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The growth of artificial intelligence (AI) technologies has affected higher education in a dramatic way, shifting the norms of teaching and learning. With these shifts come major ethical questions relating to surveillance, exacerbated social inequality, and threats to job security. This article overviews some of the discourses that are developing on the integration of AI into the higher education setting, with focus on LIS and librarianship, considers the role of LIS and librarianship in intervening in the trajectory of AI in learning and teaching, and weighs in on the place of professional LIS ethics in relation to confronting AI-led technological transformations.

Purpose: This study aims to investigate the feasibility of developing general predictive models for using the learning management system (LMS) data to predict student performances in various courses. The authors focused on examining three practical but important questions: are there a common set of student activity variables that predict student performance in different courses? Which machine-learning classifiers tend to perform consistently well across different courses? Can the authors develop a general model for use in multiple courses to predict student performance based on LMS data? Design/methodology/approach: Three mandatory undergraduate courses with large class sizes were selected from three different faculties at a large Western Canadian University, namely, faculties of science, engineering and education. Course-specific models for these three courses were built and compared using data from two semesters, one for model building and the other for generalizability testing. Findings: The investigation has led the authors to conclude that it is not desirable to develop a general model in predicting course failure across variable courses. However, for the science course, the predictive model, which was built on data from one semester, was able to identify about 70% of students who failed the course and 70% of students who passed the course in another semester with only LMS data extracted from the first four weeks. Originality/value: The results of this study are promising as they show the usability of LMS for early prediction of student course failure, which has the potential to provide students with timely feedback and support in higher education institutions.

The purpose of this paper is to express Avicenna's standpoints in the area of teachers' professional ethics. Making use of a qualitative approach and a descriptive-analytic method, this study attempted to describe and analyze Avicenna's viewpoints on prerequisites of teaching profession by the help of the available resources. In general, the results showed that skillful teacher needs to possess admirable ethical characteristics in his individual and social dimensions. He should attempt to educate his own soul before making any effort in educating others considering some important points. It is better for him to have prudence in all his affairs. He should not try to educate anybody before being acquainted with his/her traits. He also should be generous and faithful toward the others. Eventually, it is expected from such a kind of teacher to gain a very high level of skillfulness in advising and guiding the learners.

Background: Twitter is rich in data for text and data analytics research, with the ability to capture trends. Objectives: This study examines Canadian tweets on marijuana legalization and terminology used. Presented as a case study, Twitter analytics will demonstrate the varied applications of how this kind of research method may be used to inform library practice. Methods: Twitter API was used to extract a subset of tweets using seven relevant hashtags. Using open‐source programming tools, the sampled tweets were analysed between September to November 2018, identifying themes, frequently used terms, sentiment, and co‐occurring hashtags. Results: More than 1,176,000 tweets were collected. The most popular hashtag co‐occurrence, two hashtags appearing together, was #cannabis and #CdnPoli. There was a high variance in the sentiment analysis of all collected tweets but most scores had neutral sentiment. Discussion: The case study presents text‐mining applications relevant to help make informed decisions in library practice through service analysis, quality analysis, and collection analysis. Conclusions: Findings from sentiment analysis may determine usage patterns from users. There are several ways in which libraries may use text mining to make evidence‐informed decisions such as examining all possible terminologies used by the public to help inform comprehensive evidence synthesis projects and build taxonomies for digital libraries and repositories. [ABSTRACT FROM AUTHOR]

A straightforward and effective strategy for the synthesis of novel 7-cyano-4-imino-3-phenyl-6-(pyrrolidin-1-yl)-2-substituted-3,4-dihydroselenopheno[3,2-d]pyrimidines have been accomplished through a base-promoted cascade addition/cyclization sequence employing phenyl isothiocyanate followed by the S-alkylation of the obtained fused Se-containing heterocyclic framework with various alkyl halides. Free radical scavenging activity of the synthesized selenium-containing structures was studied against DPPH, as well. Antioxidant activities as IC50 values were ranged from 12.19 to 91.08 µM. Unfused 3-amino-2,4-dicyano-5-(pyrrolidin-1-yl)selenophene was much more powerful antioxidant agent than ascorbic acid. Moreover, the promising antioxidant potentials were observed with selenopheno[3,2-d]pyrimidines. Taking into account the best antioxidant activity of S-benzylated derivative amongst pyrimidine-fused heterocycles and significant relationship between antioxidant activity of compounds and many of their biological potencies, using benzyl halides bearing electron donating groups on phenyl ring can probably improve their antioxidant capacities to make them more effective anti-cancer, anti-diabetic, anti-inflammatory and anti-neurodegenerative agents. [ABSTRACT FROM AUTHOR]

Employing electron beam for radiotherapy purposes now has been established as one of the standard cancer treatment modalities. Both dedicated intraoperative and conventional electron beams can be employed in patient irradiation. Due to the differences between accelerating structure and electron beam delivery of dedicated intraoperative radiotherapy (IORT) machines and conventional ones, the initial energy spectra of the produced electron beam by these machines may be different. Accordingly, this study aims to evaluate whether these spectral differences can affect the relevant relative biological effectiveness (RBE) values of intraoperative and conventional electron beams. A hybrid Monte Carlo simulation approach was considered. At first, the head LIAC12 machine (as an IORT accelerator) and Varian 2100C/D (as a conventional accelerator) were simulated by MCNPX code and electron energy spectra at different depths and off-axis distances were scored for two nominal electron energies of 6 and 12 MeV at the field sizes of 6 and 10 cm. Then, the calculated spectra were imported to MCDS code to estimate the induced DNA-damage RBE values. Finally, the obtained RBE values for intraoperative and conventional electron beams were compared together. The results showed that the RBE values of the intraoperative electron beam are superior to those obtained for conventional electron beam at the same energy/field size combination. Variations of the depth can regularly affect the RBE value for both conventional and intraoperative electron beams, while no ordered variation trend was observed for RBE with changing the off-axis distance. Variations of electron energy and field size can also influence the RBE value for both types of studied electron beams. From the results, it can be concluded the structural differences between the dedicated IORT and conventional Linacs can lead to distinct initial electron energy spectra for intraoperative and conventional electron beams. These physical differences can finally lead to different RBE values for intraoperative and conventional electron beams at the same energy and field size. [ABSTRACT FROM AUTHOR]

A novel heterogeneous catalyst, natural halloysite@Si(CH2)3NH2@Cu(acac)2, was designed and synthesized through the functionalization of the halloysite nano clay surface by 3-aminopropyltriethoxysilane followed by copper acetylacetonate. The catalyst was characterized using various techniques such as FTIR, SEM, EDS, BET, ICP, XRD, TEM, and TGA. The catalytic activity of natural halloysite@Si(CH2)3NH2@Cu(acac)2) was also evaluated for the synthesis of propargylamines using multicomponent A3-coupling reaction of various aldehydes, amines, and phenylacetylene. [ABSTRACT FROM AUTHOR]