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Applications of Rasch measurement in learning environments research
Major advances in creating linear measures in education and the social sciences, particularly in regard to Rasch measurement, have occurred in the past 15 years, along with major advances in computer power. These have been combined so that the Rasch Unidimensional Measurement Model (RUMM) and the WINSTEPS computer programs now do statistical calculations and produce graphical outputs with very fast switching times. These programs help researchers produce unidimensional, linear scales from which valid inferences can be made by calculating person measures and item difficulties on the same linear scale, with supporting evidence. This book includes 13 Learning Environment research papers using Rasch measurement applied at the forefront of education with an international flavour. The contents of the papers relate to: (1) high stakes numeracy testing in Western Australia; (2) early English literacy in New South Wales; (3) the Indonesian Scholastic Aptitude Test; (4) validity in Learning Environment investigations; (5) factors influencing the take-up of Physics in Singapore; (6) state-wide authentic assessment for Years 11-12; (7) talented and gifted student perceptions of the learning environment; (8) disorganisation in the classroom; (9) psychological services in learning environments; (10) English teaching assistant roles in Hong Kong; (11) learning Japanese as a second language; (12) engagement in classroom learning; and (13) early cognitive development in children.
Book
Algorithms of Education
A critique of what lies behind the use of data in
contemporary education policy
While the science fiction tales of artificial intelligence
eclipsing humanity are still very much fantasies, in Algorithms
of Education the authors tell real stories of how algorithms
and machines are transforming education governance, providing a
fascinating discussion and critique of data and its role in
education policy.
Algorithms of Education explores how, for policy
makers, today's ever-growing amount of data creates the illusion of
greater control over the educational futures of students and the
work of school leaders and teachers. In fact, the increased
datafication of education, the authors argue, offers less and less
control, as algorithms and artificial intelligence further abstract
the educational experience and distance policy makers from teaching
and learning. Focusing on the changing conditions for education
policy and governance, Algorithms of Education proposes
that schools and governments are increasingly turning to \"synthetic
governance\"-a governance where what is human and machine becomes
less clear-as a strategy for optimizing education.
Exploring case studies of data infrastructures, facial
recognition, and the growing use of data science in education,
Algorithms of Education draws on a wide variety of
fields-from critical theory and media studies to science and
technology studies and education policy studies-mapping the
political and methodological directions for engaging with
datafication and artificial intelligence in education governance.
According to the authors, we must go beyond the debates that
separate humans and machines in order to develop new strategies
for, and a new politics of, education.
eBook
Using data to improve higher education : research, policy and practice
In recent decades, higher education systems and institutions have been called to respond to an unprecedented number of challenges. Major challenges emerged with the phenomenal increase in the demand for higher education and the associated massive expansion of higher education systems. In response universities were called to adopt planning and research methods that would enable them to identify and address the needs of a larger, more diverse student body. Higher education institutions began to place greater emphasis on planning and marketing, seeking to maintain their position in an increasingly competitive higher education market. Under the current economic downturn, universities are under pressure to further cut costs while maintaining their attractiveness to prospective students. As a result educational policy makers and administrators are called to select the 'right' alternatives, aiming for both efficiency and effectiveness in delivered outcomes. This book provides insights into the use of data as an input in planning and improvement initiatives in higher education. It focuses on uses (and potential abuses) of data in educational planning and policy formulation, examining several practices and perspectives relating to different types of data. The book is intended to address the need for the collection and utilization of data in the attempt to improve higher education both at the systemic and the institutional level.
Book
How Modeling Can Inform Strategies to Improve Population Health
In April 2015, the Institute of Medicine convened a workshop to explore the potential uses of simulation and other types of modeling for the purpose of selecting and refining potential strategies, ranging from interventions to investments, to improve the health of communities and the nation's health. Participants worked to identify how modeling could inform population health decision making based on lessons learned from models that have been, or have not been, used successfully, opportunities and barriers to incorporating models into decision making, and data needs and opportunities to leverage existing data and to collect new data for modeling. This report summarizes the presentations and discussions from this workshop.
eBook
Ways of thinking in STEM-based problem solving
This article proposes an interconnected framework,
Ways of thinking in STEM-based Problem Solving
, which addresses cognitive processes that facilitate learning, problem solving, and interdisciplinary concept development. The framework comprises critical thinking, incorporating critical mathematical modelling and philosophical inquiry, systems thinking, and design-based thinking, which collectively contribute to adaptive and innovative thinking. It is argued that the pinnacle of this framework is learning innovation, involving the generation of powerful disciplinary knowledge and thinking processes that can be applied to subsequent problem challenges. Consideration is first given to STEM-based problem solving with a focus on mathematics. Mathematical and STEM-based problems are viewed here as goal-directed, multifaceted experiences that (1) demand core, facilitative ways of thinking, (2) require the development of productive and adaptive ways to navigate complexity, (3) enable multiple approaches and practices, (4) recruit interdisciplinary solution processes, and (5) facilitate the growth of learning innovation. The nature, role, and contributions of each way of thinking in STEM-based problem solving and learning are then explored, with their interactions highlighted. Examples from classroom-based research are presented, together with teaching implications.
Journal Article
Defining Computational Thinking for Mathematics and Science Classrooms
Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include \"computational thinking\" as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics auricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.
Journal Article
A conceptual model of mathematical reasoning for school mathematics
The development of students' mathematical reasoning (MR) is a goal of several curricula and an essential element of the culture of the mathematics education research community. But what mathematical reasoning consists of is not always clear; it is generally assumed that everyone has a sense of what it is. Wanting to clarify the elements of MR, this research project aimed to qualify it from a theoretical perspective, with an elaboration that would not only indicate its ways of being thought about and espoused but also serve as a tool for reflection and thereby contribute to the further evolution of the cultures of the teaching and research communities in mathematics education. To achieve such an elaboration, a literature search based on anasynthesis (Legendre, 2005) was undertaken. From the analysis of the mathematics education research literature on MR and taking a commognitive perspective (Sfard, 2008), the synthesis that was carried out led to conceptualizing a model of mathematical reasoning. This model, which is herein described, is constituted of two main aspects: a structural aspect and a process aspect, both of which are needed to capture the central characteristics of MR.
Journal Article
Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course
As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI prediction models focus on the development and optimization of the accuracy of AI algorithms rather than applying AI models to provide student with in-time and continuous feedback and improve the students’ learning quality. To fill this gap, this research integrated an AI performance prediction model with learning analytics approaches with a goal to improve student learning effects in a collaborative learning context. Quasi-experimental research was conducted in an online engineering course to examine the differences of students’ collaborative learning effect with and without the support of the integrated approach. Results showed that the integrated approach increased student engagement, improved collaborative learning performances, and strengthen student satisfactions about learning. This research made contributions to proposing an integrated approach of AI models and learning analytics (LA) feedback and providing paradigmatic implications for future development of AI-driven learning analytics.HighlightsIntegrated approach was used to combine AI with learning analytics (LA) feedbackQuasi-experiment research was conducted to investigate student learning effectsIntegrated approach to foster student engagement, performances and satisfactionsParadigmatic implication was proposed for develop AI-driven learning analyticsClosed loop was established for both AI model development and educational application.
Journal Article
The Role of Mathematics in interdisciplinary STEM education
In times of rapid technological innovation and global challenges, the development of science, technology, engineering and mathematics (STEM) competencies becomes important. They improve the personal scientific literacy of citizens, enhance international economic competitiveness and are an essential foundation for responsible citizenship, including the ethical custodianship of our planet. The latest programme for international student assessment results, however, indicate that even in economically mature countries such as those in Europe, and the USA and Australia, approximately 20% of students lack sufficient skills in mathematics or science. This trend serves to highlight the urgent need for action in relation to STEM education. While it is widely acknowledged that mathematics underpins all other STEM disciplines, there is clear evidence it plays an understated role in integrated STEM education. In this article, we address an element of this concern by examining the role of mathematics within STEM education and how it might be advanced through three interdisciplinary approaches: (1) twenty-first century skills; (2) mathematical modelling; and (3) education for responsible citizenship. At the end of the paper we discuss the potential for research in relation to these three aspects and point to what work needs to be done in the future. [Author abstract]
Journal Article