Search Engines in Learning Contexts: A Literature Review

Abstract

The web is one of the primary sources of information for finding learning oriented documents. In addition, the main suitable way to find information and documents on the Internet is by using search engines. Search engines are constantly improving in terms of selection algorithms and in terms of the Human Machine interface (HMI). Also, these search engines are the basis of a new field of research called Search-As-Learning. The Search-As-Learning explores information search environments to enhance learning during user search tasks. This work focuses on our view of the state of the art in the field of Search Engines in learning context and Search-As-Learning, stressing on the most recent research. We conclude by highlighting the current shortcomings on improvement of the learning aspect within search engines, and present next work which will be the association of a layer above the traditional search engines to promote the appropriation of content during search task for a learning context

Changes in the Pattern of Weekdays Electricity Real Consumption during the COVID-19 Crisis

“…The proposed methodology uses several techniques to measure the distance/similarity between objects described by several attributes. Currently, such techniques are widely used in the theory of shape analysis and pattern recognition (a fundamental paper in this area was written by Biederman (1987) [5]); another is a book by Da Fontoura Costa and Cesar (2000) [6], classification theory (Batley (2015) [7], Parrochia (2016) [8]), machine learning (Bishop (2007) [9], Banoula (2022) [10]), web search engines (Yang and Gerasoulis (2014) [11], Kameni Homte, Batchakui and Nkambou (2022) [12]), data mining (Han, Kamber, and Pei (2012) [13], Tan et al (2018) [14]), and information retrieval (Hjørland & Pedersen, 2005 [15]), artificial intelligence (Russell and Norvig (2020) [16], Manyika (2022) [17]), and so on.…”