Critique of Design Models

In this article, I will analyze two ID models (Agile and Critical ID models) in terms of aspects such as origins, principles, pros and cons, and applications.

The Agile Design is developed by Agile Alliance in 2001, based on the principles of Embracing change to deliver customer value, delivering learning processes and platforms frequently, human centric, technical excellence, and collaboration with business people (Sidky & Arthur, 2008). The assumption of the model is to help knowledge workers to deal with new challenges and conditions in a VUCA environment, which means volatile, uncertain, complex and ambiguous (Adamson, 2012). As for the question how the model fits within the continuum of innovation, the model doesn’t simply impart knowledge or skills to learners, but to teach them the managerial skills to deal with knowledge. Students cultivated in this model will have the ability and critical judgement to search, scrutinize, evaluate legions of resources available online, and then can learn to tackle problems in the real world (Bates, 2015).

The key advantage of agile design is adaptability to different situations in which it operates. It responses instantly to students’ feedbacks during a course and makes adjustment accordingly. The differences between to the agile model and its counterparts is describe as a jazz combo to a big band (Bates, 2015). Another benefit is the accessibility of courses. Agile courses are open to diversified learners rather than registered students, such as training sessions in YouTube available to anyone interested in the topic. Nevertheless, the above benefits can be also considered from a negative angle. One apprehension may be the course content being misguided by students. As mentioned before, the contents are influenced by feedbacks of learners over the course, the discussion during the course might be involved in sensitive topics (e.g. politics, religions, etc.) if not well controlled. To make things worse, the openness to the public online may exert undesired repercussions. One example regarding this is from my personal experience of an open course on different ways of thinking between children China and Canadian. The topic transformed from academic field to political debate when some students introduced the political influence in relation to democracy and autocracy on younger generations. The problem might have been avoided should it be designed in a less agile and open model.

The other ID model analyzed in this article is the Critical Instructional Design, which was proposed by Sean Morris in 2016, the Director of Digital Pedagogy Lab. Rather than an iteration of traditional instructional design based on behaviorism or the ideologies of B. F. Skinner, the principle of the Critical Instructional Design stems from the philosophy of Paulo Freire and its contemporary counterparts, namely Howard Rheingold, Audrey Watters, Henry Giroux, bell hooks, and Jesse Stommel (S. Morris, n.d.).

The target learner in this model are students of all backgrounds, particularly groups such as minority groups (e.g. people of color, aboriginal students), LGBTQ folk, people with disabilities, etc. The model aims to cultivate practical capabilities such as job-related skills and mentality; these qualities are more prioritized in their future roles as an informed member of society (Aronowitz, 2015)

As for the question how the model fit within the continuum of innovation, the model doesn’t iterate the methodologies employed by other instructional designs; Rather, it follows a concept derived from Zen – to have “beginner’s mind” , meaning educators eradicate their stereotype of theories and preferred pedagogies, but explore a new method to re-approach the understanding of teaching, materials, and digital environment.

Its benefits include stimulating innovation of digital pedagogy (not limited to a set of supposed best solutioins), greater freedom to explore alternative pedagogies – it encourage a culture of questioning, which I see ass the key contribution to the understanding of innovation. It helps practitioners go out of their entrenched perception of distant learning and look for new answers. Likewise, the culture of questioning also changes the forms of students’ self-and social recognition, forming a space of translation between the private and the public. Nevertheless, the supposed new possibility may lead to risks caused by uncertainty. One apprehension is about the jeopardy of privacy online, given that the new learning activities will go beyond the surveillance of Learning Management System (LMS) and extend into students’ online life (M. Morris, 2018).

One case of using the Critical model can also be seen from my experience of an online course of Chinese speaking, where my methods align with the Critical design. I let myself go out of the normal way of simply ingraining knowledge into students, thus, but questioning the problems in existing material relative to students’ feedback and adjust content and tools to meet discrete needs of individual student, which is highlighted by the critical design as respect and care for students.

The implications of both models for practice is to transform traditional instructional design to an innovated measures share the same characteristics – to let me question the existing principles based on positivist and empirical knowledge, but to explore alternative strategies to achieve innovation.


Adamson, C. (2012) Learning in a VUCA world, Online Educa Berlin News Portal,

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Aronowitz, S. (2015). Against Schooling: For an Education That Matters (1st ed). Routledge.

Bates, T. (2015). Chapter 4.7 ‘Agile’ Design: flexible designs for learning. In Teaching in the digital age.

Kent, B., Mike, B., Arie, B., Alistair, C., Ward, C., Martin, F., James, G., Jim, H., Andrew, H., Ron, J., Jon, K., Brian, M., Robert, M., Steve, M., Ken, S., Jeff, S., & Dave, T. (2001). Manifesto for Agile software development.

Morris, M. (2018). Critical Instructional Design. In An Urgency of Teachers.

Morris, S. (n.d.).

Sidky, A., & Arthur, J. D. (2008). Value-driven agile adoption: Improving an organization’s software development approach. SoMeT_08 – The 7th International Conference on Software Methodologies, Tools and Techniques.

Selecting Design Models

While various factors are taken into account when educators select design models according to discrete objectives, I will place emphasis on the following determining factors when selecting an instructional design model. The first thing is reflection of my personal experiences, the pedagogies I used to, thus lifting myself out of personal cognitive limits such as presupposition, entrenched stereotype of education, or any bias based on empiricism beforehand. The second thing is to set up course objectives as a reference point, which is imperative to choose a suitable Instructional Design model that aligns with the desired methodologies, materials and behaviors. Not to be left behind is the needs and learning behaviors of students,which are the basis for the design of course materials and pedagogies (Ertmer & Newby, 2013). The last thing to consider is learning approaches of courses, whether these be digital courses or classroom-based, synchronous or synchronous – it will decide which ID models to use based on their different features.

After the scrutiny of the key considerations beforehand, the next step is to choose the appropriate design model. During the process of design decision, I will follow the Plan, Implement, Evaluate (PIE) model from Newby, Stepich, Lehman, and Russell (1996), which helps focus on the employment of technology in instructional design (Dousay, 2017).

During the design decision process, the role of design models is to move the process to a desired state to meet the requirements of various stakeholders, whether these be students, instructors or institutions. Models is also conducive to the selection or development appropriate operational tools and technology during the design process (Dousay, 2017). By the same token, innovation provides alternative methodologies during the process, introducing uncommon tools or materials that may bring fresh learning outcomes to students.

Of various design models, the one that stands out as especially useful in making decision is the ADDIE paradigm. Its 5 stages clearly identifies learning objectives of the courses, with the design of materials and content, controls the task and workloads for faculty and students, the evaluation of learning outcomes. Apart from a tool that implement instructional design in a highly systematic way, ADDIE also serves to be a management tool that guarantees distant courses at a high standard (Bates, 2019).


Bates, A. W. (Tony). (2019). Chapter 4.3 The ADDIE Mode. In Teaching in the digital age (2nd ed.).

Dousay, T. (2017). Chapter 22. Instructional Design Models. In Foundations of Learning and Instructional Design Technology (1st ed.).

Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2).

Assignment 3 – Speculative Futures Essay


The education in the 21st century is moving in a direction where almost all learning activities are dominated by computational technologies. While people are discussing the ramifications of the digitalized trend in the next decade, I will say that the prospect of education can be considered from a positive perspective with the introduction of big data analytics in both academic and management aspects. This essay will explore the future of education in 2030 in terms of the use of dataveillance of student and its impacts on the improvement of academic activities and learning management.

This essay is consisted of three parts. It first gives a brief background of the current situation of education in 2020s, highlighting the increasingly growing role of e-learning and the importance of digital technology in traditional campus-based learning environment. The background then leads to the key part of the article, which illustrates the employment of big data concept in improving educational practice, divided in two aspects – academic content and learning management. The academic improvement explores the benefits of dataveillance on learning efficacy, and on early identification of students’ problems and designing solutions. At the learning management level, the use of students’ data can be used by technology providers to optimize the design of learning tools and platforms.

Background of The Status Que of Education in 2020s

To predict the scenario of education in 2030, I need to first provide a brief introduction of the status que of education in 2020s – it is the premise of my projection of the prospect of what education will be in the next decade. One of the prominent characteristics featuring the education in 2020s is the combination of brick and mortar schooling and online learning trend. Thanks to the growth of smart devices penetration rate and the Internet, as well as the demand for closing the education gap both in developed and developing countries, e-learning over the past decades has been greater than ever in 2020s. As a result, digital learning is playing the predominant role of the 21st century in both education and adult training fields due to its advantages of low cost, high convenience, and accessibility (Pappas, 2019). This trend can be seen in the Online Education Statistics made by Bastrikin (2020), among a total of 19.7 million students enrolled in degree-granting courses, 6.6 million have chosen distance education/online options, with the majority of distance education students are undergraduates (5.5 million). As for learning behaviors of students, 87% of students reported using smart devices to access online study programs, and 67% complete course work via the Internet. From the perspective of faculty experience, in 2019, 46% of faculty members reported to have taught online courses for credit, in compared to 39% in 2016. The same trend be seen in adult training, with 77% of US companies using online learning In 2017 Elearning,Market Analysis, Trends And Forecasts (2020).

Adding to the point is the COVID-19 pandemic in 2020, which highlights the importance of distance learning across all sectors of education, from pre-K 12 to tertiary education. The pandemic has necessitated 93% of institutions to constitute policies on remote work for staff, while 43% of schools launch online learning courses.

Given the context, it is safe to predict that the next decade will witness the further employment of digital technology in teaching and management of education. This remainder of the essay aims to illustrate the relationship between new technology and digital learning in terms of several indicators, namely, dataveillance, the use of big data, and relationships between learning and other businesses.

Dataveillance in Studying Students’ Learning Behaviors 

Among these factors mentioned above, the first character featuring 2030 will be the dataveillance using big data to track students’ activities, whether it be academic performance or biometric measurements. In order to establish the relationship between big data and education in 2030, I will first clarify the definition of dataveillance and big data. According to Wikipedia, the term is a portmanteau of data and surveillance, which means the practice of monitoring and collecting online data as well as metadata (“Dataveillance,” 2020), whereas “Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software” (“Big Data,” 2020). When it comes to education, it means the data produced by students during the learning process, including the data generated as they are taking courses (Dahdouh et al., 2018). For centuries, to discipline students has been one of the prominent tasks of educators. After the introduction of computational techniques, the onus is shifted from the hands of teachers to the intelligent behavior management system in schools (Selwyn et al., 2020), which is defined as ‘code/space’ – a setting where brick-and-mortar spaces are intertwined with software code (Kitchin & Dodge, 2011). This mechanism tracks students’ academic statistics and biometric measurements, translating the student behavior information into digital data in favor of further improvement of educational activity in school and supportive intervention schemes.

At the academic level, firstly, the dataveillance mechanism help detect and solve education problems during the process of learning. Given the data analytics of all facets of students’ progress, whether these be their attendance frequency in specific course, exam marks, the comparison of the time spend on different courses online, teachers can find the areas that need to be fine-tuned, whether these be the setup of curriculum content and period or pedagogical methods. For instance, if statistics indicate that students spend an excessive amount of time to finish a particular course or homework, this probably means that the module needs to be improved in order to make it more suitable for the learners (Dahdouh et al., 2018).

Furthermore, dataveillance can predict learners’ future performances and therefore extent academic support before problems occurs. Thanks to big data analytics of student academic performance, educators can anticipate what difficulties are most frequently encountered by students, and then establish early intervention plan rather than react after difficulties occur. For example, this support can extent as early as to recruitment stage by identifying students who are most likely to struggle academically in their first semesters. Taking this point one step further, of the recruitment per se also benefit from dataveillance, when students behavioral information analytics help recruiters to better understand and predict the preferences of applicants, providing answers to questions as to what prospective applicants concerns more during their application process, what is the main reason they apply to certain schools (Dennis, 2019). As well as fostering academic performance, the analysis of data also contribute to the development of adaptive and customized learning systems (Dahdouh et al., 2018). It will indicate preferences of students and teachers behaviors in their educational activities, such as the most used browsers, the software or apps they use to access resources, etc. Such information will then be feedbacked to companies which design educational software and Learning Management Systems (LMSs), so these educational technology providers can design new or improve existing educational products in a more personalized and customized way that caters to discrete needs of individual users.

Adding to the point is the dataveillance contribute to the student recruitment of schools. According to the report Hudzik (2020), the COVID-19 disruption has resulted in overall shrinkage in the international student enrollments. In the US. For example, in America, available data indicate a 10% revenue shrinkage in 2020 and about 25% by 2021, which means $550–650 billion loss in the following years (Huang et al., 2020; McNichol & Leachman, 2020). This begs a question – what behavioral information can be applied to better recruit and enroll international students? – Again, it is the big data analytics that help recruitment teams understand the preference of prospective applicants during their application. Given the information, recruiters can better understand the key motivator for prospective applicants to apply to a school, or the main reason for their decisions, are used the data as indicators for schools’ branding strategies that evoke resonance with prospective international applicants (Dennis, 2019).


This article illustrates the future of education in 2030 with the employment of dataveillance and big data. The discussion of the importance data analytics is focused mainly on both academic and recruitment aspects. From the academic level, the collect and analytics of students learning behaviors and biometric information will play an increasingly important role in LMSs, for example by detecting students’ struggle with courses and offering early intervention, and by customizing curriculum design and pedagogy in a more personalized way. Furthermore, data analytics will be shared with educational platforms or product providers, as the basis to fine tune their design of content and service. While the prospect of digital education in 2030 remains lots of uncertainties to debate, I hope this essay can provide some insights into the potential importance of dataveillance and big data in this field.





Bastrikin, A. (2020). Online Education Statistics. Education Data.

Big Data. In Wikipedia. Retrieved from

Dahdouh, K., Dakkak, A., Oughdir, L., & Messaoudi, F. (2018). Big data for online learning systems. Education and Information Technologies : The Official Journal of the IFIP Technical Committee on Education TA  – TT  –, 23(6), 2783–2800. LK  –

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Dennis, M. J. (2019). How to recruit international students in the future. Enrollment Management Report, 23(6), 8–9.

Elearning,Market Analysis, Trends And Forecasts. (2019). Global Industry Analysts, Inc.

Huang, C.-C., Stone, C., Windham, K., & Beltrán, J. (2020). Putting the Size of the Needed COVID-19 Fiscal Response in Perspective.

Hudzik, J. K. (2020). Post-COVID Higher Education Internationalization.

Kitchin, R., & Dodge, M. (2011). Code/Space: Software and Everyday Life. MIT Press.

McNichol, E., & Leachman, L. (2020). States Continue to Face Large Shortfalls Due to COVID-19 Effects.

Pappas, C. (2019). Top 20 eLearning Statistics For 2019 You Need To Know. Elearning Industry.

Selwyn, N., Pangrazio, L., Nemorin, S., & Perrotta, C. (2020). What might the school of 2030 be like? An exercise in social science fiction. Learning, Media and Technology, 45(1).