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Many 4-year public institutions face significant pedagogical challenges due to the high ratio of students to teaching team members. To address the issue, we developed a workflow using the programming language R as a method to rapidly grade multiple-choice questions, adjust for errors, and grade answer-dependent style multiple-choice questions, thus shifting the teaching teams' time commitment back to student interaction. We provide an example of answer-dependent style multiple-choice questions and demonstrate how the output allows for discrete analysis of questions based on various categories such as Fundamental Statements or Bloom's Taxonomy Levels. Additionally, we show how student demographics can be easily integrated to yield a holistic perspective on student performance in a course. The workflow offers dynamic grading opportunities for multiple-choice questions and versatility through its adaptability to assessment analyses. This approach to multiple-choice questions allows instructors to pinpoint factors affecting student performance and respond to changes to foster a healthy learning environment.

High school graduation is a pivotal milestone that can shape students' future, offering them opportunities for higher education and gainful employment. Because research shows grade point averages and course failures to be predictive of high school graduation, it is important for students' life outcomes that their teachers assign them equitable grades. However, recent research has scrutinized how schools have traditionally graded students, calling for new approaches such as minimum grading. In this analysis, we simulate the economic impacts of implementing a minimum grading policy statewide during the ninth-grade year of the cohort of students who were in ninth grade in 2015-16 in Arkansas. On average, in Arkansas public schools, we estimate that minimum grading would affect about one (narrow bound) to 3%(broad bound) of students, increase cost-effectiveness by 6% (narrow bound) to 23% (broad bound), largely varying across districts, and have essentially no impact on the return on investment. The application of minimum grading shifted 1% of failing course grades to passing, thereby positioning 10% of previously failing students to pass. These findings suggest that minimum grading could serve as a cost-effective strategy to enhance graduation rates in districts with higher rates of affected students, without necessitating additional educational spending.

Background: Automatically graded programming assignments provide instant feedback to students and significantly reduce manual grading time for instructors. However, creating comprehensive suites of test cases for programming problems within automatic graders can be time-consuming and complex. The effort needed to define test suites may deter some instructors from creating additional problems or lead to inadequate test coverage, potentially resulting in misleading feedback on student solutions. Such limitations may reduce student access to the well-documented benefits of timely feedback when learning programming. Objectives: We evaluate the effectiveness of using Large Language Models (LLMs), as part of a larger workflow, to automatically generate test suites for CS1-level programming problems. Methods: Each problem's statement and reference solution are provided to GPT-4 to produce a test suite that can be used by an autograder. We evaluate our proposed approach using a sample of 26 problems, and more than 25,000 attempted solutions to those problems, submitted by students in an introductory programming course. We compare the performance of the LLM-generated test suites against the instructor-created test suites for each problem. Results and Conclusions: Our findings reveal that LLM-generated test suites can correctly identify most valid solutions, and for most problems are at least as comprehensive as the instructor test suites. Additionally, the LLM-generated test suites exposed ambiguities in some problem statements, underscoring their potential to improve both autograding and instructional design.

The COVID-19 pandemic had a profound impact on education, forcing many teachers and students who were not used to online education to adapt to an unanticipated reality by improvising new teaching and learning methods. Within the realm of virtual education, the evaluation methods underwent a transformation, with some assessments shifting towards multiple-choice tests while others attempted to replicate traditional pen-and-paper exams. This study conducts a comparative analysis of these two types of evaluations, utilizing real data from a virtual semester during the COVID-19 pandemic at an Ecuadorian institution. It aims to assess the impact of transitioning from one evaluation method to the other, revealing fundamental structural differences. These differences can lead to disparities that unfairly advantage or disadvantage certain student groups based on the evaluation method used. Beyond identifying the causes of these discrepancies, the study reveals that, for the specific case and dataset analyzed, the shift to virtual education led to a significant and abrupt increase in passing percentages. Moreover, under one specific type of evaluation, there is a possibility that a minimum of 21.1% of students may have passed a course due to cheating or other forms of academic dishonesty, while at least 5.5% could have failed that course despite possessing the necessary capabilities.

Different sources of assessment evidence are reviewed during International Baccalaureate (IB) grade awarding to convert marks into grades and ensure fair results for students. Qualitative and quantitative evidence are analysed to determine grade boundaries, with statistical evidence weighed against examiner judgement and teachers' feedback on examinations. A trial was conducted to explore how examiners' grading decisions were influenced by having access to statistical evidence. Grade awards were replicated in nine exams across five subjects, with examiners accessing all available evidence in one model, and only scripts and grade descriptors in the other. Preliminary findings suggest that both approaches lead to broadly comparable grading outcomes. Focus group feedback indicates that examiners consider judging the grade-worthiness of student work to be their primary role in grade award. Whilst they found item-level data helpful for prioritising questions for review, participants reported that access to evidence such as statistically recommended boundaries can cloud their judgement or encourage strategic grading. This study also raises further questions about the purposes and uses of different forms of statistical evidence, as well as how and when they should be integrated with qualitative evidence in grade awarding.

Background: Millions of children are diagnosed with a traumatic brain injury (TBI) each year, most being mild TBI (mTBI). The effect of mTBIs on academic performance is of significant importance. We investigate mTBI's impact on parent-reported academic outcomes in school-aged pediatric participants. Methods: This cross-sectional survey study queried parents (N = 285) regarding letter grade performance and the presence or absence of academic accommodations before and after an mTBI, including complicated mTBI (c-mTBI, or mTBI with radiographic abnormality). Results: We found a parent-reported decline in letter grades following c-mTBI (p < 0.001), with no significant change following uncomplicated mTBIs. Degree and length of recovery were also associated with grade changes (p < 0.05). Those with no academic accommodations prior to the injury showed significant decreases in grades after injury regardless of post-injury accommodation status (p < 0.05). Implications of School Health Policy, Practice, and Equity: This study underscores the need for an improved framework of support to maximize academic performance of children following mTBI, especially in those with a c-mTBI and still recovering from their injury. Conclusion: Our study identifies children who are at risk for adverse academic outcomes following mTBI. We encourage efforts to better support school nurses in this effort, including improved communication between health care teams and school teams.

After encountering students who lost motivation throughout the semester, and finding most students' questions focused on how to improve their grade rather than on understanding the material, we developed a gamified grading structure hoping to remove students' barriers to motivation in the classroom. A brief review of the literature on known barriers to student motivation is provided, and on the use of gamification to remove these barriers. Data collected show that the gamified grading structure may have been effective at removing barriers to student motivation and empowering students to work for understanding. Possible interventions for future iterations are explored.

Freshman grades relate to academic outcomes, yet limited research explores which students face the highest risk of course failure. With logit analysis using a five-year Arkansas dataset (n = 164,688), we find that economically disadvantaged ninth-grade students are more likely to fail a course than their more privileged peers. This disparity persists over time despite claims of a shift toward SBG. The study highlights the pressing need for grading equity.

Revision is a complex cognitive task and an integral component of the writing process. Students benefit from understanding the value of and how to revise their own writing. The purpose of this study was to turn to students to investigate their understandings of revision. We posed the following question: What are 7th graders' perceptions and understandings of revision following a unit of study on revision? Using Qualtrics, we surveyed 345 seventh grade students (52% male, 48% female) about their understanding of revision after revising a piece of writing they had previously composed and considered "finished." Within the unit, students were taught to replace, add, delete, and reorder their writing. Findings indicate that prior to the unit students confused editing tasks for revision moves. Following the unit, students' understanding of the purpose of and how to revise grew and they felt their knowledge and use of revision strategies improved their writing. In addition, students found that distance from their piece helped them to see their writing in more critical ways. Student voices and experiences are needed to shed light on what students really know and understand about revision.

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.

In January of 2023, Georgia State University proposed a policy stating that, if graduate teaching assistants (GTAs) taught courses that reported grades of Ds, Fs, or withdrawals (DFW) at a rate of twenty percent or higher, GTAs would be required to complete a course on how to teach college students before they could resume their job as a teaching assistant. If GTAs teach a course with a DFW rate of twenty percent or higher, they not only risk losing their jobs, but they also risk losing their tuition coverage until they complete a remedial course. By examining the implementation of this policy, as well as the broader political and cultural context in which it occurs, the authors argue that policies such as the DFW policy at Georgia State University limit academic freedom, bridle epistemic curiosity, and lead to a performance of knowledge.

The present study examined how information about different grading systems affects students' course expectations, particularly in ways that may have downstream consequences for learning and other academic outcomes. In an online experiment using a preregistered design, we prompted two samples of current and recent college students (N = 547) with a hypothetical course that adopted either a norm- or criterion-referenced grading system, two common grading policies in higher education. We then examined students' expectations for their own course-related goals, perceptions, and behaviors. We found that, compared to criterion-referenced grading, norm-referenced grading led participants to expect higher performance-goal orientation, lower mastery-goal orientation, lower course self-efficacy, and fewer help-related behaviors. Norm-referenced grading also increased perceptions that the instructor believes intelligence to be nonuniversal and fixed (i.e., not malleable). Some effects of grading system were stronger for students from non-minoritized backgrounds and lacking prior experience with the assigned grading system. Although participants reported experiencing criterion-referenced grading more often in college, both grading policies were commonly experienced by participants. Our findings suggest that norm-referenced grading policies negatively impact student expectations. More broadly, these findings highlight the importance of grading policies in shaping students' course-related expectations.

This multicase study explores how instructors at one higher education institution used an emerging technology, Gradescope, in their large classes to support the assessment of student learning. The study examined the practices of two instructors who taught large lecture classes with more than 100 students and used Gradescope to assist with the assessment of student learning. Results indicate that using Gradescope helped the instructors overcome specific challenges associated with large lecture courses, such as grading a large volume of assignments, providing timely feedback, and maintaining grading consistency across large numbers of students and graders. Course drop/fail/withdraw rates decreased and average course grades increased during the semester in which the instructors used Gradescope. Instructors reported increased grading efficiency and improved student satisfaction. However, the use of Gradescope was not entirely responsible for the positive impacts. Instructors also described adapting their teaching methods and assessment strategies to integrate Gradescope effectively.

Online education programs are on the rise and institutions of higher learning are utilizing Learning Management Systems (LMS) to facilitate online learning. The Community of Inquiry (CoI; Garrison et al., 1999) framework identifies three categories of cognitive presence, social presence, and teaching presence to guide the development of online instruction and has been adopted by numerous institutions of higher learning. This article identifies instructional strategies conducive to the design of online presence using the CoI framework to ensure quality learning experiences in online special educator preparation programs. We address some of the features of LMS system tools present in Canvas that are utilized by instructors when designing and implementing online instruction to include: communications, modules and assignments, collaboration, and grading.

This study conducts a comprehensive quantitative evaluation of OpenAI's language model, ChatGPT 4, for grading Task 2 writing of the IELTS exam. The objective is to assess the alignment between ChatGPT's grading and that of official human raters. The analysis encompassed a multifaceted approach, including a comparison of means and reliability measures such as Cohen's weighted kappa and intraclass correlation. The results revealed a high agreement in means and substantial reliability between the two grading methods on the level of the majority of texts. However, individual discrepancies and outliers were also identified, underscoring the nuanced nature of grading. While ChatGPT demonstrated efficiency and general alignment with human grading, the study concludes that it should not replace human judgment, particularly due to these observed inconsistencies. The findings contribute valuable insights into the potential and limitations of AI in educational grading and emphasize the importance of a comprehensive quantitative evaluation.

In this reflective essay, we explore how we and our students experienced trust and opened ourselves up to being vulnerable in two iterations of a course that was built on the pedagogies of ungrading and co-creation in teaching and learning (CCTL). As approaches that fall under the broader umbrella of critical pedagogy, ungrading usually involves an alternative to conventional alphanumeric grading systems, while co-creation in teaching and learning consists of a varied scale of student-and-instructor partnerships in the classroom. The course contexts explored here take ungrading to mean student self-assessment and self-assignment of grades, and our implementation of co-creation focused on significant elements of the course such as expectations, content, assignments, and assessments. We suggest that our combination of these pedagogies exposed the significance of vulnerability to nurturing trust in the college classroom. After an overview of the salient points in the literature on these pedagogies and a discussion of trust and vulnerability, we recollect our own experiences of them in a co-taught second-year honors course. Drawing from our reflections and those expressed in student writing, we observe that we brought a significant level of trust in each other and our students to the course. Further, we note that the processes of sharing authority embedded in both pedagogies significantly deepened that trust while also underscoring, in retrospect, the remarkable degree of vulnerability made accessible to students and instructors alike. Instructors who wish to implement these approaches should be keenly aware of the additional opportunities that trust and vulnerability, as made possible via these pedagogies, offer for building relationships in the classroom and working toward increased inclusivity and equity in the course community.

Despite the increasing ubiquity of computer-based tests, many general qualifications examinations remain in a paper-based mode. Insufficient and unequal digital provision across schools is often identified as a major barrier to a full adoption of computer-based exams for general qualifications. One way to overcome this barrier is a gradual adoption, involving a dual running of paper-based and computer-based exams. When an exam is offered in both modes, and results from both are treated as equivalent, the comparability between modes needs to be ascertained. This includes examining whether the mode in which students respond to extended writing questions such as essays, either by handwriting or by typing on the computer, introduces systematic differences. This article presents findings from a review of existing literature on writing mode effects. Specifically, it discusses findings on four comparability aspects: scores, marking, text characteristics and composing processes. It also offers recommendations for practice.

Specifications grading is a form of alternative grading that emphasizes transparency, low stakes, student learning, and equity. It has attracted practitioners for its potential to enhance student motivation, and to remedy several challenges of traditional grading. Specifications grading has become increasingly popular, yet little is known about students' perceptions of and experiences with it. We examined students' predicted and actual experiences of specifications grading in courses across several disciplines at a research-intensive, public university in the United States. Particular attention was paid to how students perceive specifications grading to influence their motivations to learn. Most students expressed positive attitudes toward specifications grading both before and after experiencing it. Qualitative responses indicate that facets of motivation, including choice, value, and expectations of success, were important factors shaping students' perceptions. Based on these findings, we conclude with recommendations for practice for instructors who are interested in implementing specifications grading.

Given the prominence of international instructors in higher education, understanding their grading practices is essential for informing college grading debates. This first large-scale assessment of undergraduate grading practices highlights how different demographic, classroom and departmental factors shape international instructors' grading behaviors. Using a unique dataset of over 2,000 randomly selected instructors from three public universities, we examine (a) whether undergraduate-level grading practices differ between domestic and international instructors, (b) what factors contribute to the differences, and (c) whether the differences vary across key subgroups. We find that international instructors grade lower than domestic instructors--about 35% of a standard deviation lower on average. Part of this gap is explained by the concentration of international instructors in particular departments. International instructor grading practices differ across regions of origin, prior U.S. higher education experience, gender, and race. Our results provide insights into U.S. college grading debates and supporting the international instructor workforce.

The development, use, and timely promotion of Open Education (OE) has been effective in addressing myriad educational concerns, including inclusivity, accessibility and learning achievement, among many others. However, limited information exists in the literature concerning how OE could enhance Generative Artificial Intelligence (GenAI), which is receiving extensive interest and criticism at this time. To address this research gap, this study relies on the Open Educational Practices (OEP) framework of Huang et al. (2020) to provide various OEP scenarios that could help to promote and facilitate the effective and safe adoption of GenAI in education. The findings of this study could provide guidelines on how relying on OEP when adopting GenAI could help in ensuring quality education which is the sustainable development goal (SDG 4) of the United Nations (UN).

No records of systematic reviews focused on deep learning in open learning have been found, although there has been some focus on other areas of machine learning. Through a systematic review, this study aimed to determine the trends, applied computational techniques, and areas of educational use of deep learning in open learning. The PRISMA protocol was used, and the Web of Science Core Collection (2019-2023) was searched. VOSviewer was used for networking and clustering, and in-depth analysis was employed to answer the research questions. Among the main results, it is worth noting that the scientific literature has focused on the following areas: (a) predicting student dropout, (b) automatic grading of short answers, and (c) recommending MOOC courses. It was concluded that pedagogical challenges have included the effective personalization of content for different learning styles and the need to address possible inherent biases in the datasets (e.g., socio-demographics, traces, competencies, learning objectives) used for training. Regarding deep learning, we observed an increase in the use of pre-trained models, the development of more efficient architectures, and the growing use of interpretability techniques. Technological challenges related to the use of large datasets, intensive computation, interpretability, knowledge transfer, ethics and bias, security, and cost of implementation were also evident.

The use of artificial intelligence (AI) is not a new concept. Still, the press, the worry, and the hype around the potential benefits and limitations of the explosion of these tools in this field is a current topic in teacher education. In this article, the authors summarize the past use of AI, present easily adaptable tools in teacher education, and discuss what is on the horizon in industry and special education teacher education. The authors highlight tools that should be considered in programs today, followed by ways to expand the field of AI in teacher education to support the learning outcomes of struggling students.

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.

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.

In postsecondary education institutions, where innovative technologies continually reshape research and pedagogical approaches, the integration of generative artificial intelligence (GenAI) tools presents promising avenues for enhancing student learning experiences. This study assesses the efficacy of integrating GenAI tools, specifically chatbots, to ameliorate learning outcomes and mitigate test anxiety among postsecondary students. Within the context of an introductory psychology course, 40 participants engaged with chatbots as supplementary "study confidantes" for exam preparation and as facilitators for an essay grading task. The results from surveys administered to the participants indicated that most students found the chatbots helpful in aiding comprehension of course materials. Moreover, a substantial proportion of respondents reported an enhanced understanding of how to structure an academic paper because of their essay grading activity. Despite the favourable perception of chatbot assistance with learning course material and fostering insights into structuring scholarly essays, no discernible alterations in the levels of test anxiety among students were observed. Overall, this research underscores the latent potential of chatbots as pedagogical adjuncts, furnishing instructive insights for educators aiming to innovate instructional methodologies and optimize student learning paradigms within the domain of postsecondary education.

In this systematic review, we synthesize ten empirical peer-reviewed articles published between 2019 and 2023 that used generative artificial intelligence (GenAI) for automated feedback in higher education. There are significant opportunities and challenges to integrate these tools effectively into learning environments as the demand for timely and personalized feedback grows. We examine the articles based on instructional contexts and system characteristics, identifying critical implementation possibilities for GenAI in automated feedback. Our findings reveal that GenAI provides diverse feedback across various contexts with multiple instructional purposes. GenAI systems can reduce instructor workload by automating routine grading and feedback tasks, allowing educators to focus on more complex teaching responsibilities with augmented capabilities. Additionally, these systems enhance communication, offer cognitive and emotional support, and improve accessibility by creating supportive, stress-free learning environments. Overall, implementing GenAI automated feedback systems improves educational outcomes and creates a more efficient and supportive learning environment for students and instructors. We conclude with future research directions to better integrate GenAI with human instruction by reconsidering instructors' roles, especially in providing feedback to create more effective educational experiences.

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.

We consider the reference-based approach for Automatic Short Answer Grading (ASAG) that involves scoring a textual constructed student answer comparing to a teacher-provided reference answer. The reference answer does not cover the variety of student answers as it contains only specific examples of correct answers. Considering other language variants of the reference answer can handle variability in student responses and improve scoring accuracy. Alternative reference answers may be possible, but manually creating them is expensive and time-consuming. In this paper, we consider two issues: First, we need to automatically generate various reference answers that can handle the diversity of student answers. Second, we should provide an accurate grading model that improves sentence similarity computation using multiple reference answers. Therefore, our proposed approach to solve both problems highlights two components. First, we provide a sequence-to-sequence deep learning model that targets generating plausible paraphrased reference answers conditioned on the provided reference answer. Secondly, we propose a supervised grading model based on sentence embedding features. The grading model enriches features to improve accuracy considering multiple reference answers. Experiments are conducted both in Arabic and English. They show that the paraphrase generator produces accurate paraphrases. Using multiple reference answers, the proposed grading model achieves a Root Mean Square Error of 0,6955, a Pearson correlation of 88,92% for the Arabic dataset, an RMSE of 0,7790, and a Pearson correlation of 73,50% for the English dataset. While fine-tuning pre-trained transformers on the English dataset provided state-of-the-art performance (RMSE: 0.7620), our approach yields comparable results. Simple to construct, load, and embed into the Learning Management System question engine with low computational complexity, the proposed approach can be easily integrated into the Learning Management System to support the assessment of short answers.

Short-Answer Grading (SAG) is a time-consuming task for teachers that automated SAG models have long promised to make easier. However, there are three challenges for their broad-scale adoption: A technical challenge regarding the need for high-quality models, which is exacerbated for languages with fewer resources than English; a usability challenge in adapting high-quality research prototypes to the needs of non-expert users, and a trust challenge in communicating the abilities and limitations of the tools. We propose to meet the technical challenge for German with a robust Transformer-based SAG model. We address the usability challenge with an easy-to-use graphical user interface for the SAG model, and the trust challenge with a workflow that allows teachers to evaluate the model on their own data, to choose on the basis of this evaluation which model predictions to trust, and in consequence to stay in control of grading their students while saving grading effort.

In the coming years, the proliferation of artificial intelligence (AI) will lead to changes and challenges to many traditional practices in school music and beyond, particularly related to student assessment and grading. At the same time, the AI revolution may also facilitate new and exciting directions for assessment and differentiation in music education. In this article, I offer a range of considerations and suggestions for music educators seeking to teach music effectively and ethically in the age of AI.

In today's schools, students can be given high grades in examinations and class performance. High grades can lead to the problem of inflated grades. Inflated grades can lead to grade inflation. The subject of this study is to investigate the existence of high grades in primary school, secondary school and high school. The main purpose of the research is to reveal the existence, causes and consequences of high grades. The study group consists of teachers (n = 203) working in primary, secondary and high school educational institutions in Van. Case study design, one of the qualitative research methods, was used. According to the research results, the majority of teachers point out that inflated grades are given to cover up failures. According to the participants in this research, there is a problem of inflated grades at all levels of education. Likewise, inflated grades are common in both public and private schools.

This study aimed to examine the predictive validity of junior high students' grit (including perseverance of effort (PE) and consistency of interest (CI)) on their short-term and long-term academic achievements using a longitudinal survey design under the structural equation modeling framework. Data were collected on 236 junior high students (56.3% boys) in Taiwan across three years. The analytical results showed that PE successively influences students' semester grades (as short-term achievements) in a unidirectional way. However, CI could not predict the consecutive semester grades. Furthermore, we found that both PE and CI did not directly predict students' achievement on the scores of the Comprehensive Assessment Program (CAP, as long-term achievements). Only PE can indirectly associate with the CAP scores via students' successive semester grades. The study results can be explained by the goal hierarchy of grit, where achieving good semester grades can be treated as the lower-order goals that are cohesively aligned with the superordinate goal to do well in the CAP. Strategies for fostering students' perseverance of effort and implications for future research on educational equity are discussed.

Assessment has become a routine feature of school life, internationally. Little is known, however, about the consequences for young people of assessment and grading in physical education (PE) -- a subject often associated with physical recreation. This paper explores young Norwegian's perceptions of assessment and grading in PE from a sociological perspective. In doing so, it contemplates the penetration of neo-liberal discourses as part of wider processes of globalization and Europeanization in school PE in Norway. The study utilizes data generated by 31 focus groups involving 148 youngsters from the 10th grade (15-16-year-olds) in eight purposively sampled secondary schools in Norway. Norwegian PE teachers continue to use tests in order to set grades in PE. The upshot is that students' enjoyment of and engagement in PE, as well as their self-identities and self-esteem, can be compromised by apprehension towards assessment and grading. These processes seem likely to undermine or even erode the potential sociopsychological benefits of PE for some young people by reinforcing the impression that the subject is fast becoming just one more outcome-oriented subject on an academic treadmill. In this regard, the ascendancy of neo-liberalism -- associated with the twin challenges of globalization and European integration -- appears to merely reinforce the hegemony of competitive individualism within PE in Norway, as elsewhere.

The 2020 COVID-19 crisis forced universities to make a dramatic shift to emergency remote education (ERE). While the urgency of the pandemic is gone, schools continue implementing ERE, but more often on a short-notice, short-term basis in response to various situations. This research examines students' suggestions for improving university transitions to ERE and applies them to post-pandemic use of ERE. Qualitative thematic analysis was used to analyze responses to an open-ended survey question answered by 2,751 students at a large Southeastern U.S. university. Responses identified main concerns as Grades and Grading (38%), Better University and Instructor Preparation (23%), Improved Learning Experience (18%), Meeting Student Needs (12%), and No Suggestions (9%). The study found that reducing the uncertainty students felt about ERE's impact on grades, course content, class schedules, assignments, and communication with instructors would improve their online learning experience. Recommendations suggest preemptive steps universities can take to improve post-pandemic use of emergency remote education.

Specifications Grading was implemented in a multisection upper-level analytical chemistry laboratory, the first reported for a full lab course redesign in the discipline or in an upper-level chemistry lab class. The primary goals of the redesign were (1) to assess student proficiency of three separate goals: techniques, data quality, and written communication, with three separate assignments, (2) to encourage growth across the semester, providing students the opportunity to show their improvement, and (3) to implement a clear grading system that emphasized equity across the multisection course. Mark distributions on assignments show an increase in students earning exemplary marks on first attempts as the semester progressed, indicating improvement not only within assignments but also across assignments. Student feedback surveys indicate high satisfaction with the grading scheme, with over72% of survey respondents indicating they would like the grading scheme applied to other courses. Additionally, survey results demonstrate success in achieving the three goals, with students able to articulate the benefits associated with the priorities of the course structure. A particular focus was on the actionability and timeliness of instructor feedback, a hallmark of specifications grading, which students also highly rated. Teaching Assistant feedback indicated that, despite more time spent on grading, teaching satisfaction was higher compared to other courses. The use of regular student feedback to improve the course design is also discussed.