Addressing issues of access and fairness in education through Dynamic Assessment

Addressing Issues of Access and Fairness in Education through Dynamic Assessment

Introduction: Addressing issues of access and fairness in education through Dynamic Assessment

In search of a more in-depth grasp of the process and practice of dynamic assessment (DA) in second language acquisition (SLA), the present study adopted a qualitative meta-synthesis methodology and identified a number of synoptic accounts and themes pertinent to the practical implementation of DA and the philosophical worldview adopted towards it. The overarching inferences made based on the systematic review of 40 peer-reviewed, primary studies, which met certain predetermined criteria for selection and inclusion in the data set, emanated a shared set of two primary and two secondary themes. Expounding upon the dual function of DA in terms of both diagnosing and developing learners’ abilities and elucidating how DA, formative assessment, and scaffolding are different, the two primary themes reflect on commonalities dissected across the 40 selected primary studies on DA, respectively. The two secondary themes give fresh insights into the nature of DA by hailing DA as an assessment tool that can ameliorate fairness in education and explaining how DA is in line with experientialism and pragmatic worldviews. Therefore, the established primary themes can shed light on further dimensions of DA implementation in language pedagogy as well as its practical application guidelines, and the secondary themes can reflect on the way fairness, validity, reliability, and generalizability in DA can be revisited.

Artificial intelligence in education has changed the learning process by enabling personalised instruction, adaptive assessments, and intelligent tutoring systems. Yet AI in education also raises enormous ethical concerns, particularly around issues of algorithmic bias and fairness. This chapter examines the ethical implications of AI in education, focusing on the understanding and mitigation of algorithmic bias. The following section gives an overview of the types and sources of bias in AI systems, strategies for the detection and mitigation of bias, important aspects concerning algorithmic fairness in educational contexts. It outlines approaches that need to be taken in the near future in order to further enhance fairness in AI-powered education. It also examines possibilities of useful biases in AI and makes a case for a shared framework that may help achieve responsible AI implementation in higher education.

Dynamic Assessment (DA) originated in the writings of L.S. Vygotsky more than 80 years ago, and despite its popularity among a small community of specialists it is not widely pursued by educational researchers. This paper seeks to strengthen dialogue between DA proponents and the broader assessment community by identifying potential contributions DA may offer to considering such pressing questions as how assessment may support teaching and learning and how fairness in education may be pursued. It is argued that the defining feature of DA that cuts across the varied approaches that have been developed by psychologists and educators working in diverse contexts around the world is a commitment to looking beyond learner independent performance and examining contribution to joint activity as central to diagnosing abilities. This position prompts a view of teaching and assessment as integrated activity and approaches fairness through the provision of culturally available forms of support. Attention then turns to trends in DA research that are elaborated in the articles in this special issue.

Data-driven predictive models are increasingly used in education to support students, instructors, and administrators, which has raised concerns about the fairness of their predictions and uses of these algorithmic systems. In this introduction to algorithmic fairness in education, we draw parallels to prior literature on educational access, bias, and discrimination, and we examine core components of algorithmic systems (measurement, model learning, and action) to identify sources of bias and discrimination in the process of developing and deploying these systems. Statistical, similarity-based, and causal notions of fairness are reviewed and contrasted in how they apply in educational contexts. Recommendations for policymakers and developers of educational technology offer guidance for promoting algorithmic fairness in education.

The purpose of this study is to look at how the keyword “fairness,” which is constantly attracting attention in society as a whole and academia, relates to the field of Korean language education. To this end, meaningful major issues were derived based on the analysis of previous studies dealing with ‘fairness’ in the field of Korean language education and its adjacent fields. In addition, the content and direction were suggested to be addressed in subsequent discussions. As a result, issues related to fairness in Korean language education could be roughly divided into ‘textbooks and fairness’, ‘evaluation and fairness’, and ‘language data (AI) and fairness’. Through this, it was revealed that: (1) Korean language education should be developed, used, and evaluated without bias (2) it is important to secure fairness in language evaluation implementation and evaluation tools (3) the issue of fairness should be considered when language data (AI) is used in Korean education in various ways. This paper is significant in that it is the first discussion to examine the issue of fairness in Korean language education overall. In the future, systematically establishing the concept of fairness in Korean language education is necessary, subdivide the criteria for judging fairness, and discuss in-depth the relationship between practical implementation and fairness, considering cultural diversity.

With the establishment of the socialist market economic system in China, the issue of social fairness has been paid more and more attentions. The fairness issue of higher education, which is responsible for training high-quality talents for the national economy construction, has become the focus of attention. We discuss and research on the fairness of the higher education, and strive to achieve the goal of education equity in China.

Since the mid-1980s, a breakthrough has taken place in the completely charge-free training of college students and a series of changes has been initiated: The amount of tuition and miscellaneous fees has increased, a number of self-paying students have been enrolled, minban (run by the people; nongovernment) institutions of higher education, which maintain their operations with income from tuition and miscellaneous charges, have been founded, and in gongban (publicly run; in other words, government-run) institutions, a system for collecting part of the cost of education is gradually being implemented. … This reform has affected innumerable households and sparked off a major debate—"Have China's educational policies lost their fairness?" How people understand the issue of fairness in education after colleges and universities begin to charge fees not only affects the effective use of all available resources to develop more higher education and do so better, faster, and more economically; it also affects state power and the Party's relations with the masses of people.

“Education For All” – A Dimension of Education in the 3rd Millennium

Summary The results of this analysis show that, while the mechanism forgaining opportunities to access higher education is complex, thebasic thread that runs through it can be clearly seen: Though dif-ferences in nature among the various types of higher educationhave determined that opportunities for them are governed by dif-ferent models, the scarcity of such social opportunities and thediffering effects of social-class background and social status wereclearly present throughout the 1978–2003 period, while, at the sametime, the different types of senior high level education correspondedclearly to the differences among types of higher education.The impact of the expansion of higher education on fairness ineducation is extremely complex. If we view opportunities for allthe different kinds of higher education as a unified whole, we dis-cover that after 1998 inequality in the area of higher educationexhibits a tendency to decrease. In-depth analysis shows, how-ever, that this process of becoming fairer is subject to conditionsthat entail social class differences remaining clearly defined withinhigher education. The differences inherent in higher educationdetermine the competitive posture and strategy each social classwill adopt toward each type of education: In the case of baccalau-reate education, which has a clear status orientation, expansion inhigher education has led to the privileged classes somewhat dis-proportionately increasing their relative advantage with respect toit, while expansion of opportunities for adult higher education,which has a survival orientation, has caused groups from the lowersocial strata to derive greater benefit.At present higher education in China is also providing a sys-tem-based guarantee of upward mobility for qualified portions ofthe lower social strata, which, as a class-based behavioral strat-egy, enables physical laborers who have received a good educa-tion to utilize advantages such as their cultural capital and, in theprocess of maximizing their opportunities, to accomplish movesupward beyond the lower stratum in one generation. This does notconflict with the possibility that members of the privileged stratamay gain even better access to opportunities; quite to the contrary,

Artificial intelligence (AI) contributes significantly to improving access and fairness in transnational higher education (TNE) by employing technology to solve a variety of obstacles and possibilities. The chapter discusses about the use of AI based technologies and role of financial stress on the TNE through an extensive Case Study on the UK, largest degrees awarding country based on TNE. Using the Cross-Quantilogram (CQ) and Wavelet Local Multiple Correlation (WLMC) approaches on a dataset ranging 2013-2023 the chapter divulges that in the long-term AI use enhance TNE in the UK. While increased global financial stress can hamper the TNE in the long-term. Although, the associations varies across both quantiles and frequencies over time for UK. However, important way forward for stakeholders are suggested based on the findings of the case study.

In Zimbabwe, the discourse on access and quality in education has been a raging one since the colonial days of bottlenecks and outright discrimination against black Zimbabweans in education. The doors to education were declared open to all at independence in 1980 with the new Zimbabwe government’s enunciated policy of education for all. It is an uncontested fact that strides were made soon after independence to address issues of quality and access in education. However, with the prosecution of the fast track land reform programme the dream for access and quality in education became a nightmare. Whilst trust schools, boarding schools, urban and some rural day schools have a comparative advantage in terms of resources like infrastructure and qualified and relatively motivated human resource, emerging resettlement schools bear the brunt of hastened and impromptu establishment. It is the contention of this paper that resettlement schools like Zvivingwi, established in the last decade, are a facade of the schools envisioned by many Zimbabweans at independence. These schools reel from abject shortage of everything except pupils. It would be recommended that government should show creativity in mobilising resources to intervene, failing which, most of the resettlement schools like Zvivingwi, risk closure as public confidence in them wanes. The researcher made use of a questionnaire and interviewed critical stakeholders at the school like headmaster, teachers, parents, pupils and education officers. School records and other critical documents were also made use of.

With the rapid development of information technology, virtual reality (VR) technology has gradually transformed from the concept of science fiction to reality, and become an important driving force for innovation and development in the field of education. As an immersive technology, VR can break the limitations of time and space in traditional teaching modes and create an immersive learning experience for learners. This technology can not only make the learning process more vivid and interesting, but also improve students’ learning efficiency and initiative through interaction and immersion. Especially in the field of distance education, the application of VR technology is redefining teaching methods, increasing the efficiency of educational resource sharing, and enhancing the fairness and accessibility of education. This paper will take VR technology as the starting point, analyze its basic principles and technical characteristics, and deeply discuss the diversified practices of VR in distance education, including teaching scene simulation, interactive experience optimization, other aspects of educational resources equity, etc.

From classifying learners to predicting learner behaviour, the application of Big Data in online education has been vast. Besides the potential benefits of Big Data in education, it is necessary to critically engage with some ethical and social challenges that Big Data presents to the field of online learning. The increasing use of big data by large institutional actors and corporations raises questions not only about data privacy and ownership, but whether this data is used to genuinely improve learner and teacher online learning experiences, or primarily for commercial profits and institutional benefits. When addressing ethical concerns regarding the use of Big Data in education, critiques often follow a reasoning that is in line with corporate interests and neoliberal logic of marketization of education. Given the importance of the pursuit for democratic online education, the need for critical perspectives in the field is ever-more essential. This research tries to critically address the role and impact of Big Data on labour relations and economic fairness in online education by examining both corporate and institutional data practices in online learning. The study puts forward a provisional theory of the use of Big Data in two large online learning platforms (Coursera and Blackboard) using critical grounded theory. The core category of Exploitation of the learning community, the three constituent concepts; the Vendor-Institutional Complex, Use of learner generated value for profit, and the Behavioral monitoring and engineering; and the sustaining category, the Magic Trick, were the foundational blocks for developing an emancipatory theory that addressed ethical issues of economic fairness regarding the use of big data in online education.