The workload of any academic can be challenging, with a plethora of activities and conflicting priorities. This paper provides an analysis of one of the most time-consuming of teaching roles; the assessment of student work. It begins with a literature review identifying the role of assessment and assessment feedback, and identifies best-practice in assessment feedback. A method of generating high quality feedback efficiently through the use of IT and comment banks has then been critiqued. The method proposed here has been trialled on a large cohort of students and feedback gleaned through two focus groups. Students demonstrated clear preference for feedback generated through the proposed method, and the time savings to academics are dramatic

This report has been commissioned by The Higher Education Academy (The Academy) in January 2010. The report addresses the main questions an academic audience is likely to have about assessment and feedback with technology enhancement by reference to evidence-based, widely available and peer-reviewed materials and papers available on technological support and enhancement for assessment and feedback. Of the 142 references which were recommended, the report reviews the 124 which were accessible, of which 67.7% were peer-reviewed, 28.2% provided quantitative data, and 18.5% provided experimental designs or statistical analyses. Most references focused on the reaction of students and teachers to the use of technology for assessment and feedback. Although it may be ideal to have high-quality evidence before implementing a new assessment approach, in the absence of this level of support, the insights and advice of reputable authors are valued by other practitioners and their messages have impact. The report provides a detailed introduction to and summary of references useful to practitioners on technology-enhanced assessment applicable to Higher (and Further) Education in a UK context. Some of the messages that are supported by evidence in the recommended literature include: Assessment for learning shows an effect size of between 0.34 and 0.46; Tutors can use technology-enhanced methods to implement effective learning designs that would not otherwise be possible because of factors such as time constraints, student numbers and geographical or temporal distribution; Effective regular, online testing can encourage student learning and improve their performance in tests; Student retention and inclusion can be increased by using technology-enhanced methods. Exam anxiety can also be reduced; Using technology-based methods does not disadvantage women or older students; Automated marking can be more reliable than human markers and there is no medium effect between paper and computerized exams; The success of assessment and feedback with technology enhancement lies with the pedagogy rather than the technology itself; technology is an enabler; Technology-enhanced assessment is not restricted to simple questions and clear-cut right and wrong answers, much more sophisticated questions are being used as well; Modern technology can be matched to the learning characteristics of the contemporary learner; The design of appropriate and constructive feedback plays a vital role in the success of assessment, especially assessment for learning. The literature offers detailed guidance on designing effective feedback such as conditions, research-backed principles and a typology, as well as specific advice for the design of audio feedback and peer assessment; What the characteristics of useful technologies to use for assessment and feedback are; Taking a team approach to the creation of technology-enhanced assessment and feedback is valuable because successful implementation requires skills in the application of technology and how to use to the technology itself as well as learning and the subject content; Staff development and support are vital when introducing and developing assessment and feedback with technology enhancement; Testing the assessment and feedback to ensure it is reliable and valid and piloting it with people who are similar to or understand the targeted students are important stages in the development process. A good reporting system can help academics see and analyse the results (including student answers) and will help refine the assessment and feedback; It is important to prepare students to take the assessments that use technology enhancement by practising with similar levels of assessment using the same equipment and methods. This is similar to being able to practise on past papers; The reports generated by many technology-enhanced assessment systems are very helpful in checking the reliability and validity of each test item and the test as a whole. In conclusion, the references that were recommended to us are clearly having an impact on current practice and are found valuable. We would welcome more high-quality statistical studies that offer evidence to support the lessons that practitioners have learned from experience

Smart worksheets are an emerging educational technology that supports self-directed learning and provides targeted feedback on student assessment, while also reducing staff marking workload. However, their adoption may be limited by high cost, limited flexibility, and proprietary ownership. This study presents the design, implementation, and evaluation of an open-source smart worksheet developed using Python. This worksheet provides a flexible, interactive platform for performing calculations, analysing data, and receiving real-time feedback. It supports automated grading, dynamic content updates and graphical data analysis. Student feedback indicates high satisfaction with the worksheet’s ability to enhance conceptual understanding and streamline the analysis process, demonstrating that students perceive similar learning benefits to commercial alternatives. Importantly, its open-source nature ensures adaptability across academic disciplines, enabling its use in diverse educational contexts to support assessment, feedback, and self-directed learning.

The current practice of fraud-detection techniques has been unable to match the growing complexity and sophistication of the offenses of financial fraud, which has relied mostly on manual audit and rule-based systems. The efficacy of machine learning, deep learning, natural language processing, and anomaly-detection methods have, however, been shown in improving the accuracy, speed, and efficiency of AI-based solutions to fraud detection. However, the list of such advancements is combined with considerable problems in the implementation of AI in financial auditing. The data validation is not enough since other controls that include transparency, voluntary compliance, ethics, and cybersecurity are to be implemented to make sure the AI is functioning as expected to prevent the occurrence of fraud. More advanced AI systems have the capability to learn and adapt continuously hence tracing patterns on-demand through vast amounts of structured and unstructured financial information. It is predicted that the widespread use of such technologies will enhance risk-management procedures, since they have the capability of discovering previously unexplored patterns, predicting unethical actions, and developing based on new scams, thus transforming organizations into an active form of detection fraud. The fact that AI has incorporated forensic accounting further improves fraud detection capability among the auditors and makes the financial reporting more accurate and relevant. The highly regulated environment makes financial institutions to work under more restrictive legal provisions including the General Data Protection Regulation (GDPR), the Anti-Money launder (AML) laws, Know-Your-Customer (KYC) laws as well as Sarbanes-Oxley. Such regulations serve as a strong driving force, attracting regulators and the media, and can lead to significant fines where violations take place. Failure to comply is a major reputational, monetary and stakeholder-trust risk, the expenses of which may be prohibitive. As the AI industry progresses over the coming years, there will be a need to have a close joint effort between the regulatory bodies, financial institutions, and AI researchers in order to come up with fundamental rules and minimum standards that would include AI regulation in the area of fraud detection. At the same time, AI-based fraud-detection tools are also a significant security concern: as the level of AI maturity increases, so do the resources of fraudsters, who also surrender deeperfakes and adversarial machine-learning methods as well as deepfakes based on artificial identities to overcome AI defenses.

The core focus of this review is to show how immediate and valid feedback, qualitative assessment influence enhances students learning in a higher education environment. With the rising trend of online education especially in this COVID-19 pandemic, the role of assessment and feedback also changes. Earlier the assessment part is not considered the main focus in learning and teaching in HEIs, but now with the increase in online education, it is observed that the paradigm is shifted toward assessing those activities of students that enhance their learning outcomes. A lot of research work has been done on developing assessment strategies and techniques that can support learning and teaching effectively. Yet, there is limited research that looks at how methods applied in learning analytics can be used and possibly constitutes the assessment process. The objective of this work is to provide an exploratory and comparative study of how assessment and feedback practices can enhance students learning outcomes using AI. The key contribution of this study attempts to capture an outline of the most used artificial intelligence and machine learning algorithms for student success. The results showed that I-FCN performed better than other techniques (ANN, XG Boost, SVM, Random Forest, and Decision Trees) in all measured performance metrics. Also, the result of the comparative analysis study will help the educators, instructors, and administrators on how they could take the advantage of a data-driven approach, design less pressurized, more valid, reliable, constructive assessment findings, and connect the power of assessment and feedback to enhance the learning outcomes.

Abstract Instructors are increasingly using interactive student response systems (SRS) to foster active learning and deepen student understanding in statistics education. Yet most studies focus on either the benefits of SRS or on how students can receive and use feedback, rather than on how instructors can use formative assessment data to inform instructional revisions for the purpose of enacting inclusive pedagogy to reach more learners. This article draws from a case study of an introductory statistics course at a community college to detail a framework for how an instructor can engage in what I call a reciprocal formative assessment and feedback cycle using SRS. This cycle is designed to address the goal of increasing equity—as defined by learning growth for all students—through a structured approach of reflecting on student misconceptions to determine who to reteach, what to reteach, when to reteach, and how to reteach course content.

This paper contributes to the development of learning and academic analytics in Higher Education (HE) by researching how four graphical visualisation methods can be used to present student assessment and feedback data to five stakeholder groups, including students, external examiners and industrialists. The visualisations and underlying data sets are described, together with the results of a questionnaire designed to elicit the perspectives of the stakeholder groups on the potential value of the visualisations. Key findings of this study are that external examiners agree that the visualisations help them to carry out their role and students concur that they can assist with study organisation, relative performance assessment against the wider cohort and even module choice. All stakeholder groups were positive about the benefits of graphical visualisations in this HE context and supported an increased use of visualisations to assist with data interpretation. (Published: 30 July 2018) Citation: Research in Learning Technology 2018,  26 : 1997 -  http://dx.doi.org/10.25304/rlt.v26.1997 View Supplementary material

This study examined students' perceptions of the usefulness of assessment feedback provided by ReMarksPDF and provides a summary of positive and negative aspects of different types of feedback annotations. Students randomly received one of 7 different combinations of feedback. 74.9% of students found ReMarksPDF feedback better than that they have received in the past and 73.2% of students agreed or strongly agreed that other units should adopt the ReMarksPDF system. Students found the mark tally table, assessment rubric, spider chart, spider chart (with average), colour coding and smileys to be significantly valuable feedback in that order of preference. Females gave higher ratings than males on all feedback types, except spider charts, which were equally highly rated by males. Respondents indicated that ReMarksPDF feedback was easy to read and understand and that it was beneficial to have comments appear in a side column note.

Abstract In recent years, there has been a growing body of research on student voice in the context of higher education, generating significant insights for pedagogical improvement. This systematic literature review aims to examine studies on student voice from 2011 to 2022, specifically those concerned with assessment and feedback in higher education. The review draws on 38 empirical studies and identifies the increasing use of mixed-methods designs in student voice research related to assessment and feedback. The analysis of these studies highlights that student voice research can improve students’ experiences, change teachers’ practices and inform university support concerning assessment and feedback. The review finds, however, that most studies were conducted in, or on students from, developed countries. It is necessary for researchers to engage students from different backgrounds to investigate their experiences of assessment and feedback. The results of the review also suggest that more longitudinal, mixed-methods studies should be conducted to generate further critical insights. Future research should regard students as partners in effective assessment and feedback practices, and a focus should be placed on students developing assessment and feedback literacy.

: Assessment and feedback mechanisms are essential components towards effective teaching in higher education and are continuously monitored. The annual student satisfaction survey in UK higher education collects students’ perception on those dimensions and issues results to assist institutions identify their weaknesses and amend their strategies and improve their teaching effectiveness. This study explores assessment and feedback as predictors for overall student satisfaction. It focuses on business schools mainly and uses the officially published dataset. Following a regression analysis approach, it can be concluded that there is evidence to support the claim that assessment and marking can be used as predictors for overall student satisfaction in this subdomain. The significance of the study lies in the fact that universities consider assessment and feedback as of key importance for improving student experience. It is thus critical for the institutions to gain a better understanding on whether those factors can be safely used as predictors of overall student satisfaction, something that is related to university ranking tables. Results in the study, demonstrate some important aspects of this and indicate that improved quality in marking and feedback can have a positive effect in student satisfaction. A more comprehensive study can unfold additional dimensions of the survey and shed light on how students perceive marking, assessment and feedback in higher education in general.

Abstract Student frustration with the level and content of feedback is well-noted, especially in relation to new or unfamiliar assessment types. At the same time, students are often unlikely to engage with marking and feedback processes, including the use of rubrics or marking criteria before submission. The aim of this project was to build students’ assessment and feedback literacy through engagement sessions both before and after submission in two departments at a UK university. Following discussions with students on their perceptions of feedback, assessment-specific marking criteria were developed. Students were provided with an opportunity to discuss these criteria and assess exemplars before submission and then, after return of marks, discussed the helpfulness of the feedback provided. Feedback Studio® was used to provide electronic feedback via comment libraries, general comments and qualitative rubrics. Both staff and students found that the combination of engagement sessions and online feedback was a positive improvement, although questions were raised about the timing and frequency of engagement sessions and students’ ability to apply marking criteria to their own work. The conclusions summarise recommendations for the use of timetabled engagement sessions to support students in the use of assessment-specific marking criteria.

Effective student feedback is fundamental to enhancing learning outcomes in higher education. While traditional assessment methods emphasise both achievements and development areas, the process remains time-intensive for educators. This research explores the application of cognitive computing, specifically open-source Large Language Models (LLMs) Mistral-7B and CodeLlama-7B, to streamline feedback generation for student reports containing both Python programming elements and English narrative content. The findings indicate that these models can provide contextually appropriate feedback on both technical Python coding and English specification and documentation. They effectively identified coding weaknesses and provided constructive suggestions for improvement, as well as insightful feedback on English language quality, structure, and clarity in report writing. These results contribute to the growing body of knowledge on automated assessment feedback in higher education, offering practical insights for institutions considering the implementation of open-source LLMs in their workflows. There are around 22 thousand assessment submissions per year in the School of Computer Science, which is one of eight schools in the Faculty of Engineering and Physical Sciences, which is one of seven faculties in the University of Leeds, which is one of one hundred and sixty-six universities in the UK, so there is clear potential for our methods to scale up to millions of assessment submissions. This study also examines the limitations of current approaches and proposes potential enhancements. The findings support a hybrid system where cognitive computing manages routine tasks and educators focus on complex, personalised evaluations, enhancing feedback quality, consistency, and efficiency in educational settings.