Educational Data Mining (EDM) is an emerging inter-disciplinary research area that involves education and computer science. EDM employs data mining tools and techniques, on large datasets related to education, to extract meaningful and useful information. EDM works toward the improvement of educational processes by introducing better and effective learning practices. EDM methods refer to the set of methods that are used for building models/applications. This article presents an extensive literature survey of EDM methods. The article also discusses research trends and challenges in EDM. This insight into EDM attempts to provide useful and valuable information to researchers interested in furthering the field of EDM.

One of the most challenging aspects of satellite remote sensing is image fusion. Image fusion increases the visual interpretation of the image and has many applications such as monitoring water bodies, land cover, urbanisation, agriculture, national defence, and so forth. Remote sensing applications require images with a high spatial and spectral resolution for accurately processing and distinguishing the land cover classes with fine texture and shape details. Due to technical limitations, most satellites cannot take high‐resolution multi‐spectral images but can get high‐resolution panchromatic images and low‐resolution multi‐spectral satellite images separately. This article proposes a novel fusion method, geospatial data fusion (GDF), to obtain high‐resolution multi‐spectral images. GDF, along with three well‐known fusion methods viz., Brovey Transform (BT), wavelet transform (WT), and Fourier transform (FT), have been comparatively implemented to fuse the Cartosat‐2 and Sentinel‐2 imageries of the Sangam area of Prayagraj, India. The fusion has been done to extract the earth’s surface features from the fused imagery. In this research, the fused image is utilised for river water mapping. Results confirm that the GDF outperforms other existing fusion methods and successfully maps the river water in the study area.

A manuscript, written by the researcher(s), undergoes a lot of revisions to improve its readability. The revision process eliminates grammatical mistakes, enhances presentation style and minimizes ambiguity. A better version of the research article is possible after a sufficient number of revisions. However, there is no specific criterion for the number of revisions. It takes a lot of time and efforts to prepare a draft. Hence, it is required to finalize a manuscript in minimum number of drafts. This article highlights the key mistakes made by most of the researchers while writing and revising their drafts. The first three drafts of ten different manuscripts have been investigated for common mistakes. The article also finds the relationship(s) among these mistakes. Association rule mining methods have been applied on the investigation dataset. The detected rules infer that the mistakes are interrelated