AI Pedagogy: Rooted in Pedagogy, Centered on Humans

In modern education, as technology advances rapidly, AI (Artificial Intelligence) has become an essential tool in teaching and learning activities. However, for successful AI integration, we must always remember the principle: “rooted in pedagogy, centered on humans.” This means that AI is a means to serve educational goals, while the core remains the pedagogical methods and the interaction between educators and learners. This article will analyze the concept of "AI pedagogy" and provide user-friendly examples that resonate with learners.

1. What is AI Pedagogy?

“AI Pedagogy” refers to the application of AI in teaching and learning from a pedagogical perspective. AI acts as a supporting tool to implement learning and training activities but does not replace the central role of humans and pedagogical principles. In other words, AI is “integrated” into the teaching process to optimize and personalize learning while adhering to the fundamental values of education: humanity, interaction, and a focus on holistic learner development.

 

Training on AI Pedagogy for LHU and Hanoi University of Pharmacy

2. Rooted in Pedagogy

2.1 Teaching methods based on Kolb’s Learning Styles

Challenges in the past: Designing lessons based on Kolb’s learning styles (with four stages: Concrete Experience, Reflective Observation, Abstract Conceptualization, and Active Experimentation) often required educators to spend significant time creating scenarios, activities, and various materials tailored to different learner groups (e.g., those who prefer direct experience versus those who prefer analysis).

AI Integration: AI can suggest learning activities tailored to each learner’s style, create real-life simulations, or provide diverse materials (videos, readings, interactive games). This allows lesson planning to become faster and more flexible while preserving the core value of Kolb’s method: connecting learners with experiences and reflection.

Applying AI to Plan Lessons Based on Kolb Learning Style

2.2 Blended Learning

Challenges in the past: Designing a blended learning course (combining in-person and online learning) required educators to carefully balance teaching time, select online platforms, organize group activities, and assess learners. Ensuring that online content was cohesive and engaging was a major challenge.

AI Integration: AI can track learners' progress and recommend schedules, content, or interactive activities tailored to individual abilities and preferences. This enables learners to engage in online learning comfortably, while in-person sessions focus on interaction and discussion. Thus, the essence of blended learning—a harmonious combination of in-person and online learning—is enhanced, not overshadowed by technology.3. Centered on Humans

3.1 Virtual tutors and friendly learning assistants

Virtual tutors: These are learning assistants available anytime, anywhere. For instance, when learners need to review knowledge or ask questions outside classroom hours, virtual tutors can provide answers, suggest materials, or assign practice exercises.

Human-centric value: While AI can respond quickly, educators remain crucial in guiding, sparking critical thinking, and offering emotional and psychological support. Virtual tutors cannot replace the motivation and personal care educators provide.

3.2 Chatbots trained on educators’ curricula

Learning chatbots: Chatbots built on official course materials help learners quickly search for information, address common questions, and review knowledge. Learners can interact with chatbots, asking questions ranging from simple to complex.

Human-machine interaction: Since chatbots are trained from materials developed by educators, their answers align closely with classroom teaching and the pedagogical intentions of educators. Additionally, educators can track frequently asked questions to adjust lessons, enrich examples, or design appropriate learning activities.

Core value: Humans (educators) remain the ones who “ignite inspiration,” crafting lively curricula, encouraging learning enthusiasm, and ensuring the quality and standards of content. Chatbots act as bridges, helping learners grasp and reinforce knowledge more effectively.

4. Benefits and Challenges

The integration of AI into education offers numerous benefits and presents certain challenges. On the positive side, AI optimizes time and resources for educators by streamlining lesson planning and assessment, while also personalizing and enhancing interactions with learners. It creates engaging learning experiences through simulations, games, and visual aids, empowering learners to take charge of their own learning and revision.

However, challenges include ensuring that AI serves as a means to support education, rather than becoming the ultimate goal. Safeguarding learners’ personal data, such as information and learning outcomes, is a critical concern. Additionally, educators must be flexible and equipped with technological skills to effectively use AI. Continuous updates and maintenance of AI tools are essential to prevent inaccuracies or irrelevant chatbot responses, ensuring that the technology remains a valuable and reliable educational tool.

5. Conclusion

“AI Pedagogy” is not just about using AI for technical purposes but, more importantly, about maintaining the core values of education “rooted in pedagogy, centered on humans.” No matter how powerful and versatile AI becomes, the central role lies with educators and learners. AI is a companion, helping us design creative and personalized lessons and learning environments, without overshadowing the essential elements of interaction, empathy, and mutual understanding—the foundations of education.

The application of AI from a pedagogical perspective empowers learners to take charge of their education, becoming more active and engaged in acquiring knowledge. At the same time, it allows educators to devote more time to fostering the holistic development of learners. This approach paves the way for a modern, high-quality, and humane vocational education system.

Lê Phương Trường

 

 

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AI in Quality Assurance

 In the past, when designing learning outcomes for a course or educational program, educators often had to spend significant time researching and memorizing the verbs in Bloom’s taxonomy. Memorizing or manually referencing Bloom’s verb table across various materials was not only time-consuming but also prone to errors. For example, if a teacher wanted to express the competency of "analyzing," they would have to look up suitable verbs in Bloom’s taxonomy and then cross-check their alignment with learning objectives.

Today, with the support of AI models specifically trained in quality assurance, educators no longer need to “commit to memory” every detail of Bloom’s taxonomy. Instead, they can interact directly with AI to quickly identify appropriate verbs and pinpoint areas for improvement. This significantly reduces the effort required to consult resources or verify the coherence of training programs. For instance, if an educator aims to adjust a learning outcome from the level of "understand" to "apply," AI can suggest verbs like "perform," "apply," or "execute," accompanied by a clear explanation of their relevance.

Additionally, AI can provide detailed advice and guidance to help educators better understand accreditation standards such as AUN-QA or the Ministry of Education and Training’s requirements. Rather than navigating through numerous legal documents or accreditation manuals, teachers simply need to ask questions and receive immediate, accurate answers. This makes the process of skill enhancement more flexible and engaging, allowing educators to explore quality assurance knowledge in their own proactive and creative way.

In summary, AI does not replace the role of educators but acts as an intelligent “assistant” that collaborates with them to design and refine educational programs. It helps save time, accelerates workflows, and, most importantly, sustainably enhances the quality of education.

To learn more about the application of AI in quality assurance, visit:

 Link:   ChatGPT - Trợ lý đảm bảo chất lượng

Le Phuong Truong

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The AI Competency Framework for Students

 

The AI Competency Framework for Students, developed by UNESCO, aims to equip learners with the knowledge and skills needed to understand, apply, and innovate with AI in modern education and society. The framework is structured around three levels—Understanding, Applying, and Creating—and focuses on four key domains:

Human-Centered Mindset

Critical Reflection on AI: Encourages students to critically examine the role and impact of AI in society.

Safe and Responsible Use: Guides students on using AI ethically, securely, and responsibly.

Self-Development in the AI Era: Helps students understand how AI can support personal and professional growth.

Ethics of AI

Human Autonomy: Ensures students understand that AI should support, not replace, human decision-making.

Ethical AI Design: Promotes the design of AI systems in line with ethical principles that respect human rights and values.

AI Citizenship: Prepares students to actively and responsibly participate in AI-supported societies.

AI Foundations

Data, Algorithms, and Models: Provides foundational knowledge on how AI operates, including data collection, algorithms, and model building.

Programming and Data Analysis: Develops skills in programming and data analysis to create basic AI applications.

Modeling and Visualization: Teaches students to visually represent AI data and results in clear and understandable formats.

AI Skills

AI Techniques and Applications: Equips students with skills to utilize AI tools and techniques in practical scenarios.

AI Programming: Trains students to develop AI programs tailored to specific needs.

AI Product Development: Encourages creativity and practical application in designing and implementing AI projects or products.

AI for Problem-Solving

Identifying Problems: Guides students in recognizing issues that can be addressed using AI.

Co-Designing Solutions: Promotes collaboration in designing AI solutions.

Co-Creation and Continuous Feedback: Encourages the iterative improvement of AI solutions based on real-world feedback.

This framework aims to holistically develop students, not only in technical competencies but also in ethical considerations and social responsibility when utilizing and developing AI. It provides a foundation for students to navigate the AI-driven future effectively and responsibly.

(unesdoc.unesco.org)

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Redesigning Assessments in the AI Era

 

Introduction

In the technological era, particularly with the explosion of Artificial Intelligence (AI), numerous fields have been profoundly impacted, such as content creation, data analysis, and marketing. Education is no exception. One significant advantage of AI is its ability to reduce administrative tasks and personalize the learning process for each individual. However, a major challenge educators face is assessment. As AI becomes more prevalent in learning, students can rely on AI tools to complete assignments and exams more quickly and effortlessly. This raises an urgent question: How can fairness and integrity in assessments be ensured?

This article will discuss redesigning assessment systems in the AI era and propose solutions to address the challenges posed by students using AI in exams.

The Impact of AI on Assessment

Generative AI provides numerous helpful tools for learners, ranging from solving complex problems, generating text content automatically, to offering detailed answers to academic questions. While these advancements bring significant benefits to learning, they also present serious challenges in assessing the actual competencies of students. Specifically:

• Cheating: Students can use generative AI tools such as ChatGPT, Claude, and Copilot to generate essays or answer complex questions effortlessly without conducting their own research or critical thinking.

• Difficulty in Determining Actual Competence: Without appropriate control measures, assessments may fail to reflect students' actual competencies, as AI can help them complete tasks without in-depth understanding. Over time, this reliance on AI may erode their critical thinking and deep comprehension of the topics under study.

Solutions for Redesigning Assessment in the AI Era

1. Design Assessments Based on Critical Thinking

Instead of focusing on knowledge-based or straightforward questions, assessments should evaluate learners’ critical thinking, problem-solving, and creativity. Open-ended questions requiring reasoning, analysis, and personal opinions are more challenging for AI tools to address effectively.

2. Integrate Direct and Practical Assessments

An effective solution is to combine theoretical tests with practical or in-person assessments. For instance, after completing a written essay or exam, students could be required to present orally or perform related tasks on the spot to verify their understanding and ability to apply knowledge. This ensures that students genuinely grasp the content rather than relying on AI.

3. Leverage Technology to Detect AI-Generated Content

AI-detection software is being developed and can be integrated into assessment systems. These tools help identify whether an essay or answer has been generated by AI, reducing the risk of cheating.

4. Focus on Learning Processes Instead of Final Results

Rather than concentrating solely on final exams, educators can evaluate students through their learning processes. Long-term projects, stage-by-stage reports, or continuous group activities can assess progress and capabilities comprehensively. This makes it harder for students to rely on AI throughout all phases without demonstrating genuine understanding.

5. Encourage Using AI as a Learning Tool, Not for Cheating

Educators should design teaching and assessment activities where AI is a support tool rather than a means of cheating. For example, students may be asked to use AI for information retrieval but then analyze and provide personal insights based on the results. This promotes responsible and creative use of AI while still accurately assessing learners’ abilities.

6. Use Presentation Methods

Presentations are one of the most effective tools to discourage students from over-relying on AI. Presentations require students to understand the subject matter and demonstrate their communication and logical content delivery skills. Through this method, educators can easily evaluate students' actual understanding and their ability to address arising questions during the presentation. Benefits of this method include:

• Enhancing Critical Thinking and Analysis: Students must synthesize information, think critically, and explain concepts clearly.
• Fostering Creativity: Presentations provide an opportunity for students to showcase creativity through interpretation and content illustration.
• Developing Communication Skills: Communication skills are essential not only in learning but also in future careers.

Conclusion

In the AI era, assessments face numerous challenges but also present opportunities to redesign and improve systems to enhance learners’ critical thinking and adaptability. By applying innovative assessment methods, leveraging technology, and promoting critical thinking, educators can build fair assessment systems that accurately reflect students’ real abilities while encouraging responsible use of AI. Teachers and educational administrators must proactively adjust their approaches, not only to address the challenges AI brings but also to turn it into an effective learning support tool.

Le Phuong Truong

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AI Competence Framework for teacher

 The AI Competency Framework for Teachers by UNESCO is designed to equip educators with the necessary skills and knowledge to effectively integrate artificial intelligence (AI) into their teaching practices. This framework emphasizes a human-centered approach, prioritizing inclusivity, human agency, and respect for diversity.

The framework is structured around five key areas:

1. Human-Centered Mindset: Encouraging teachers to understand and assert their agency in relation to AI.

2. Ethics of AI: Promoting responsible use, ethical principles, and safe practices in AI applications.

3. AI Foundations and Applications: Providing foundational AI knowledge and skills necessary for educators.

4. AI Pedagogy: Supporting teachers in leveraging AI for innovative teaching methods and enhancing learning experiences.

5. AI for Professional Development: Outlining how educators can utilize AI to drive their own lifelong professional growth.

These competencies are categorized into three progression levels: Acquire, Deepen, and Create, allowing for a structured development pathway for teachers at different stages of AI integration.

For a comprehensive understanding and to access the full document, you can download the framework from UNESCO's official repository.

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Quality Assessment for Distance Higher Education Programmes in Vietnam: Introduction, Benchmark and Lessons Learned

Quality Assessment for Distance Higher Education Programmes

in Vietnam: Introduction, Benchmark and Lessons Learned

Ho Dac Hai Mien

Vice-Director of Center for Education Accreditation, Vietnam National University - Ho Chi Minh City

PhD student, VNUHCM-University of Social Sciences and Humanities

 

Abstract

In the context of Industry 4.0 and the pandemic of Covid – 19, the quality of distance eduction in higher education institutions has been much concerned by stakeholders. This paper introduces distance education development and quality assessment standards for distance higher education programmes in Vietnam. The paper also provides benchmark between Code of Practice for Programme Accreditation: Open and Distance learning issued by Malaysian Qualifications Agency and Vietnam’s quality assessment standards for distance higher education programmes issued by the Ministry of Education and Training, from that the lessons are withdrawn for improvement. Some suggestions for the Ministry of Education and Training are also mentioned with hope to shorten the gap with international standards and to promote distance education quality improvement in Vietnam.

Keywords: accreditation, distance education, quality assessment

1. General introduction on distance education in Vietnam

1.1. Definition of distance education

In Vietnam, distance education is defined as “an education process in which the student and the teacher are mostly separated in time and place. By this mode of education, the students study through textbooks, video tapes, audio tapes, CD-ROMs, computer software, using personal audio-visual media, broadcasting radio, television, multimedia communication complexes and the internet under the management and support of the school” (MOET, 2003). This definition contains basic contents that UNESCO as well as other international institutions define about distance education. Distance education includes online education (with ≥ 80% of the content delivered online) and blended education (with 30 – 79% of the content delivered online), as well as modes of education using printed material delivered by post and/or other tools for bridging the distance. Distance education programmes have the same volume, content and structure as traditional programmes of the same major and level. The degrees are guaranted in legality and value as other forms of education by the government.

There are 4 methods of distance education: a) Mail: The programme is conducted mainly by mail through the main printed learning materials (textbooks, reference books, study guides, researches, workbooks, exam and test guides); b) Radio and television: The programme is mainly conducted through radio and television systems, in which the main learning materials are radio and television programme that are broadcast live or replayed on radio and television channels; c) Computer network: The programme is implemented mainly through computer networks, internet and telecommunications networks in which the main learning materials are electronic learning materials transferred over the network, the teaching is carried out online or offline; d) Combination: combine the above methods (MOET, 2017).

1.2. The history of distance education development in Vietnam

The history of distance education development in Vietnam can be divided into 2 periods of time: before and after 2009. Before 2009, there were not many research on distance education in Vietnam. Around 2003-2004, distance education in Vietnam attracted more attentions from many higher education institutions through many seminars and conferences on distance education held at school and national level. Vietnam has joined the Asia E-learning Network (AEN) with the participation of the Ministry of Education and Training, the Ministry of Science and Technology, the Ministry of Posts and Telecommunications and Ha Noi University of Science and Technology. Besides, some software companies in Vietnam launched a number of products to support training. Although these products were not perfect, they have initially contributed to promote the development of e-learning in Vietnam (Pham Hong Hanh & Ha Thanh Hoa, 2019).

After 2009, being invested by businesses and higher education institutions, distance education gradually has attracted the attention of many learners/students. In 2013, Hanoi Open University built an online training technology system, becoming the first university in Vietnam to provide a full e-learning programme which has put the foundation in distance education in higher education institutions until today.

At present, Vietnam has over 100 bachelor degree programmes under distance education mangaged by around 21 higher education institutions. The programmes include a variety of majors such as: business administration, accounting, finance - banking, information technology, law, English, Vietnamese studies, tourism…

2. Introduction on quality assessment standards for distance higher education programmes in Vietnam 

2.1. The legal foundation of the establishment quality assessment standards for distance higher education programmes in Vietnam.

In Vietnam, the policy of distance education development and information technology application has been early concerned, first shown on the Project "Development of distance education in the period 2005-2010" approved by the Prime Minister (2005). The Project mentioned the policy of developing and improving the quality of distance education; creating conditions for people, especially who live in remote areas with difficult socio-economic conditions for regular study and lifelong learning; that contributes to enhance the education level, professional and vocational skills; raises people's intellectual level and trains human resources for country industrialization and modernization. In 2015, the Prime Minister (2015) continued to promulgate the Project "Development of distance education for the period 2015-2020" which set a specific goal for all distance higher education programmes to be accredited by 2020. Higher education institutions conducting distance programmes are also encouraged to be accredited by reputable international accreditation agencies. To implement this Project, two legal documents have been issued including Circular No. 10/2017/TT-BGDDT dated on April 28, 2017 of the Minister of Education and Training on Regulation on distance learning at university level (MOET, 2017) and Circular No. 39/2020/TT-BGDDT dated on October 9, 2020 of the Minister of Education and Training stipulating standards/criteria for assessing the quality of distance education programmes at university level (MOET, 2020). These two main documents created a solid legal foundation for the development of distance education, as well as emphasized the important attention to quality assurance of this form of education.

In particular, Law No. 34/2018/QH14 promulgated on November 19, 2018 by the National Assembly (2018) has a remarkable point in which there are no differences in degree value between different forms of education. All requirements of outcome standards, programme content, teaching methods... of training programmes, no matter what forms of training are implemented, must ensure the same quality. This adjustment reflects the spirit of integration with global education, creating equality for learners, and emphasizes the important role of quality assurance for all types of training.

From the above bases, standards/criteria for assessing the quality of distance education programmes at university level were born, making the foundation for the quality assurance and accreditation of this type of education.

2.2. The structure of quality assessment standards for distance higher education programmes in Vietnam.

Highly influenced by AUN-QA's assessment at programme level (AUN-QA, 2015), the quality assessment standards for distance higher education programmes in Vietnam (below called Vietnam’s standards) has about 80% of the same content as the AUN-QA’s assessment (see Figure 1). The remaining 20% ​​is designed to be adapted with Vietnam education context and with the features of distance education programmes (especilly facilities, infrastructure and materials, as well as Programme Management).

Figure 1. Comparison between AUN-QA and Vietnam’s model

The Vietnam’s model (see Figure 1: the model on the right) includes the following eleven standards: (1) Expected Learning Outcomes; (2) Programme Specification, Structure and Content; (3) Teaching and Learning Approach; (4) Student Assessment; (5) Academic Staff; (6) Support Staff Quality; (7) Student Quality and Support; (8) Facilities, Infrastructure and Materials; (9) Programme Management; (10) Programme Quality Enhancement; (11) Output.

The model starts with stakeholders needs which are transfered into the expected learning outcomes which drive the programme. In the centre, there are four rows in which the first row addresses the question of how the expected learning outcomes are translated into the programme; and how they can be achieved through teaching and learning approach and student assessment.

The second row considers the "input" into the process including academic and support staff; student quality and support; facilities and infrastructure and materials; and Programmes Management.

The third row addresses the programme quality enhancement which requires to identify the methods to get feedback from stakeholders, to measure and to use feedback for improvement. The concept of PDCA (Plan, Do, Check, Act) is manipulated into the process of improvement.

The fourth row focuses on the output of the programme including pass rates and dropout rates, the average time to graduate, employability of the graduates, research activities and stakeholders’ satisfaction.

The final column addresses the achievements of the expected learning outcomes and the programme.

The model finishes with the fulfilment of stakeholders’ needs and the continuous improvement of the quality assurance system and benchmarking to seek best practices.

3. Benchmark with Code of Practice for Programme Accreditation: Open and Distance learning (COPPA: ODL - MQA)

According to my research, in countries of ASEAN, Malaysia, specifically Malaysian Qualifications Agency (MQA) has developed and applied Code of Practice for Programme Accreditation: Open and Distance Learning (COPPA: ODL) for accreditation and program audit purposes. The COPPA:ODL contains specific indicators and benchmark standards that will guide the institutions in the development, delivery, assessment as well as the monitoring and review of the ODL programme (Malaysian Qualifications Agency, 2019).

Compared with COPPA: ODL, the Vietnam’s standards are mostly the same in term of assessment domains (see Table 1) . Although the distribution/arrangement of criteria and sub-criteria is different, both focus on assessing these following main areas: Programme Development and Delivery, Student Assessment, Academic Staff, Student Support, Facilities and Materials, Programme Managment, Continual Quality Enhancement.

 

 

Vietnam’s standards	COPPA: ODL (MQA)
1. Expected Learning Outcomes	1. Programme Development and Delivery
2. Programme Specification, Structure and Content	1. Programme Development and Delivery
3. Teaching and Learning Approach	1. Programme Development and Delivery
4. Student Assessment	2. Assessment of Student Learning
5. Academic Staff	4. Academic Staff;
6.  Support Staff Quality	4. Academic Staff;
7. Student Quality and Support	3. Student Selection and Support Services;
8. Facilities, Infrastructure and Materials	5. Educational Resources;
9. Programme Management	6. Programme Management
10. Programme Quality Enhancement	7. Programme Monitoring, Review and Continual Quality Improvement.
11. Output	7. Programme Monitoring, Review and Continual Quality Improvement.
11 criteria, 55 sub-criteria	7 criteria/areas, 21 sub-criteria

Table 1. Comparison between Vietnam’s standards and COPPA: ODL (MQA)

Through benchmark with COOPA: ODL, there are some important and special points that Vietnam could learn as follows:

Firstly, COOPA: ODL emphasizes the sufficience in academic autonomy of the ODL programme department and staff in programme development and management. These points are clearly mentioned at sub-criteria: 1.2.1, 2.3.1, 4.2.2, 6.1.4 (see Table 2). However, this requirements have not been mentioned in Vietnam’s standards while academic autonomy is one of the basic and important requirement in higher education. 

A HEP is expected to have sufficient autonomy, especially over academic matters. Such autonomy must be reflected at the departmental level where the programme is being designed and offered. 1.2.1  The department must have sufficient autonomy to design the curriculum and to utilise the allocated resources necessary for its implementation. 2.3.1  The department and its academic staff must have an adequate level of autonomy in the management of student assessments. 4.2.2  The academic staff must be given sufficient autonomy to focus on areas of their expertise. 6.1.4  The academic board of the department must be an effective decision-making body with an adequate degree of autonomy”.

Table 2: Sub-criteria: 1.2.1, 2.3.1, 4.2.2, 6.1.4  (Malaysian Qualifications Agency, 2019)

Secondly, COOPA: ODL requires clearly the allignment between the programme with Malaysian Qualifications Framework (MQF) in some aspects, such as: learning outcomes; assessement principles, methods and practices (see Table 3). However, in Vietnam’s standards, there is only one standard (sub-criteria 1.2) that refers the allignment between programme learning outcomes and Vietnamese Qualifications Framework (VQF), but the related requirements do not show in detail how and what clusters of VQF learning outcomes should be assessed. This could make the institution and external assessors pay less attention on this allignment and then not assess exactly and specifically the allignment level.

1.1.4  The programme learning outcomes must correspond to an MQF level descriptors and the five clusters of MQF learning outcomes: i. Knowledge and understanding; ii. Cognitive skills; iii. Functional work skills; iv. Personal and entrepreneurial skills v. Ethics and professionalism; v. Ethics and professionalism. 2.1.1  Assessment principles, methods and practices must be aligned to the learning outcomes, consistent with the levels defined in the MQF.

Table 3: Sub-criteria: 1.1.4, 2.1.1 (Malaysian Qualifications Agency, 2019)

 

Thirdly, COOPA: ODL guildline shows clearly the relationship between criteria/areas and sub-criteria (for example sub-criteria 1.1.2 must be read together with sub-criteria 1.2.2 in Area 1 and 6.1.6 in Area 6…). This helps the institutions and external assessors have an overview picture of the matrix of criteria/areas and sub-criteria relationship, from which the quality of assessment/self-assessment become more comprehensive. I can not find this information in the Vietnam’s standards.

Finally, through the information I got from the conference “Education & Research in the COVID-19 Era” organized by  DESE Australian High Commission KL from 23 -25 August 2021, 336 ODL programmes (the statistics updated to 30 April 2021), including provisional accreditation and full accreditation, were assessed and accredited using COOPA: ODL (MQA, 2021). While, in Vietnam, although standards have taken effect from November 2020, however, there has not been any programmes assessed and accredited. The lack of clear processes and guildances for implementing the standards should be the main reason for this situation.

As it can be found that there are many similar points in context between Malaysia and Vietnam, Vietnam should do more research on distance education quality assessment in Malaysia, including how to build and apply the COPPA: ODL, from that good practices could be learnt for improvement.

4. Conclusion and suggestion for Vietnam

This paper introduced the situation of distance higher education, as well as the quality assessment standards for distance higher education programmes in Vietnam.  The paper also showed some important points withdrawn through benchmark/comparison between COPPA: ODA (MQA) and Vietnam’s standards. Some more suggestions for the Ministry of Education and Training in Vietnam are shown as follows:

-      Conduct more regional and international research on distance higher education assessment, from that they have scientific and practical basis for building up the quality assurance system for distance higher education programmes.

-      Build sufficiently guildlines on process, methods and instrument for distance higher education assessment.

-      Encourage institutions who are offerring distance programmes to build up and carry out quality assurance process to distance programmes. When the internal quality assurance process is implemented well enough, they can be ready for external assessment and be accredited. 

I believe that the above suggestions if being concerned by the Ministry of Education and Training will help Vietnam’s standards shorten the gap with international standards and improve the quality of distance programmes in higher education institutions.

References

AUN-QA. (2015). Guide to AUN-QA Assessment at Programme Level Version 3.0. ASEAN University Network.

Malaysian Qualifications Agency. (2019). Code of Practice for Programme Accreditation: Open and Distance Learning (COPPA; ODL).

MOET. (2003). Decision 40/2003/QD-BGDĐT dated 08/8/2003 promulgating regulations on organization of training, examination, assessment, granting certificates and graduation degrees in the form of distance education.

MOET. (2017). Circular No. 10/2017/TT-BGDDT dated April 28, 2017 promulgating the Regulation on distance learning at university level.

MOET. (2020). Circular 39/2020/TT-BGDDT dated October 9, 2020 of the Minister of Education and Training stipulating standards for assessing the quality of distance education programs at university level.

MQA. (2021, August 23). MQA update on quality assurance in the higher education sector in Malaysia with respect to online education. Quality Assurance in Online Higher Education in Australia and Malaysia. Education & Research in the COVID-19 Era.

National Assembly. (2018). Law No. 34/2018/QH14 promulgated on November 19, 2018 by the National Assembly on amending and supplementing a number of articles of the Higher Education Law.

Pham Hong Hanh, & Ha Thanh Hoa. (2019). E-learning Training in China and Experiences for Vietnam. 120–121.

Prime Minister. (2005). Decision No. 164/2005/QD-TTg dated July 4, 2005 approving the Project “Development of distance education in the period 2005–2010.”

Prime Minister. (2015). Decision No. 1559/QĐ-TTg dated September 10, 2015 approving the Project “Development of distance education in the period 2010–2020.”

 

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