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A Content Based Course With the Assistance of Artificial Intelligence
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In 2020, the COVID-19 pandemic strongly affected all sectors, including education. Across the globe, governments enacted policies restricting people's movement, affecting the educational assessment industry. During the lockdown, South Africa's National Benchmark Tests (NBTs) were cancelled. In contrast to paper-pencil and linear tests deployed for NBTs, computer-adaptive testing (CAT) is a modern alternative. CAT technology is widely used for licensing and certification exams in most developed nations. CAT is expected to be used by many educational assessment companies in sub-Saharan Africa for their high-stakes exams due to its reduced testing time and algorithms that produce stable and reliable results. This paper aims to provide an overview of the 4IR tools that will enable creation of a comprehensive CAT, such as feasibility studies, item bank development, test-and-calibrate item banks, and specifications for the final CAT. To successfully implement CAT, these steps are essential. The importance of thorough research and documentation in each stage of the CAT cannot be overemphasized. Based on Fourth Industrial Revolution (4IR) artificial intelligence tools, we conclude that CAT is an excellent modality to adopt for ensuring accurate evaluations of examinees' abilities within high-stakes exams.
This article aims to evaluate a flipped class teaching model based on an AI (artificial intelligence) Language Learning Platform. The platform adopts mainstream collaborative filtering and BPMF (Bayesian Probabilistic Matrix Factorization) and big data management methods to provide students’ detailed performance analysis reports and push recommendation exercises. The researcher reconciled the flipped teaching model with the AI-based language learning platforming in an experimental class and conventionally in a control class. At the end of the semester, the researcher compared students’ performance as well as their attitudes toward the course between the two classes through a postexam paper and a questionnaire. Combining quantitative and qualitative methods, the survey shows that although the AI-based language learning mode failed to significantly increase students’ English listening scores, compared with the students in the traditional class, those receiving the new mode held a more positive attitude toward their English listening experience, especially in regard to learning interest, study autonomy, and class involvement. The flipped class mode not only relieved students’ partial cognitive processing workloads in class, but also boosted their confidence to engage in classroom discussion by reducing their nervousness through before-class preview. The AI-based language learning platform mainly performs monitoring and tutoring functions which facilitate the flipped class instruction. This study proposes a new flipped teaching model and proves it effective, indicating that for the flipped class learning mode to be successful, it is of importance to integrate face-to-face classroom instruction with the AI-technology-assisted online learning experience in a way that they can coherently support each other.
With the progression of economic globalisation, China’s expectations about the quality of English teaching in the country have increased, and because of the restrictive drawbacks of orthodox English teaching models, they cannot meet these requirements. The emergence of artificial intelligence (AI) technology can create new opportunities for the optimisation of English teaching. As such, this study aims to systematically review and analyse the current research on the innovations afforded by AI for China’s English teaching model, especially the application of AI in unquiesced English teaching. The descriptive–analytical approach is used to study and analyse the literature. The research results highlight evidence of success cases, illustrating that the implementation of AI in English teaching in China can provide new ideas and create new spaces for the innovation of teaching models. Keywords: artificial intelligence; English teaching in China; innovation in English teaching
How to improve student engagement and educational learning outcomes in the classroom have always been high on a teacher’s priority list. Unfortunately, it seems these challenges have become more pronounced in teaching Generation Z. To address these challenges educators are exploring new teaching methodologies such as flipped learning. The authors examined the effect of flipped learning on student motivation and educational outcomes on sophomore students from a private university in Indonesia majoring in Information Systems. The survey was taken from two groups, a control group which was not taught using flipped learning (60 respondents), and an experimental group which was taught using flipped learning (60 respondents). Results showed that flipped learning increased student motivation and improved educational outcomes compared to a group that was not exposed to flipped learning. The results can help guide teachers and administrators on potential implementation of flipped learning at their universities.
This article presents a pedagogical design for teacher education that combines flipped materials, in-class instruction, and telecollaboration (also known as virtual exchange) for foreign language teacher education. The context of this study is a course on technology and language learning for future teachers in which the flipped classroom concept was applied to technology-infused collaborative teacher training between future ESL/EFL instructors located at two partner universities (one in the USA, one in Europe). The three main teaching approaches (flipped materials, in class, and telecollaborative, or “FIT”) were symbiotic in that each structure reinforced the other through reception, discussion, and reflection as a means to help the student teachers bridge the gap between theory and practice. We apply classroom ethnographic discourse analysis to data sources (face-to-face and online discussion groups, student e-portfolios) to look at uptake of ideas, conceptual understanding, and successful transfer of new knowledge, and thereby identify whether the design provides significant learning opportunities for the future teachers. Although most studies of telecollaboration in language teacher education look principally at output, this approach allows an in-depth look at the learning process as knowledge is developed collaboratively between the participants.
Content-based instruction (CBI) is a pedagogical approach used in the second/other language classroom that commits to addressing both language- and content-learning objectives. Scholars have found that, particularly in the planning stages, teachers struggle with achieving that balance (Donato, 2016; Lyster, 2007). This study examines world language (WL) teachers’ planning for CBI via these research questions: (1) When experienced teachers from traditional WL classroom contexts design CBI unit plans, what do these plans reveal about the pathways they take towards CBI in a traditional WL settings? (2) What broader challenges and opportunities related to using CBI in the traditional WL context are revealed? The data sources in this qualitative lesson analysis study are 36 unit plans developed as the final project for a graduate-level course on CBI in one US institution. The three investigators examined the content–language dichotomy and the academic content-cultural content dichotomy in each lesson and identified patterns. The findings reveal that teachers often chose content that was more cultural than academic in nature, and they were challenged by several aspects of relating the language and content objectives. Implications for researchers, teacher educators, and teachers present a broader discussion of CBI as a viable curricular option in WL classrooms.
This paper on artificial intelligence in education (AIEd) has two aims. The first: to explain to a non-specialist, interested, reader what AIEd is: its goals, how it is built, and how it works. The second: to set out the argument for what AIEd can offer teaching and learning, both now and in the future, with an eye towards improving learning and life outcomes for all. Computer systems that are artificially intelligent interact with the world using capabilities (such as speech recognition) and intelligent behaviours (such as using available information to take the most sensible actions toward a stated goal) that we would think of as essentially human. At the heart of artificial intelligence in education is the scientific goal to make knowledge, which is often left implicit, computationally precise and explicit. In other words, in addition to being the engine behind much ‘smart’ ed tech, AIEd is also designed to be a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us more fine-grained understandings of how learning actually happens. Although some might find the concept of AIEd alienating, the algorithms and models that underpin ed tech powered by AIEd form the basis of an essentially human endeavor. Using AIEd, teachers will be able to offer learners educational experiences that are more personalised, flexible, inclusive and engaging. Crucially, we do not see a future in which AIEd replaces teachers. What we do see is a future in which the extraordinary expertise of teachers is better leveraged and augmented through the thoughtful deployment of well designed AIEd. We have available, right now, AIEd tools that could support student learning at a scale previously unimaginable by providing one-on-one tutoring to every student, in every subject. Existing technologies also have the capacity to provide intelligent support to learners working in a group, and to create authentic virtual learning environments where students have the right support, at the right time, to tackle real-life problems and puzzles. In the near future, we expect that teaching and learning will increasingly be supported by the thoughtful application of AIEd tools. For example, by lifelong learning companions powered by AI that can accompany and support individual learners throughout their studies - in and beyond school - and new forms of assessment that measure learning while it is taking place, shaping the learning experience in real time. If we are ultimately successful, we predict that AIEd will help us address some of the most intractable problems in education, including achievement gaps and teacher retention. AIEd will also help us respond to the most significant social challenge that AI has already brought - the steady replacement of jobs and occupations with clever algorithms and robots. It is our view that this provides a new innovation imperative in education, which can be expressed simply: as humans live and work alongside increasingly smart machines, our education systems will need to achieve at levels that none have managed to date. True progress will require the development of an AIEd infrastructure. This will not, however, be a single monolithic AIEd system. Instead, it will resemble the marketplace that has developed for smartphone apps: hundreds and then thousands of individual AIEd components, developed in collaboration with educators, conformed to uniform international data standards, and shared with researchers and developers worldwide. These standards will also enable system-level data collation and analysis that will help us to learn much more about learning itself – and how to improve it. Moving forward, we will need to pay close attention to three powerful forces as we map the future of artificial intelligence in education, namely pedagogy, technology, and system change. Paying attention to the pedagogy will mean that the design of new edtech should always start with what we know about learning. It also means that the system for funding this work must be simultaneously opened up and refocused, moving away from isolated pockets of R&D and toward collaborative enterprises that prioritise areas known to make a real difference to teaching and learning. Paying attention to the technology will mean creating smarter demand for commercial grade AIEd products that work. It also means the development of a robust, component-based AIEd infrastructure, similar to the smartphone app marketplace, where researchers and developers can access standardised components that have been developed in collaboration with educators. Paying attention to system change will mean involving teachers, students, and parents in co-designing new tools, so that AIEd will appropriately address the inherent “messiness” of real classroom, university, and workplace learning environments. It also means the development of data standards that promote the safe and ethical use of data. Said succinctly, we need intelligent technologies that embody what we know about great teaching and learning, embodied in enticing consumer grade products, which are then used effectively in real-life settings that combine the best of human and machine. We do not underestimate the new-thinking, inevitable wrong-turns, and effort required to realise these recommendations. However, if we are to properly unleash the intelligence of AIEd, we must do things differently - via new collaborations, sensible funding, and (always) a keen eye on the pedagogy. The potential prize is too great to act otherwise.
The role of project-research-based academic forum approach in graduate English teaching, Academic Degrees & Graduate Education
- J G Cai
J. G. Cai, The role of project-research-based academic forum approach in graduate English teaching,
Academic Degrees & Graduate Education, 2, pp. 18-24, 2017.
Exploration of SPOC based flipped oral classroom, Academic Degrees & Graduate Education
- Y Y Mo
- F Chang
Y. Y. Mo and F. Chang, Exploration of SPOC based flipped oral classroom, Academic Degrees &
Graduate Education, 12, pp. 39-43, 2017.
BERT: Pre-training of deep bidirectional transformers for language understanding
- J Devlin
- M W Chang
- K Lee
- K Toutanova
J. Devlin, M. W. Chang, K. Lee and K. Toutanova, BERT: Pre-training of deep bidirectional
transformers for language understanding, 2019 Conference of the North American Chapter of the
Association for Computational Linguistics: Human Language Technologies-Proceedings of the
Conference, 1, pp. 4171-4186, 2019.