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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.

The development of qualitative research methods addresses the need to explore, understand and interpret complex and subjective phenomena across various fields of study. These methods are guided by methodological frameworks, and data collection involves taking several precautions for observation or interviews. While these guidelines facilitate an emphasis on the objective aspects of discourse, accounting for subjectivity and emotions proves more challenging. However, these subjectivity and emotions are deemed as significant sources of information. In this chapter, we propose an innovative data collection method centred around creating collages and engaging in group discussions to decipher their meaning. Collage serves as a visual medium, and we recommend utilising semiotic analysis tools to comprehend its significance. To gain a more precise understanding of the value of collage as a data collection method, we studied a collage workshop organised by CREE. Through image analysis and exchanges, our findings reveal that collage acts as a physical medium that fosters exchanges, deepens ideas and restricts digressions. Additionally, collage allows for the expression and discussion of emotions linked to the image rather than the individual. The space of intersubjective reflexivity facilitated by collage enables a profound comprehension, critical assessment and augmentation of ideas and the interpretation of emotions without compromising the sensitivity of the author. This chapter’s main contribution is evidently manifested here.

The geographic information systems (GIS) sector is witnessing significant growth in recent times at the global level and in the Kingdom of Saudi Arabia at the same pace. In this research, the authors aim to measure the GIS market employability in the Kingdom of Saudi Arabia, the required skills of those seeking GIS employees, existing knowledge and abilities of graduates of relevant academic programs and gaps, if any, between the graduates and the labor market requirements in terms of GIS. Research on GIS education and GIS employability from an information system perspective is scarce; thus, this study is needed.

In this study, a questionnaire was distributed manually and electronically to a specific group of GIS students, teachers and employees. The questionnaire was prepared in Arabic and English language. It was distributed manually to the attendees of the 12th GIS Symposium, organized by Imam Abdulrahman Bin Faisal University at Dammam city, Saudi Arabia in April of 2018. The event is a yearly one that gathers local GIS community and outside speakers and guests. An electronic version of the questionnaire was also available and distributed via personal contacts. Responses were received until the end of 2019.

The results of 107 respondents indicates good GIS foundation (for example in topology, geospatial data processing and cartography); however, programming and business skills (namely cost benefit analysis, organizational perspective and business understanding) are lacking from Saudi GIS graduates. About 67% of respondents acknowledge directly that it is difficult to find locally qualified GIS graduates. Moreover, 55% of respondents classify GIS job candidates as underqualified. This research identifies under qualification in business competencies and computer programming skills for GIS students and workers in Saudi Arabia. This research did not find a statistically significant different response between GIS teachers and GIS employees/employers; however, students’ response was significantly different from these two groups.

Research on GIS education and GIS employability from an Information System perspective is scarce; thus, this study is needed. Moreover, research about this topic in Saudi Arabia and the Arabian Gulf is almost nonexistent.

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.

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.

The use of technology in Saudi Arabian higher education is constantly evolving. With the support of the 2030 Saudi vision, many research studies have started covering learning analytics and Big Data in the Saudi Arabian higher education. Examining learning analytics in higher education institutions promise transforming the learning experience to maximize students' learning potential. With the thousands of students' transactions recorded in various learning management systems (LMS) in Saudi educational institutions, the need to explore and research learning analytics in Saudi Arabia has caught the interest of scholars and researchers regionally and internationally. This chapter explores a Saudi private university in Jeddah, Saudi Arabia, and examines its rich learning analytics and discovers the knowledge behind it. More than 300,000 records of LMS analytical data were collected from a consecutive 4-year historic data. Romero, Ventura, and Garcia (2008) educational data mining process was applied to collect and analyze the analytical reports. Statistical and trend analysis were applied to examine and interpret the collected data. The study has also collected lecturers' testimonies to support the collected analytical data. The study revealed a transformative pedagogy that impact course instructional design and students' engagement.

This chapter provides an in-depth examination of the quality assurance process in the higher education system of Iraq and the Kurdistan Region of Iraq (KRI) in terms of standards, processes, and procedures. More specifically, the chapter aims to examine the impact of the quality assurance process on the quality development of education in Iraq and Kurdistan Region, as well as identify the challenges that staff (top management and faculty members) face at various higher education institutions therein. For this purpose, a qualitative/quantitative analysis was conducted, which involved reviewing existing official policies, reports, and forms; interviewing top officials (presidents, vice-presidents, quality assurance directors) at a few higher education institutions (public and private); and developing a survey questionnaire that detected the perspective of top officials and faculty members regarding the level of impact quality assurance has had on the development of higher education. A total of 284 university staff members were surveyed from different universities in Iraq and Kurdistan. A total of 29 universities from Iraq and Kurdistan were represented in the survey. The respondents came from a variety of colleges and departments at public and private universities, with 79% of respondents coming from public universities. Study results demonstrate that quality assurance has contributed significantly to the improvement of university staff performance. There are a number of recommendations made within this chapter for educational leaders, which could lead to the further development of quality practices and the upgrading of the higher education system.