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This study aims to investigate the integration of education for sustainable development (ESD) into landscape architecture (LA) programs in North American universities to understand its benefits and challenges and provide insights into the implementation of sustainability principles, pedagogies and teaching methods.

This study analyzed survey data from 128 LA faculty members from 85 Council of Educators in Landscape Architecture member schools. The survey assessed the implementation of sustainability principles, pedagogies, teaching benefits and challenges to integrating sustainability into their courses. To complement the survey data, 62 LA course syllabi were analyzed using a grounded theory approach to identify the topics covered, learning objectives and learning activities.

The study revealed that LA programs incorporate sustainability topics in various courses using project-based and interdisciplinary learning approaches. Integrating ESD into LA curricula promotes sustainability literacy, improved understanding of sustainability concepts and the development of sustainability skills. The study also found positive correlations between teaching methods, the enhancement of sustainability literacy and the development of design and planning skills.

This research examined the effects of teaching methods on student learning outcomes and the challenges instructors faced, providing practical insights into the integration of ESD in LA education. It offers recommendations to enhance the ESD knowledge of future LA practitioners, considering the inclusion of ESD as a core value of the standard by the Landscape Architectural Accreditation Board.

The purpose of this case study is to determine the effectiveness of the UDL academy in terms of the number of UDL techniques used by faculty after participating in the academy and surveys to explore faculty perceptions of UDL.

This quantitative case study utilized faculty surveys about the UDL academy, class observations and review of course syllabi to determine the effectiveness of the UDL academy and explore the experience of implementing UDL.

The UDL initiative has been a positive and effective experience. Broadly, faculty have had positive perceptions of UDL implementations based on faculty surveys. The effectiveness of the UDL academy was demonstrated by the number of UDL techniques used by faculty increased significantly for faculty that participated in the UDL academy. The control group of faculty members did not increase the number of UDL techniques used based on class observations and a review of course syllabi.

This research is from one librarian's perspective since the librarian led the UDL initiative and is the sole librarian with faculty status currently at the institution. As a proponent of UDL, the librarian's perspective may be biased. Librarians can implement UDL to reduce educational barriers and support student success. Additionally, librarians can offer support to faculty in learning about UDL by offering a similar UDL academy.

Most faculty were able to incorporate UDL elements into their courses and responded positively to the concept of integrating UDL in the classroom, feeling that it helped improve their teaching. These results demonstrate that faculty can integrate UDL into higher education to use effective teaching strategies after participating in a UDL academy.

This paper is an original work describing a campus UDL initiative from a librarian's perspective.

Research shows that postsecondary students are largely unaware of the impact of algorithms on their everyday lives. Also, most noncomputer science students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aims to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors and pedagogical considerations to aid faculty in teaching algorithmic literacy to postsecondary students.

Eleven semistructured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. A content analysis was manually performed on the transcripts using a mixture of deductive and inductive coding. Data analysis was aided by the coding software program Dedoose (2021) to determine frequency totals for occurrences of a code across all participants along with how many times specific participants mentioned a code. Then, findings were organized around the three themes of knowledge components, coping behaviors and pedagogy.

The findings suggested a set of 10 knowledge components that would contribute to students’ algorithmic literacy along with seven behaviors that students could use to help them better cope with algorithmic systems. A set of five teaching strategies also surfaced to help improve students’ algorithmic literacy.

This study contributes to improved pedagogy surrounding algorithmic literacy and validates existing multi-faceted conceptualizations and measurements of algorithmic literacy.

The purpose of this paper is to demonstrate how network theory and methods can provide insights into the forces shaping architectural learning agendas and knowledge construction in architectural schools.

The methodology involves conceptualising learning as a constructivist process and the agenda as an interconnected network of actors, concepts and relations. Network analysis techniques, including centrality and brokerage metrics, are used to identify roles and knowledge flows using the data locally collected from Turkish universities as well as from the OpenSyllabus open-source database.

The analysis reveals the enduring influence of early modernists, signalling imbalanced canon formation in the architectural learning system. However, marginal voices highlight struggles in integrating unconventional perspectives. Limited integration of local figures indicates a consolidation of Eurocentric epistemes. Identifying these hidden forces is vital for reimagining learning agendas and socio-culturally engaged forms of learning. Pioneering figures demonstrate potential for synthesis when situated as brokers, not bifurcated schools.

The outcomes are limited by the geographical and temporal boundaries of the data and the analysis method employed. Despite limitations, the diagnostic network framework reveals architectural learning as an open, contested ecosystem demanding pluralistic pedagogies concerning not only the global but the local, both canonical and marginal. Further research covering more data could enrich the understanding of qualitative complexities.

The network perspective prompts critical reflexivity about power, ideology and exclusion in knowledge construction. Strategic inclusion and diversification of voices provide pathways to bridge divides and ground learning locally.

This research offers a methodology model to examine forces and influences shaping architectural education by elucidating hidden and remote roles and knowledge gaps in learning agendas. Extending the techniques more widely can enable strategic interventions toward inclusive, impactful learning across disciplines, time and geographies.

With so many placement opportunities around the students in their final or prefinal year, they start to feel the strain of the season. The students feel the need to be aware of their position and how to increase their chances of being hired. Hence, a system to guide their career is one of the needs of the day.

The job role prediction system utilizes machine learning techniques such as Naïve Bayes, K-Nearest Neighbor, Support Vector machines (SVM) and Artificial Neural Networks (ANN) to suggest a student’s job role based on their academic performance and course outcomes (CO), out of which ANN performs better. The system uses the Mepco Schlenk Engineering College curriculum, placement and students’ Assessment data sets, in which the CO and syllabus are used to determine the skills that the student has gained from their courses. The necessary skills for a job position are then extracted from the job advertisements. The system compares the student’s skills with the required skills for the job role based on the placement prediction result.

The system predicts placement possibilities with an accuracy of 93.33 and 98% precision. Also, the skill analysis for students gives the students information about their skill-set strengths and weaknesses.

For skill-set analysis, only the direct assessment of the students is considered. Indirect assessment shall also be considered for future scope.

The model is adaptable and flexible (customizable) to any type of academic institute or universities.

The research will be very much useful for the students community to bridge the gap between the academic and industrial needs.

Several works are done for career guidance for the students. However, these career guidance methodologies are designed only using the curriculum and students’ basic personal information. The proposed system will consider the students’ academic performance through direct assessment, along with their curriculum and basic personal information.

Higher education plays an essential role in achieving the United Nations sustainable development goals (SDGs). However, there are only scattered studies on monitoring how universities promote SDGs through their curriculum. The purpose of this study is to investigate the connection of existing common core courses in a university to SDG education. In particular, this study wanted to know how common core courses can be classified by machine-learning approach according to SDGs.

In this report, the authors used machine learning techniques to tag the 166 common core courses in a university with SDGs and then analyzed the results based on visualizations. The training data set comes from the OSDG public community data set which the community had verified. Meanwhile, key descriptions of common core courses had been used for the classification. The study used the multinomial logistic regression algorithm for the classification. Descriptive analysis at course-level, theme-level and curriculum-level had been included to illustrate the proposed approach’s functions.

The results indicate that the machine-learning classification approach can significantly accelerate the SDG classification of courses. However, currently, it cannot replace human classification due to the complexity of the problem and the lack of relevant training data.

The study can achieve a more accurate model training through adopting advanced machine learning algorithms (e.g. deep learning, multioutput multiclass machine learning algorithms); developing a more effective test data set by extracting more relevant information from syllabus and learning materials; expanding the training data set of SDGs that currently have insufficient records (e.g. SDG 12); and replacing the existing training data set from OSDG by authentic education-related documents (such as course syllabus) with SDG classifications. The performance of the algorithm should also be compared to other computer-based and human-based SDG classification approaches for cross-checking the results, with a systematic evaluation framework. Furthermore, the study can be analyzed by circulating results to students and understanding how they would interpret and use the results for choosing courses for studying. Furthermore, the study mainly focused on the classification of topics that are taught in courses but cannot measure the effectiveness of adopted pedagogies, assessment strategies and competency development strategies in courses. The study can also conduct analysis based on assessment tasks and rubrics of courses to see whether the assessment tasks can help students understand and take action on SDGs.

The proposed approach explores the possibility of using machine learning for SDG classifications in scale.

This study aims to assess the digital literacy of African library and information science (LIS) professionals. Digital literacy skills are required in the digital library environment.

Relevant literature was reviewed on this topic; previous study questionnaires, various university LIS curriculum, content analysis and job advertising were used for preparing the survey questionnaire. This study used an ex post facto research design. Purposive sampling was adopted in this study, and data were collected from Google form by sharing the research questionnaire by hyperlink which was undertaken from March 15, 2022 to May 24, 2022. The data were collected by a structured questionnaire. A four-point Likert scale was used to measure how library professionals acquired digital literacy skills. Content analysis of more than 500 job vacancies over the past three years (2018–2022) of library job advertisements was taken randomly.

This study’s findings show that the majority of the good at basic level of digital literacy skills at the same time have less in advanced digital literacy skills. The finding of this study shows the majority of them have to upgrade and learn about advanced digital literacy skills.

This study recommends adopting a new syllabus and updating a LIS curriculum based on the library’s technological development. Furthermore, this study’s potential result suggests more practical classes instead of theoretical study and it is useful to the library schools, associations, MOOC providers and lifelong learners.

This study is conducted with 102 African library professionals from 13 countries. This study discusses futuristic digital literacy skills and basic literacy skills. This study is beneficial to those who are preparing a new curriculum in LIS fields.

The purpose of this study is to examine the formation of sustainable development competencies (SDCs) in future primary school teachers during English for specific purposes classes, and to assess the correlation between English proficiency and the development of SDCs, including Collaboration, Strategic thinking, Critical thinking, Modelling sustainable behaviour, Systems thinking and Future thinking.

The research experiment involved the application of content and language integrated learning and facilitation methods in three higher education institutions in Ukraine. The students’ level of English language proficiency was assessed based on the results of the online Cambridge English Language Assessment test, while the level of SDC formation was measured using research methods adapted to the Ukrainian context.

The experiment revealed positive changes in the levels of SDCs and English language proficiency through integrated learning and the application of facilitation methods.

The study established a correlation between the level of English language proficiency and the formation of competencies, such as Collaboration, Strategic thinking, Critical thinking, Modelling sustainable behaviour, Systems thinking and Future thinking, all of which are vital for sustainable development.