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Intelligent systems of essay grading constitute important tools for educational technologies. They can significantly replace the manual scoring efforts and provide instructional feedback as well. These systems typically include two main parts: a feature extractor and an automatic grading model. The latter is generally based on computational and artificially intelligent methods. In this work, we focus on the feature extraction part. More precisely, we focus on argumentation and discourse-related features, which constitute high-level features. We discuss some state-of-the-art systems and analyze how argumentation and discourse analysis are used for extracting features and providing feedback.

English academic writing is fundamental at tertiary level as it is the main medium of communication in the academe. Over the years, ESL lecturers have not only struggled to search for the most suitable teaching approach(es) to teach academic writing but have encountered several other problems in their teaching, such as class size, students' English proficiency levels, heavy workload, and others. Given these circumstances, a review of the literature has indicated there are scant studies that explore the problems faced by ESL lecturers where comparisons are made between public and private universities. Grounded in multiple case studies, four ESL lecturers from two public and two private Malaysian universities were purposively selected for the study. Data were collected from semi-structured in-depth interviews, lesson observations, and document analysis. A total of 12 interview transcripts were transcribed verbatim by the researchers, and data were analysed thematically using thematic analysis and cross-case analysis, where emerging themes were used to present the findings. Findings reveal that ESL lecturers at higher education institutions encountered problems, such as constraints in time, marking issues, students' lack of interests and motivations, lecturers' lack of interest in writing, lecturers' lack of content and pedagogical content knowledge, and lack of institutional support. Hence, professional support should be given to ESL lecturers to overcome their teaching problems, and future research should explore the strategies employed by ESL lecturers to alleviate their teaching problems.

Although there are claims that ChatGPT, an AI-based language model, is capable of assessing the writing of L2 learners accurately and consistently in the classroom, a number of recent studies have shown discrepancies between AI and human raters. Furthermore, there is a lack of studies investigating the intrareliability of ChatGPT scores. Accordingly, this study aimed to examine the accuracy and consistency of ChatGPT compared to teachers, as well as with itself, after being trained on a rubric. To accomplish this goal, the study adopted a quantitative correlational non-experimental design. A dataset of 100 writing assignments, submitted by a cohort of B1-level students at an international branch university in Egypt, was analyzed quantitatively. These assignments were initially evaluated and moderated by trained teachers (n=11), and subsequently, the same assignments were also assessed twice by ChatGPT. The findings indicated that teachers' scores exhibited a higher level of accuracy compared to those generated by ChatGPT. The results also revealed that ChatGPT exhibits a moderate, yet questioned, level of intra-rater reliability. The weak-to-moderate correlations between ChatGPT and teacher scores raise concerns about the accuracy and consistency of ChatGPT's scoring of writing assignments. The implications of the findings highlight the potential applications and limitations of ChatGPT in L2 writing assessment. This study contributes to the ongoing discourse on the use of AI technologies in language education and provides insights into the accuracy and reliability of ChatGPT as an evaluation tool for L2 writing.

Duolingo has been collecting feedback from education users for the past seven years in order to make the app useful as a classroom tool, in addition to its ongoing use in informal learning settings. To understand teachers' experiences about Duolingo for classroom and to understand further measures recommended to strengthen the platform for classroom instruction, the purpose of this paper was to examine the research question: What are educators' feedback on Duolingo's school program based on a netnographic analysis of discussion boards? Netnography was used to research publicly available data concerning how educators can use the assessments in Duolingo classrooms as an instructional tool to personalize learning. The netnographic analysis found three themes among educators' comments: (Theme 1) questions about grading, credit, and testing; (Theme 2) the use of grading rubrics; and (Theme 3) requested modifications from Duolingo for the Duolingo school program. In understanding the educators' concerns with Duolingo for Schools, with modifications to connectivity issues and scoring guidelines, educators and students may be better prepared to learn with Duolingo in a classroom setting. With some additional improvements to Duolingo classrooms, educators may benefit from using the progress reporting in Duolingo classrooms as an assessment to support personalized learning.

An objective structured clinical examination (OSCE) has been recognised as a reliable but workload-intensive assessment method across health sciences studies. Though a variety of digital marking tools have been employed to improve marking and feedback provision for OSCEs, many of these require specialist software or maintenance. This pilot study examines the development and trialling of Microsoft Forms as a marking and feedback instrument for an OSCE within a Sport and Exercise Therapy module. This study aims to assess whether the use of a non-specialist digital tool, such as Microsoft Forms, might be able overcome limitations in current assessment procedures and ultimately provide a more effective method for marking and feedback provision for an OSCE. Results from OSCE examiners (N = 8) and students (N = 30) who participated in the pilot indicate that Microsoft Forms does have the potential to provide a more effective experience for examiners and ultimately improve upon feedback provision for students when compared with a paper-based marking tool. However, concerns around the form's ease-of-use may ultimately influence its adoption as a marking instrument above current paper-based methods.

Evaluation is an essential component of the learning process when discerning learning situations. Assessing natural language responses, like short answers, takes time and effort. Artificial intelligence and natural language processing advancements have led to more studies on automatically grading short answers. In this review, we systematically analyze short-answer evaluation studies. We present the development of the field in terms of scientific production features, datasets, and automatic evaluation features. The field has developed with pioneering studies in the US. Researchers generally conduct applications with English datasets. There has been a significant increase in research in recent years with large language models that support many different languages. These models have applications that achieve accuracy close to that of human evaluators. In addition, deep learning models do not require traditional approaches' detailed preprocessing and feature engineering processes. The dataset size trend is 1000 and above regarding the number of responses. It was observed that metrics such as accuracy, precision, and F1 score were used in performance determination. It is seen that the majority of the studies focus on scoring or rating. In this context, there needs to be more literature on the context of evaluation system applications that can provide descriptive and formative feedback. In addition, the developed assessment systems must be actively used in learning environments.

In a higher education context, students are expected to take charge of their learning by deciding "what" to learn and "how" to learn. While the learning analytics (LA) community has seen increasing research on the "how" to learn part (i.e., researching methods for supporting students in their learning journey), the "what" to learn part is still underinvestigated. We present a case study of curriculum analytics and its application to a dataset of 243 students of the bachelor's program in the broad discipline of health sciences to explore the effects of course choices on students' academic performance. Using curriculum metrics such as grading stringency, course temporal position, and duration, we investigated how course choices differed between high- and low-performing students using both temporal and sequential analysis methods. We found that high-performing students were likely to pick an elective course of low difficulty. It appeared that these students were more strategic in terms of their course choices than their low-performing peers. Generally, low-performing students seemed to have made suboptimal choices when selecting elective courses; e.g., when they picked an elective course of high difficulty, they were less likely to pick a following course of low difficulty. The findings of this study have design implications for researchers, program directors, and coordinators, because they can use the results to (i) update the course sequencing, (ii) guide students about course choices based on their current GPA (such as through course recommendation dashboards), (iii) identify bottleneck courses, and (iv) assist higher education institutions in planning a more balanced course roadmap to help students manage their workload effectively.

A large body of evidence shows that many ungrading practices are as good or better than conventional approaches at supporting learning outcomes. Much of the research on student perceptions of ungrading, however, is based on individual case studies which, although informative, are often anecdotal, not systematically implemented, and tend to emphasize the instructor's perspectives. Building on this literature, we offer a systematic study that asks: how do students perceive pedagogical practices designed by instructors to support an ungrading strategy? To answer this question, we conducted a survey of students across a range of disciplines and a variety of ungrading approaches to assess how they perceive their learning experiences in these courses as compared to others. Findings indicate that students generally perceive that ungrading practices improve their relationship with their instructor; enhance their engagement, agency, enjoyment, and interest; foster their intrinsic motivation and focus on learning; and facilitate their creativity. While many students reported reduced stress, others reported that the unfamiliarity and uncertainty of ungrading increased their stress. Gaining a better understanding of how students react to these pedagogical techniques can help instructors improve their practices.

Studies indicate that racial disparities in STEM achievement or equity grade gaps are associated with faculty fixed mindset beliefs; however, whether specific instructional beliefs are linked to student academic achievement remains unclear. We surveyed 216 STEM faculty to assess their mindset and instructional beliefs and linked these to detailed student transcript data (n = 31,361). Results reveal that faculty with fixed mindset beliefs also endorsed more traditional instructional beliefs regarding assessment, grading, and diversity. Further, the endorsement of these beliefs was associated with larger equity grade gaps. Analysis of faculty characteristics indicate that male faculty, full professors, and instructors in Physical Sciences tended to hold instructional beliefs that are linked to larger equity grade gaps.

Background: Peer assessment has played an important role in large-scale online learning, as it helps promote the effectiveness of learners' online learning. However, with the emergence of numerical grades and textual feedback generated by peers, it is necessary to detect the reliability of the large amount of peer assessment data, and then develop an effective automated grading model to analyse the data and predict learners' learning results. Objectives: The present study aimed to propose an automated grading model with reliability detection. Methods: A total of 109,327 instances of peer assessment from a large-scale teacher online learning program were tested in the experiments. The reliability detection approach included three steps: recurrent convolutional neural networks (RCNN) was used to detect grade consistency, bidirectional encoder representations from transformers (BERT) was used to detect text originality, and long short-term memory (LSTM) was used to detect grade-text consistency. Furthermore, the automated grading was designed with the BERT-RCNN model. Results and Conclusions: The effectiveness of the automated grading model with reliability detection was shown. For reliability detection, RCNN performed best in detecting grade consistency with an accuracy rate of 0.889, BERT performed best in detecting text originality with an improvement of 4.47% compared to the benchmark model, and LSTM performed best with an accuracy rate of 0.883. Moreover, the automated grading model with reliability detection achieved good performance, with an accuracy rate of 0.89. Compared to the absence of reliability detection, it increased by 12.1%. Implications: The results strongly suggest that the automated grading model with reliability detection for large-scale peer assessment is effective, with the following implications: (1) The introduction of reliability detection is necessary to help filter out low reliability data in peer assessment, thus promoting effective automated grading results. (2) This solution could assist assessors in adjusting the exclusion threshold of peer assessment reliability, providing a controllable automated grading tool to reducing manual workload with high quality. (3) This solution could shift educational institutions from labour-intensive grading procedures to a more efficient educational assessment pattern, allowing for more investment in supporting instructors and learners to improve the quality of peer feedback.

Background: Education is often the primary arena for exploring and integrating new technologies. AI and human-machine communication (HMC) are prevalent in the classroom, yet we are still learning how student perceptions of these tools will impact education. Objectives: We sought to understand student perceptions of credibility related to written feedback attributed to a human or an AI grader (Study One). We also investigated how corrective messages containing verbal immediacy and social support influenced student perceptions of an AI grader's credibility based on feedback in an evaluated essay (Study Two). Methods: We used an online experimental design to assess the perceived credibility of a grader. In Study One, we randomly assigned students (N = 155) to a condition that contained a paragraph they were told was evaluated by a human or an AI grader. In Study Two (N = 222), we investigated ways of increasing perceptions of an AI grader's credibility by writing messages with higher/lower levels of immediacy and social support. Results: In Study One, the students rated both the human and AI grader as credible (yet rated the AI grader lower on goodwill). The data suggest that students in Study Two attributed more goodwill (i.e., caring) to the AI grader when the feedback included more verbal immediacy. Conclusions: Our results highlight the importance of student perceptions and communication styles when integrating technology into education. The two studies imply that students viewed the human and AI graders as competent, caring, and trustworthy, specifically when feedback included more immediacy cues.

English as a medium of instruction (EMI) is commonly adopted as a strategy for higher education internationalisation. While there are numerous studies on the teaching practices of EMI programmes, the relationship between EMI and structural inequalities has been less investigated, especially in "universal" higher education systems. To address the research gap, this study investigates the EMI practices of two Taiwanese higher education institutions (HEIs) under current government initiatives. Qualitative data from policy documents and semi-structured interviews are analysed with an institutional logics approach and reflexive thematic analysis. The findings suggest that while state, managerial, and academic logics jointly shape EMI strategies in the public university case, EMI practices in the private university of technology case are predominantly driven by market and managerial logics and challenged by academic logic. Furthermore, this study reveals the structural "stuckness" encountered by the private case. In Taiwan's hierarchical higher education system, the promotion of EMI could result in widening horizontal inequalities among HEIs. More specifically, under the EMI grading certification scheme for students and the tiered award system for HEIs, the majority may be left behind whereas the few with linguistic capital are spotlighted. Therefore, this study concludes that in light of organisational conditions, policymakers should allow greater flexibility for HEIs to develop performance indicators appropriate to their students' needs.

Quantitative measures of systematic differences in OSCE scoring across examiners (often termed examiner stringency) can threaten the validity of examination outcomes. Such effects are usually conceptualised and operationalised based solely on checklist/domain scores in a station, and global grades are not often used in this type of analysis. In this work, a large candidate-level exam dataset is analysed to develop a more sophisticated understanding of examiner stringency. Station scores are modelled based on global grades--with each candidate, station and examiner allowed to vary in their ability/stringency/difficulty in the modelling. In addition, examiners are also allowed to vary in how they discriminate across grades--to our knowledge, this is the first time this has been investigated. Results show that examiners contribute strongly to variance in scoring in two distinct ways--via the traditional conception of score stringency (34% of score variance), but also in how they discriminate in scoring across grades (7%). As one might expect, candidate and station account only for a small amount of score variance at the station-level once candidate grades are accounted for (3% and 2% respectively) with the remainder being residual (54%). Investigation of impacts on station-level candidate pass/fail decisions suggest that examiner differential stringency effects combine to give false positive (candidates passing in error) and false negative (failing in error) rates in stations of around 5% each but at the exam-level this reduces to 0.4% and 3.3% respectively. This work adds to our understanding of examiner behaviour by demonstrating that examiners can vary in qualitatively different ways in their judgments. For institutions, it emphasises the key message that it is important to sample widely from the examiner pool via sufficient stations to ensure OSCE-level decisions are sufficiently defensible. It also suggests that examiner training should include discussion of global grading, and the combined effect of scoring and grading on candidate outcomes.

In 2019, the four authors were teaching mathematics at the same public middle school in a suburban district of the mid-atlantic. They had been seeking ways to make adjustments to assessment and grading practices to support students in their learning, and after reading Joe Feldman's "Grading for Equity" (2018), they were inspired to make some significant changes to their grading practices. It started with small, incremental changes, and then three years later the entire team of eight teachers were using a new grading system. During the 2022-23 school year, students' behaviors and attitudes about their work and grades had improved. In this article, the authors first identify the goals they had for improving their grading practices. Then, they explain how they changed their grading practices to value the learning process. Finally, they share how they have observed these changes promote a positive shift in students' mindsets about their grades and learning.

Addressing calls to develop assessment theories for integrated teaching and learning, we propose an evidence-informed perspective on grading in Science, Technology, Engineering, Arts and Mathematics (STEAM) education. We leveraged a qualitative collective case study design to generate rich profiles of three exemplary STEAM teachers' grading approaches and practices. Data sources included semi-structured interviews and artefacts of teachers' instruction and assessment practice. We analysed qualitative data from interviews and artefacts using a general inductive approach. The teachers in our study pushed back against 'objective' views of grading, whereby grades are composites of summative assessments, in favour of informed and contextualised grading, which aims to document and support a negotiated understanding of each student's learning journey. Teachers' grading practices aligned with a hermeneutic approach to classroom assessment validity: the teachers (a) collected and interpreted a wide range of evidence of student (re)learning; (b) centred students' perspectives and evidences; and (c) employed their professional judgement to determine students' grades. Teachers characterised grading as a process of accounting for all available evidence, blurring the boundaries between formative and summative assessment. Documenting the learning process, rather than focusing on products, can support deeply integrated learning. Importantly, the teachers supported students in documenting their own learning and negotiating their grades with reference to self-generated evidence. This practice stands to reduce power imbalances between students and teachers and foster students' self-regulated learning. Our findings inform a framework which STEAM educators can use to guide grading in integrated classrooms, an enduring challenge for integrated learning.

This paper reports results from an Australian study into how teachers see features of quality in student work and connect these to next-step teaching. Data were drawn from a national 3 year project investigating teacher judgement using A-E standards. The project developed scaled exemplars of authentic student written performance assessments to support teacher judgement and inform next-step teaching and learning. Fifty-seven participants created evaluative explanations of grading decisions (i.e. cognitive commentaries), wrote reflective responses and participated in online meeting discussions of their cognitive commentaries. These data were examined using qualitative content and thematic analyses. Findings highlighted how stated standards influence teachers' judgements of student work. Variation was evident in teachers' focus on (1) content (i.e. core skills vs. extended thinking), (2) specificity of improvement points and suggested teaching strategies and (3) the connection between identified areas for improvement and teaching strategies. Most teachers were able to identify specific points for improvement. However, in the main, they selected next-step teaching strategies that were general rather than targeted and specific. Teachers reflected that structured analysis helped direct their thinking and judgements, targeting attention on next-step teaching. The results suggested that teacher education and professional development should focus on supporting teachers to link specific teaching strategies to identified student weaknesses. This study found that artefacts, such as cognitive commentaries that connect assessment, teaching and learning, can help build professional knowledge and expertise, which remain key components of teacher assessment literacy.

Automatic grading models are valued for the time and effort saved during the instruction of large student bodies. Especially with the increasing digitization of education and interest in large-scale standardized testing, the popularity of automatic grading has risen to the point where commercial solutions are widely available and used. However, for short answer formats, automatic grading is challenging due to natural language ambiguity and versatility. While automatic short answer grading models are beginning to compare to human performance on some datasets, their robustness, especially to adversarially manipulated data, is questionable. Exploitable vulnerabilities in grading models can have far-reaching consequences ranging from cheating students receiving undeserved credit to undermining automatic grading altogether--even when most predictions are valid. In this paper, we devise a black-box adversarial attack tailored to the educational short answer grading scenario to investigate the grading models' robustness. In our attack, we insert adjectives and adverbs into natural places of incorrect student answers, fooling the model into predicting them as correct. We observed a loss of prediction accuracy between 10 and 22 percentage points using the state-of-the-art models BERT and T5. While our attack made answers appear less natural to humans in our experiments, it did not significantly increase the graders' suspicions of cheating. Based on our experiments, we provide recommendations for utilizing automatic grading systems more safely in practice.

Machine learning methods can be used to reduce the manual workload in exam grading, making it possible for teachers to spend more time on other tasks. However, when it comes to grading exams, fully eliminating manual work is not yet possible even with very accurate automated grading, as any grading mistakes could have significant consequences for the students. Here, the evaluation of an automated grading approach is therefore extended from measuring workload in relation to the accuracy of automated grading, to also measuring the overall workload required to correctly grade a full exam, with and without the support of machine learning. The evaluation was performed during an introductory computer science course with over 400 students. The exam consisted of 64 questions with relatively short answers and a two-step approach for automated grading was applied. First, a subset of answers to the exam questions was manually graded and next used as training data for machine learning models classifying the remaining answers. A number of different strategies for how to select which answers to include in the training data were evaluated. The time spent on different grading actions was measured along with the reduction of effort using clustering of answers and automated scoring. Compared to fully manual grading, the overall reduction of workload was substantial--between 64% and 74%--even with a complete manual review of all classifier output to ensure a fair grading.

This is a protocol for a Campbell Review following JBI scoping review methodology. The objectives are to answer the following questions: What has been reported in the literature about collaborative learner-educator design, implementation, or evaluation of learner assessment in health professional education? (1) Where is learner-educator co-creation of assessment occurring? (i.e., which disciplines, course types, level of learner, year of study). (2) What course assessment decisions are influenced or being made together? (i.e., assessment instructions and/or grades). (3) How much influence do learners have on decision-making? (i.e., where does it fall on Bovill and Bulley's ladder of participation). (4) How do learners and educators go about making decisions together? (i.e., discussion or voting, with a whole class or portion of the class). (5) What are the perceived benefits, disadvantages, barriers, and/or facilitators reported by the authors?

School leaders struggle in their efforts to implement standards-based grading reforms in large part because they fail to address the inconsistency in grading practices among individual teachers. This article describes three crucial steps necessary to gain greater consistency in teachers' grading practices: (1) Reaching consensus on a purpose statement for grading; (2) using grading scales with four to seven categories of student performance; and (3) reporting multiple grades to describe academic achievement and non-academic aspects of students' performance separately. These steps provide the necessary foundation for successful implementation of standards-based and competency-based grading reforms.

Online learning has grown due to the advancement of technology and flexibility. Online examinations measure students' knowledge and skills. Traditional question papers include inconsistent difficulty levels, arbitrary question allocations, and poor grading. The suggested model calibrates question paper difficulty based on student performance to improve understanding assessment. The suggested student assessment system paradigm involves determining difficulty, creating the exam, and assessing the student. Based on the previously established relationship between question difficulty and right responses, questions are computed and then divided into difficulty categories. This model improves testing by adapting to the student's ability in real-time. This method ensures that all students are graded uniformly and fairly using pre-determined questions and criteria. The methodology can also cut exam creation and administration time, freeing up teachers and administrators to focus on other assessment tasks. It considers more evidence, learner-centered assessment can help employers evaluate candidates more accurately and meaningfully. It might boost academic productivity by letting assessors quickly write high-quality papers and save up time for deeper investigation and experimentation. This may accelerate scientific progress. Automatic paper generation raises ethical questions about research validity and reliability.

Letter grading systems in education have been widely accepted as a strong medium to assess the educational performance of students across the world. It has been a successful system for many years because it can motivate students to achieve satisfactory grades in a course. However, recent studies indicate that grades can also foster anxiety and demotivate students to learn. This fact can be challenging because many investigators claim that motivation and student engagement are key to student success, especially in online education. This situation is even more problematic with the recent outbreak of COVID-19. There has been a major shift in the education sector from face-to- face instruction to online instruction. To mitigate the negative effects of the grading system, incorporating a partial student self-grading model in remote learning is proposed. The partial self- grading student model can potentially increase motivation, positive attitude and decrease the temptation to cheat on assignments. Another benefit of this system is that it can create a platform where students and instructors can discuss the grades given, thus creating a two-way learning system instead of a one-way learning system. The powerful impacts and benefits of the partial student self-grading model in online education are explored and discussed.

The purpose of this study is to examine the impact of artificial intelligence (AI) on online assessment in the context of opportunities and threats based on the literature. To this end, 19 articles related to the AI tool ChatGPT and online assessment were analysed through rapid literature review. In the content analysis, the themes of "AI's assistance role", "automatic grading and feedback", "improving assessment" and "time benefit" were obtained in the opportunities category, while the themes of "academic integrity concern", "reliability issues" and "adaptability issues" were obtained in the threats category. The impact of AI on online assessment was explained within the scope of these themes. The results revealed that the most emphasis was placed on "improving assessment" themes in the opportunities category, and "academic integrity concern" themes in the threats category. At the end of this preliminary review, it was revealed that more studies investigating the integration of AI to online assessment are needed and all educational institutions, especially distance education institutions, should take measures to ensure the ethical use of AI.

Differentiated instruction, an inclusive teaching strategy incorporating essential skills while individualizing curriculum at current instructional levels, has little inquiry into its effect on standards-based grading system within the middle grades setting. This qualitative study was designed to better understand teacher perceptions of differentiated instruction in a standards-based grading middle school. Systems theory framed the study by exploring differentiated instruction and standards-based grading as parts, combining parts toward the whole of the educational system at one specific middle school, and looking at the emergent characteristics of teachers' perceptions that arose through qualitative themes. Focusing on teacher perceptions in a middle grades' context illuminates possible connections while explicating teachers' views of the phenomenon within the larger scope of education at one middle school.

Grading approaches differ in undergraduate higher educational settings, but most often reflect traditional systems that support an unbalanced power dynamic that does not acknowledge or support continuous learning. These practices are then taken up by pre-service teachers and applied within their future classrooms with children. Using Critical Theory and Transformative Learning Theory, we analyzed our own grading practices for the purpose of designing and implementing a more equitable approach to assessment. The process led us to think about what we want grades to represent and what matters most to us as educators. The paper includes our process and the challenges we have faced. We hope it is a valuable resource for educators who are considering adjustments to their grading systems.

Specifications grading is a relatively recent approach to assessing student learning. In this approach, students make progress toward completion of a course by demonstrating mastery of specific skills or material. The assessment tools are short, frequent exercises that can be attempted multiple times until mastered. This contrasts with the traditional, exam-based assessment of student learning. There are multiple benefits to the specifications grading-based strategy, including reduced test anxiety, better knowledge retention, and increased flexibility. In this study, specifications grading was implemented into an upper-level biochemistry course at a private, liberal arts university. The student cohort consisted almost exclusively of junior and senior biochemistry, biology, and chemistry majors. Students earned points for demonstrating mastery on each of 12 short quizzes in addition to points earned from laboratory exercises and on the cumulative final exam. Student attitudes were assessed using three surveys that were administered at the beginning, middle, and end of the course. The survey results indicated that the students had overall favorable opinions of the specifications grading approach and its use in this course. A comparison of student performance on the quizzes to their performance on the final exam showed that the students learned and retained the course material. Combining the survey and performance data, we demonstrated that the students' perceptions of their learning correlated well with their performances on the specifications grading tools. Together, these results indicated that specifications grading is an effective approach to assessing student learning and to maintaining student enthusiasm in an upper-level biochemistry course.

Background/purpose: ChatGPT is an artificial intelligence program released in November 2022, but even now, many studies have expressed excitement or concern about its introduction into academia and education. While there are many questions to be asked, the current study reviews the literature in order to reveal the potential effects of ChatGPT on education as a whole. The potential implications, possibilities, and concerns about the use of ChatGPT in education are disclosed as mentioned in the literature. Materials/methods: The data of the study were collected and then subjected to a systematic review. Research findings were analyzed according to the themes and categories identified. Results: The results of this research were examined under themes according to the positive and negative aspects of ChatGPT. The positive categories and sub-categories of ChatGPT's integration into education were determined, and the relationship between education and artificial intelligence determined. Similarly, the negative category highlighted the potential negative impact of artificial intelligence on educational processes. Conclusion: The reviewed research evaluated and discussed the impact of AI on education and training processes. In conclusion, this review revealed the critical applications of ChatGPT for educational settings and the potential negative impact of its application. The findings established how ChatGPT and its derivatives would create a new paradigm in education as a whole.

Peer assessment (PA) is widely implemented in higher education, and it can play an important role in online learning by connecting students to their peers and enabling feedback from multiple sources. However, high-quality feedback is not guaranteed. And students tend not to use peer feedback. Preparing for, designing, and implementing PA can be challenging for instructors, especially those who are relatively new to online teaching. This paper proposes guidelines for preparing for, designing, and implementing PA based on a review of empirical studies. The paper first reviews the benefits of PA, the important role that PA can play in online courses, and the need for effective preparation, design, and implementation of PA activities in online courses. Afterwards, based on a review of empirical studies that aim to improve the effectiveness of technology-facilitated PA interventions, the paper proposes guidelines for PA activities.

During the pandemic, there were many discussions concerning how the pandemic was affecting students' learning and their ability to function in a remote classroom. However, the day-to-day routines and instructional strategies of teachers were being overlooked. The purpose of this research is twofold. The first is to provide examples and information on how teachers had to adapt their instructional practices, assessments, and day-to-day routines to address the needs of students during the COVID-19 pandemic. The second purpose is to use that information to provide school districts and parents with the teachers' perspectives. This information helps all stakeholders see what teachers have done, and how they can support educators in a way that will lead to success for students going forward while the education community recovers from any pandemic loss.

Failure is often taken as a given in higher education, as an inevitable part of learning new things. Yet, it remains a part of learning that students tend to fear, and faculty tend to neglect. As faculty, we do not always strategize with or leverage our students' struggles and failures for improved learning. Instead, we hope that students learn from their mistakes and study harder or try harder the next time, because moving on with material in class is necessary to meet learning objectives. In this article, we outline several strategies for using failure advantageously for promoting student growth and learning, and to minimize the stigma of struggle in academia. We make concrete suggestions and outline strategies and resources for faculty to incorporate a "pedagogy of failure" into their work with students and we describe structural barriers to using failure strategically in higher education.

Standards-based grading is an alternative grading system that gives students multiple opportunities to demonstrate proficiency in course standards or objectives. It has the potential to provide flexibility and reduce student anxiety, which are particularly important during difficult times such as the COVID-19 pandemic. In this study, students in a Precalculus course using standards-based grading between Spring 2021 and Spring 2022 were surveyed about how the pandemic had affected their learning and the benefits and challenges of standards-based grading. The students who took the course in Spring 2021, whose previous learning was least affected by the pandemic, were most likely to find standards-based grading beneficial. However, in all three semesters, students on average reported that compared with exams in previous math courses, assessments in the standards-based graded course were lower stakes and would better help them understand the material and to know what to study.

This study aims to examine graders' satisfaction with the open-ended question implementation in one of the open education systems in Türkiye. The quantitative research method was adopted in this study in which the data were collected through a questionnaire developed by the researchers. The graders' satisfaction regarding open-ended question implementation was discussed in three dimensions. These are "the functioning of the open-ended question grading system", "the need for communication/help while using the system" and "the importance of open-ended questions in open and distance learning". Whether the sub-dimension scores of the measurement tool were different from the expected values was examined with the Wilcoxon test. The graders' satisfaction regarding open-ended question implementation was analysed according to gender, title, and course variables. It was observed that the graders' satisfaction levels regarding the "Satisfaction with the grading system" and the "importance of open-ended questions in open and distance learning" were high. It was observed that the satisfaction levels of the graders regarding the "Satisfaction with the grading system", "the need for communication/help arising during using the system" and "the importance of open-ended questions in open and distance learning" did not change according to gender, academic title and the type of course they scored.

Ticks and crosses (TCs) are a common aspect of teachers' classroom practice in relation to assessment in many learning areas including mathematics. Putting TCs in learners' written work is a strategy of feedback. Even though these TCs are frequently used in different types of mathematics assessments, there is limited research in relation to what they actually stand for and what functions they are designed for and especially what purpose they eventually serve in practice. This article emerged from a broader study that aimed at exploring classroom formative assessment practices of Grades 4-6 mathematics teachers, a learning goals and documentary analysis. Since this study was qualitative in nature, we used qualitative, non-probability sampling to recruit respondents according to pre-selected criteria relevant to our research questions. The study participants were 43 qualified and experienced Intermediate Phase mathematics teachers and 95 Grades 4-6 learners from the Tshwane South district, where a phenomenon of low achievement was of great concern. We engaged in document analysis of all the 95 learners' mathematics workbooks. Questionnaires were administered to the 43 teachers. We report on an analysis of teachers' assessment practices of Grades 4-6 learners' mathematics work. We narrate the extent of the use of TCs among teachers from selected schools in Tshwane South district in Gauteng, South Africa. Our analysis shows that while there is prevalent use of TCs among teachers, there are critical gaps in relation to knowledge of TCs in assessing mathematics. We present a qualitative and quantitative data analysis to illustrate how these were used in connection with assessment of learners' mathematics work linked to the concepts of numerical, geometric, and graphical relationships. We use our analysis of the vignettes to explore and argue that teachers use TCs without adequate understanding of what these actually mean in relation to assessment broadly and assessment intended at collecting and clarifying goals for mathematical learning specifically. Despite teachers having mathematical qualifications and a repertoire of experience for teaching, the majority of teachers grappled with understanding mathematical concepts as evidence in how they marked learners' mathematics work. The study also found that teachers' understandings of assessment of mathematics were diverse and largely inconsistent with the formal definitions of mathematics.

Standards-based grading (SBG) is a grading system in which each student is given a set of grades, each reflecting their proficiency in relation to a specific skill. The present study examined K-12 teachers' implementation of SBG practices in a small, suburban Midwestern school district, as well as their perceptions of and confidence in implementing SBG. It also examined these teachers' prior formal and informal opportunities to learn about SBG and how both pre-service and in-service opportunities to learn relate to their implementation of SBG. Results provide evidence of mixed levels of teacher SBG implementation, mixed teacher perceptions of SBG, and mixed teacher confidence in implementing such practices. In addition, the study finds that teachers' prior opportunities to learn about SBG practices formally and informally were variable. Participation in both pre-service and in-service opportunities to learn about SBG were moreover related to SBG implementation, and these relationships were partially or fully mediated by teachers' confidence in implementing SBG practices.

The current paper focusses on the teachers' attitude towards and experiences with e-learning tools at two universities in different phases of e-learning implementation. The study population comprises teachers at university level and a simple random sampling method was used. A total of 45 teachers in bachelor programmes from the Faculty of Agriculture of Kabul University (KU) in Afghanistan filled out the survey. For this study, secondary data on Wageningen University and Research (WUR) in the Netherlands was also used from a survey which was recently conducted by Banihashem et al., (2023) in which 307 teachers participated. Data were analysed through descriptive statistics and the two settings were compared in general terms. The results showed that the majority of the respondents at KU and WUR had a favourable attitude towards e-learning tools and that this attitude is an important enabling or disabling factor in the adoption of technology. The findings also indicated respondents' access and facilities, e.g., at KU for interaction with students mainly among teachers HELMS during COVID-19 and for assessment, individual assignments and quizzes were used. Meanwhile, at WUR and mainly among teachers, for interactive purposes Brightspace assignments (and, for assessment, group assignments), Brightspace grades, individual assignments and rubrics were used. Consequently, this research paper will add to the existing knowledge in this area, potentially having a positive influence on the educational sector, because doing so could help the educational system to improve and to better consider the aspects which influence the attitude of teachers that leads to applying a technology. In addition, we recommend more e-learning research studies in Afghanistan in order to find a solution for educating women in Afghanistan.

This study aims to develop a scale to identify the factors influencing mathematics teachers' grading practices regarding students' in-class performance. The study was carried out with 180 secondary and 140 high school mathematics teachers from the southwestern region of Türkiye. The scale's construct validity was determined using item analysis, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). The EFA results showed that the scale consisted of a 5-point Likert-type scale with 36 items under eight factors (mathematical knowledge and skills, social behaviors, affective skills, effort, homework performance, follow-up test results, academic exam results, and external benchmarks). The scale explained 70.02% of the total variance, with factor loadings ranging from 0.50 to 0.92. The item-total correlations ranged from 0.36 to 0.64, and t-test results for the item discrimination index were significant. The Cronbach's alpha coefficient for the overall scale was calculated to be 0.92. The CFA results indicated that the scale model had a good fit (Chi-square/df = 1.39; RMSEA = 0.051; IFI = 0.98; NNFI = 0.97; CFI= 0.98; and SRMR = 0.062). Based on the findings, the scale is a valid and reliable instrument that may be used in determining the factors influencing mathematics teachers' grading practices.

The aim of this study was to identify process-related indicators for grading group essays. The research participants were students registered in a teacher-training course using an instructional design with face-to-face and digital blended learning. The course required the students in small collaborative groups to design, implement, and evaluate a teaching program using creative pedagogical designs, which were documented using group essays. Four indicators relating to group essays along the course process were collected: (A) group essay grades assessed by different agents, (B) students' other course grades or behaviours (i.e., multiple assessments) as well as (C) comment behaviours and (D) version history behaviours through an online co-editing system (i.e., Google Docs). Statistical analysis results indicated that the instructor's group essay grades were related to the group essay grades assessed by out-group peers (i.e. peers from other groups), online group comment frequencies, and online group comment interaction density.

Language assessment literacy (LAL) remains an uncharted area for many English language lecturers, hindering their ability to effectively bridge the gap between theoretical knowledge and practical application in language assessment within the classroom setting. This research aimed to investigate the language assessment literacy of Thai EFL university lecturers, with a focus on the reciprocal relationship between their knowledge and beliefs and their assessment practices. Employing a multistage sampling method, the study involved 152 Thai EFL university lecturers from eight public universities in Thailand. Participants completed a language assessment literacy questionnaire and assessment practice inventory. The findings indicated that the majority of Thai EFL university lecturers demonstrated a moderate level of language assessment knowledge. Notably, they exhibited higher proficiency in articulating clear learning objectives but lower proficiency in providing effective feedback. Moreover, the lecturers expressed that performance assessment held the greatest significance in classroom assessment, while traditional assessment methods and non-achievement-based grading were deemed less important. Additionally, it was revealed that lecturers often communicated grading criteria in advance, but they paid little attention to item analysis and test revision based on its analysis. These findings are expected to contribute to the understanding of English language lecturers' language assessment literacy and provide a basis for further development in this area.

Traditional grading methods have faced criticism for potentially exacerbating educational disparities, leading to the proposal of grading equity practices as a remedy (Feldman, 2019). We conducted an analysis with survey data from 506 Arkansas public school teachers, utilizing an Ordinary Least Squares (OSL) regression. Our findings illuminate key determinants of support for grading equity practices. Liberal and moderate-leaning teachers are more likely to favor grading equity, in contrast with their conservative counterparts. Additionally, elementary-level teachers, core and programmatic teachers, and educators with advanced degrees demonstrated heightened support for equitable grading. Personal responsibility levels and years of teaching experience do not significantly associate with support. This research contributes to the discourse on equitable grading, offering valuable insights and directions for future studies.

The paper examines the degree of digitalization of Academic Writing and Information Literacy (hereafter AW & IL) course in the countries of the post-Soviet era. Numerous research demonstrated that digital transformation has taken place toward the teaching of AW & IL in most of the developed countries and beyond, yet little is studied about the digitalization in countries of the former Soviet Union that have passed a long way to align their education with the globe. Keeping up with the four main categories of digitalization, the paper will look at its application as a part of curriculum development, assessment, students' competency, and university maintenance. The paper employs a phenomenological approach confirmed through the qualitative analysis of 18 in-depth interviews conducted with AW & IL instructors from nine countries. The study revealed that recently AW & IL course has moved through partial digitalization, providing that the university stakeholders do not exert any necessary support.