Assessment In Higher Education Research Articles

Inequity in exam experience

Online digital exams, both with and without online invigilation, have become a common form of assessment in Higher Education, particularly since the COVID-19 pandemic. However, the impact of online exams and online invigilation (proctoring) on student experience, stress levels, and academic performance remains underexplored, especially for disadvantaged or underrepresented student groups. Despite growing concerns over inequitable outcomes in Higher Education, there is limited large-scale research investigating how these online assessment methods may exacerbate existing disparities. This paper addresses this gap by analysing data from a large-scale survey (N = 16,010) conducted across five semesters at an Australian University (2021-2023). Specifically, it examines the exam preparedness and experiences of on-campus students who sat online digital exams with and without proctoring. By exploring the intersectionality of gender, international status, and language, the study aims to assess whether and how online proctoring exacerbates inequities among disadvantaged student populations. Our findings suggest that social and cultural factors significantly influence exam preparedness, stress levels, and perceived academic performance, with proctoring amplifying these disparities.

Assessment IS learning: developing a student-centred approach for assessment in Higher Education.

Assessment and the associated feedback from those assessments are powerful factors in the development of students' learning. We have seen a shift within the Higher Education sector to conceptualise assessment as being more than summative assessment 'of' learning. Instead, there has been a greater emphasis on assessment 'as' learning, or assessment 'for' learning, through the enhanced use of formative assessments. Centralising assessment within the learning process highlights that assessment IS learning and cannot be separated from other elements of the learning process. In particular, assessment has a vital role to play in the development of students' self-regulated learning skills and the development of independence in learners. However, for assessments to effectively support learning, they need to be meaningful, engaging, well-integrated into the learning activities and 'student-focused'. Placing student skills development and personal development at the centre of assessment design has the potential to empower students through assessment. This review focuses on the potential of assessment to support student learning and development, using the 'Equity, Agency, Transparency' ('EAT') framework as a lens for effective assessment and feedback practices. We suggest ways in which we can make our assessment and feedback practices more inclusive, meaningful and authentic to the students' learning needs.

A Bibilometric Analysis of Assessment as Learning in Higher Education

Assessment in higher education has been dominated by traditional practices that mainly focus on grades for student progression. However, there has been some transformation in assessment due to the advancement in education. Generally, assessment is viewed as central to student learning and experience. Assessment as learning, is not just an addition to learning and teaching, but it presents an opportunity through which students’ involvement in assessment could manifest as part of learning: assessment is learning-oriented. Assessment as Learning in Higher Education is about student empowerment; hence, students take ownership of their own learning process. Furthermore, assessment as learning promotes constructivism through student engagement and knowledge construction. This bibliometric analysis analysed the trends evident from research on assessment as learning and implementation in Higher Education. Data were retrieved from the Scopus database and analysed using VOSviewer. The thematic analysis approach was also employed. The study reveals that self-assessment is important in promoting student empowerment, which is one of the benefits of assessment as learning. It is important that higher education institutions rethink assessment as learning because this might assist in achieving the 21st-century educational goal of independent learning. The study’s findings confirm that there is a need for constructive alignment of teaching, learning and assessment; hence, assessment as learning becomes more significant. The findings provided an overview of key themes, research gaps and potential areas for future research. The insight that was gained from this bibliometric analysis will be valuable for lecturers, course designers, universities, assessors and researchers seeking to improve assessment in higher education.

Exploring staff and student experiences of group-work assessment in higher education

Group work in higher education is well known to promote deep learning through collaboration with peers. However, there can be issues with facilitation, perceived fairness, and motivation. The aim of this project was to explore staff and student views of a new group presentation assessment. A student survey explored preparation, experiences, and learning/skills. Two academics were interviewed. According to the survey, students felt prepared for the group work, but less so for the presentation. Students also felt their group worked well, although some did not agree that everyone contributed. Most agreed that the task was enjoyable and that they gained new knowledge. Two broad themes arose from the interviews (preparedness and student support), which link to the findings in the survey. Though the assessment format was well received, there was room to develop the support resources. The individual contribution grade for the assessment could include more weight on quality to enhance engagement. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0/ .

Why authenticity matters: a qualitative investigation of students’ perceptions of personal values in first-year assessments

ABSTRACT Authentic assessments in higher education typically imply the replication of real-world tasks. Psychological authenticity is the perceived alignment between an individual’s internal reference sources (i.e. personal values and beliefs) and their actions. Applying psychological authenticity logic to assessment tasks, we investigated students’ perceptions of scaffolded first-year assessment tasks on their health behaviour. In particular, we explored the meaning and value students associated with these tasks. One hundred students (86 female) completed an online open-text survey. Following our thematic analysis of the data, we constructed two higher-order themes. First, ‘the assessments were the catalyst for enabling self-reflection’ exemplified the opportunity students were afforded to reflect upon their current health behaviour. Second, ‘most changed for the better, but some stayed the same’ captured the dichotomy of student perceptions in completing the assessment tasks. Collectively, our findings offer new insights into authenticity in higher education assessment practices.

Research on the Evaluation of College English Teaching Based on Data Mining Technology From the Perspective of “Internet Plus Education”

This paper explores using data mining in English teaching assessment in higher education within the 'Internet + Education' era. Traditional assessment methods struggle to meet modern teaching needs. By collecting diverse data like student performance and learning behavior, and employing data mining, a comprehensive assessment model is built. This model tracks student progress, uncovers data patterns, and offers personalized teaching guidance. Empirical analysis validates the model's feasibility, enabling colleges to better understand English teaching quality and adjust methods accordingly. In essence, this study offers a novel approach to assessment and contributes to education digitization and teaching enhancement.

Addressing student non-compliance in AI use declarations: implications for academic integrity and assessment in higher education

This study examines the factors driving student non-compliance with AI use declarations in academic assessments at King’s Business School, where 74% of students failed to declare AI usage despite declaration being a requirement of a mandatory coursework coversheet. Utilising the Theory of Planned Behaviour (TPB) as a framework, the research combines service evaluation survey data and semi-structured interviews to explore how attitudes, subjective norms, and perceived behavioural control influence compliance. Findings reveal that fear of academic repercussions, ambiguous guidelines, inconsistent enforcement, and peer influence are key barriers to AI use declaration. These factors complicate the declaration process, undermine transparency, and challenge academic integrity. The study extends the TPB model by highlighting the ethical and practical dilemmas posed by generative AI, which blur traditional norms of academic integrity. This research offers critical insights for policymakers, suggesting that clear, consistent, and trust-based policies are crucial in fostering ethical AI use. The findings underscore the importance of transparent communication and supportive institutional cultures to improve compliance. Ultimately, this study informs policy development by evaluating the effectiveness of declaration mechanisms and providing actionable recommendations to promote a culture of academic integrity in the evolving landscape of AI technologies.

University Teachers’ Views on the Adoption and Integration of Generative AI Tools for Student Assessment in Higher Education

This study examines the factors that may impact the adoption of generative artificial intelligence (Gen AI) tools for students’ assessment in tertiary education from the perspective of early-adopter instructors in the Middle East. It utilized a self-administered online survey and the Unified Theory of Acceptance and Use of Technology (UTAUT) model to collect data from 358 faculty members from different countries in the Middle East. The Smart PLS software 4 was used to analyze the data. The findings of this study revealed that educators developed new strategies to integrate Gen AI into assessment and used a systematic approach to develop assignments. Moreover, the study demonstrated the importance of developing institutional policies for the integration of Gen AI in education, as a driver factor influencing the use of Gen AI in assessments. Additionally, the research identified significant factors, namely performance expectancy, effort expectancy, social influences, and hedonic motivation, shaping educators’ behavioral intentions and actual use of Gen AI tools to assess students’ performance. The findings reveal both the potential advantages of Gen AI, namely enhanced student engagement and reduced instructor workloads, and challenges, including concerns over academic integrity and the possible negative impact on students’ writing and thinking skills. This study emphasizes the significance of targeted professional development and ethical criteria for the proper integration of Gen AI in educational assessment.

Examining Validity and Reliability of General English Course Assessments in Higher Education

This study investigated the validity and reliability of a General English Course assessment tool at Universitas Harapan Bangsa. Using a quantitative descriptive technique, the study examined the assessment items using point-biserial correlation for validity and Cronbach's alpha for reliability. While the instrument had high overall reliability, several items needed to be revised to improve its validity. The findings indicate that the assessment instrument is generally reliable and valid; nevertheless, continuing monitoring and adjustment are required to maintain its quality and assure accurate evaluation of student English competence.

Transforming digital learning and assessment strategies in higher education

This study proposes a transformative approach to integrate modern technology and online assessments in higher education, emphasizing the shift from traditional methods to advanced-conceptualized digital platforms. It explores the potential of technologies such as artificial intelligence (AI), virtual reality (VR), and mobile applications to reshape education by offering scalability, real-time feedback, and personalized learning experiences. These technologies provide opportunities to create immersive learning environments and enable students to engage with content more interactively. Through a survey conducted with 367 sophomores from four faculties at Universitas Terbuka, digital readiness, challenges, and expectations were assessed. The survey results indicated a diverse range of digital proficiency among students, highlighting both strengths and areas needing improvement in digital skills. Preliminary findings indicate significant differences in digital readiness and expectations for interactive and flexible learning environments among faculties. Students from technology-oriented faculties exhibited higher readiness and adaptability to digital tools compared to those from humanities and social sciences faculties. The study underscores the need for targeted strategies to enhance digital competencies and support systems, ensuring equitable access to digital learning tools. Additionally, it identifies key barriers such as limited access to reliable internet and insufficient digital literacy training, which must be addressed to fully realize the benefits of online education. For instance, students reported difficulties in accessing advanced educational software due to limited bandwidth, which impacts the efficacy of online learning platforms. These insights provide practical recommendations for educators, developers, and policymakers to optimize digital learning experiences and address emerging educational challenges, setting the stage for future transformative practices in higher education. The findings advocate for a comprehensive institutional policy that promotes continuous digital skills development and infrastructure investment to support inclusive and innovative learning experiences.

Results and implications for generative AI in a large introductory biomedical and health informatics course

Generative artificial intelligence (AI) systems have performed well at many biomedical tasks, but few studies have assessed their performance directly compared to students in higher-education courses. We compared student knowledge-assessment scores with prompting of 6 large-language model (LLM) systems as they would be used by typical students in a large online introductory course in biomedical and health informatics that is taken by graduate, continuing education, and medical students. The state-of-the-art LLM systems were prompted to answer multiple-choice questions (MCQs) and final exam questions. We compared the scores for 139 students (30 graduate students, 85 continuing education students, and 24 medical students) to the LLM systems. All of the LLMs scored between the 50th and 75th percentiles of students for MCQ and final exam questions. The performance of LLMs raises questions about student assessment in higher education, especially in courses that are knowledge-based and online.

Comprehensive examination of diverse assessment methodologies applicable to Sport Science departments at Higher Education institutions

Educators and educational leaders have engaged in ongoing discussions and debates concerning student assessment in higher education. Concerns have been raised by academics about the disconnect between assessment methods and actual student learning outcomes. The aim of this review was to conduct a comprehensive examination of diverse assessment methodologies applicable to higher education. Furthermore, a critical analysis was performed to evaluate the alignment of current assessment practices within the author's specific academic field with the various assessment methods under scrutiny, as well as to assess their efficacy. The literature review pertaining to the evaluation techniques employed in higher education underwent an electronic search across several databases: EBM Reviews, Current Contents, Science Direct, Google Scholar, CISTI Source (from 2000 to June 2021), Cochrane Database of Systematic Reviews and international e-catalogues. A total of (n=38) studies were found that met the necessary criteria to be included in the study. It is important to acknowledge that this examination should be regarded as a foundational framework, with ample room for future research to incorporate additional assessment approaches, given the multitude of options available. Assessment for learning, tasks of learning, and peer and self-assessment emerge as invaluable tools applicable across various disciplines within higher education.

Conversational Artificial Intelligence: A Catalyst for Rethinking Assessment in Higher Education

Conversational Artificial Intelligence has disrupted higher education by fundamentally altering its landscape. Fuelled by natural language processing and machine learning this technology has gained widespread adoption particularly since the release of ChatGPT in November 2022. As universities embrace digital transformation, assessment practices must evolve to align with the capabilities of Artificial Intelligence-driven chatbots and virtual assistants. This paper explores how conversational artificial intelligence impacts higher education, in particular, student assessment. A fundamental shift in assessment and evaluation of student competencies is necessary to not only consider knowledge retention but also critical thinking, communication, and adaptability skills. A review of the literature was conducted to understand how assignments should change due to the emergence of this disruptive technology. Conversational Artificial Intelligence and its application within the higher education context is uncertain, with disparate practices—in terms of ethical consideration and understanding—across the sector. A case study was conducted in which MSc Management students undertaking a specific module were tasked to use three Artificial Intelligence tools in their report writing of a business, to verify the sources and content provided by the Artificial Intelligence tool, and to critically evaluate the process as well as the output received for each prompt. The paper proposes a collaborative approach to navigate the ethical implementation and utilization of conversational Artificial Intelligence in higher education, advocating for the co-creation of guidelines through forums like Knowledge Cafés, stressing the need to rethink student assignments and its assessment and the adoption of artificial intelligence technologies by students for assignments.

Evaluating the student attainment of graduate attributes through direct method

The attainment of Graduate Attributes, GA-1: Creative and Solution oriented, GA-2: Effective Communicator, and GA-3: Critical Thinker, by the graduates of Modern College of Business and Science, Oman, was evaluated using the Direct Method. Student grades in various courses that embed these GAs were accessed by mining data from the examination archives. The average scores of these GAs were compared with the benchmarked global test values of TTCT, IELTS, and CCTST respectively. The analysis revealed that, while the attainment of GA-1 is satisfactory, GA-2 and GA-3 need focus. Gender differences were noticed wherein female graduates’ attainment of GAs was found to be higher than male graduates. The attainment of GAs was not uniform across various departments in the college. Based on VUCA Theory, Social Penetration Theory, and Theory of Inquiry, this study attains significance in the light of HEIs attempting to provide job-ready students to the industry globally. This study establishes the practical implications of GA assessment in higher education and enables us to identify the GAs to focus on.

Generative AI detection in higher education assessments

AbstractThis chapter presents a critical analysis of generative AI (GenAI) detection tools in higher education assessments. The rapid advancement and widespread adoption of GenAI, particularly in education, necessitates a reevaluation of traditional academic integrity mechanisms. I explore the effectiveness, vulnerabilities, and ethical implications of AI detection tools in the context of preserving academic integrity. My analysis synthesizes insights from various case studies, newspaper articles, and student testimonies to scrutinize the practical and philosophical challenges associated with AI detection. I argue that reliance on detection mechanisms is misaligned with the educational landscape, where AI plays an increasing role. I advocate for a strategic shift toward robust assessment methods and educational policies that embrace GenAI usage while ensuring academic integrity and authenticity in assessments.

Cautionary comments on the cliftonstrengths assessment in higher education.

Cautionary comments on the cliftonstrengths assessment in higher education.

Communication Skills Assessments and Rubrics In The Higher Education: A Meta-Analysis

Effective communication skills are crucial in the higher education system. Consequently, there is a growing concern about how higher education institutions can better equip students with the communication skills required for success in both academic and professional settings. The assessment of communication skills, especially in higher education, is complex and can have a substantial impact on students' academic achievements, preparedness for their careers, and overall professional growth. Due to this disparities and assessment challenges, this meta-analysis sought to answer the following research questions: (a) What are the assessments employed for communication skills in multi-discipline studies in higher education? (b) What are the “key findings” contributing to the issues of communication skills assessment in higher education? In this article, a meta-analysis was conducted to determine the types of communication skill assessments and other key findings concerning the assessment issues in this area. Sources were identified using Scopus, Web of Science, Wiley Online Library, Taylor & Francis Online and Springerlink, and, after inclusion criteria were applied, 21 samples of articles were included in the meta-analysis. The findings demonstrate the existence of issues from the aspects of audience, mode, and purpose of the assessments, which mainly are: (1) impact of language proficiency and ICT competency; (2) communication skills approach to non-scientific audience; (3) blended assessment and feedback communication; (4) use of explicit and direct teaching assessment; (5) critical thinking and opportunity for justifying scientific ideas; and (6) benefit of early and continuous communication tasks. The review finds an extensive gap on the communication skills assessment related to higher-order thinking and problem-solving abilities in junior students. This meta-analysis may be of great interest to university policymakers, language lecturers, and academic lecturers from other disciplines who are searching for the best assessments strategies to measure students' communication skills.

Educationally authentic assessment: reframing authentic assessment in relation to students’ meaningful engagement

ABSTRACT With increasing calls for authentic assessment in higher education, the reference point for authenticity has been questioned. Typically, researchers define authenticity in relation to purposes of higher education, which are contested. Advancing the notion of educational authenticity rather than professional, societal, disciplinary, or developmental authenticity, we shift the reference point from outcomes to the process of meaningful student engagement. To illuminate features of educational authenticity in assessment, we surveyed students about their most interesting, engaging assessment and analysed 302 explanations why that assignment was so engaging. While many students cited real-world connections, consistent with most authentic assessment literature, more described having choice in some aspect of the assignment. We examined how choice, not typically a defining feature of authentic assessment, and real-world connection mattered to students and promoted meaningful engagement. Understanding and designing for educational authenticity can lead to various, valued outcomes highlighted in existing authentic assessment literature.

Instructors’ Beliefs About ESP Assessment: A Case Study

With the world's increasing globalization, English has become the most widely used language for communication, and English for Specific Purposes (ESP) has begun to receive more attention in the twenty-first century due to the effects of globalization and an increase in the need for skilled workers. English for Specific Purposes is a specialized branch of English language learning that focuses on teaching English in particular fields. The assessment process must be customized to the unique characteristics of English for Specific Purposes (ESP), and teacher beliefs can significantly impact on how students are taught and assessed in this subject. Previous research has concentrated on ESP course design and needs assessment in the field. However, teachers' beliefs regarding ESP assessment have not received enough attention. As a result, the study's goal is to investigate the instructors' beliefs regarding ESP assessment in higher education. Three English instructors in a state university’s College of Foreign Languages serve as a sample for this qualitative case study. Semi-structured interviews and seven weeks of reflective journaling were used to gather the data. According to the results, participants believe that ESP assessment has specificity, and it is valuable and accountable. It is also believed that ESP assessment lacks students’ motivation and requires adaptation. The school policy, student motivation, and teacher performance in ESP assessment have influenced these beliefs. To conclude, teacher participation and education are necessary for ESP assessment.

Revolution, evolution, or regression – the transformation of assessments through times of pandemic and AI

In this paper we will discuss the tensions between, revolution, evolution and regression that are shaping assessment practice in higher education. The two major influences on shaping assessment in higher education in recent years have been: (1) the pandemic that pressurised universities to embrace online practice, sometimes temporarily. When the post pandemic pushback restored ‘tried and tested’ face to face practices in exam centres and traditional assessment spaces (e.g. labs), it revealed the potential for adopting online tools and practices and the value of enhancing digital competences for staff and students; and (2) Artificial Intelligence (AI) as a disruptor, which since the autumn of 2022 continues to influence approaches and attitudes to assessment. Institutional concerns for impact on reputation and the focus on integrity and student conduct is shaping the assessment landscape as are the technological and pedagogical opportunities and risks presented by proctoring software and generative AI. We claim that elements of revolution, evolution and regression can be traced in the developing landscape of assessment redesign and technology mediated assessment. The paper will synthesise the outcomes of two projects, at the Centre for Online and Distance Education, University of London: (a) the longitudinal evaluation of assessment practises between 2020 and 2023 and (b) a project that looked at perceptions of academic integrity in the current climate of AI threats and opportunities.