset of curated learning paths (LPs) to support search as learning. LPs were obtained through an online survey delivered to experts in different domains. Data were then analyzed and described in terms of a set of variables. The resulting dataset comprised 83 LPs, each containing three web pages, for an overall collection consisting of 249 documents. The dataset is intended to provide information scientists, education researchers, and industry professionals, who provide information services in educational contexts, a valuable resource to (i) investigate patterns in the order of LPs, (ii) improve ranking models and/or re-ranking methods, (iii) explain the structure of the recommended LPs, and (iv) investigate alternative approaches to display search results based on the features of LPs.
Highlights
Background and RationaleThe concept of learning paths (LPs)—defined as a finite and organized sequence of learning objects (LOs)—can be linked to Vannevar Bush’s notions of trails [1]
The dataset consisted of 249 LOs organized in 83 LPs recommended by experts from Argentina, Chile, Colombia, Ecuador, Mexico, Spain, and Venezuela
We introduced a dataset of curated LPs
Summary
This article describes a novel dataset comprising 83 learning paths (LPs) curated by experts. Each LP in the dataset includes a list of three sequential web pages, experts’ demographic information, experts’. The uses of the dataset includes, but are not limited to, research and development in areas such as information science, education, information retrieval, linguistics, and industry. The dataset is available through the Mendeley Data repository. The remaining sections of this article are structured as follows. The Python script used to extract general features in the dataset is presented in Appendix A
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