Automated Differentiated Instruction for the eLearning Platform

As stated by John Macarthy (2010), in his Differentiated Instruction Toolbox, “Just about any strategy can be differentiated. What can make the processes effective is if student need is thoughtfully considered during the planning process. Every teacher differentiates in some fashion on a daily basis. The most effective and efficient methods are the ones planned during the lesson development, when student needs data can be evaluated and incorporated.”

Differentiated instruction entails various forms of instruction based on various factors related to the learning preferences and needs of the students. It is most popularly attributed to Carol Ann Tomlinson and Jay McTighe.  According to Tomlinson and McTighe (2006), “Differentiated instructions offers a framework for addressing learner variance as a critical component of instructional planning.” These variances or factors can stem from brain research, multiple intelligences, culture, language, ability/disability, gender, learning styles, and more.”

The Educational Challenge

With the growing notice and responsiveness to the impact and value of differentiated instruction, there have been various studies and promotion for differentiated instruction based on factors such as culture, language, ability/disability, gender and learning styles. Some factors have been deemed important enough that it has been put into legislation.

Differentiated instruction may be challenging in the online classroom. The online classroom is typically created via a course shell that has included in it all the standard curriculum, objectives, material and assignments. Typically instructional designers will work with subject matter experts to create this material based on the course objectives that have been approved and accredited by an accreditation board. A template will be followed and the material created to populate the course shell.

Individual instructors usually cannot change any items in the course shell. This is so as to promote a standard delivery of the course curriculum based on the approved and accredited objectives. These requirements can be found in the contract of employment with the institution, as well as, the editing capabilities in the course shell itself.

Furthermore, it is important that resources for all learning styles should be available to online students. The best way to facilitate this is to include and require these resources in all course shells. Even if an instructor has some ability to add to the course in a differentiated instructional manner, the standard, quality and frequency may not be the same as if it were implemented by the institution in the course shell itself. As a course shell standardizes curriculum, objectives and material, it can also standardize differentiated instruction based on all learning styles.

According to Susan Padalisky (2015), in her article titled, Utilizing Learning Management System (LMS) Tools to Achieve Differentiated Instruction, “When taking into consideration that teaching is tailored to meet individual needs, it becomes apparent that differentiated instruction means more work for the teacher or instructor. It also means the teacher or instructor has to continually change learning activities and is not able to use handy pre-designed ones because student progress or lack of progress informs teaching strategies. This chapter argues that differentiated instruction is worth the time and effort because it responds to individual needs, and responsive teaching maximizes each student’s success.”

I think another challenge to differentiated instruction and learning is knowing the student, as well as the student knowing themselves. This is more so in the virtual classroom. Differentiation instruction and learning is based on the individual student. While it is easier for instruction to contain a variety of resources, media, delivery and assessment strategies that will hopefully be effective for most of the students, the direct address to any individual student is difficult unless it is a one-one teaching or tutoring scenario. Classrooms in general have been grouped by age with the assumption that if they are of the same age, their skill levels and knowledge are the same as are the strategies needed to teach them and those they need to learn. This of course is not true.

Advertising and marketing research and implementations have long used user preferences when delivering results or advertising. Same can be done in a learning management system. If there is a good management of the elements for differentiated instruction and patterns created to identify the unique needs for each individual student, then the process can be automated to an extent and then personalization of the instruction and learning thereby inherently capturing the essence of differentiated instruction.

learninganalytics-smaller
(Simon, 2013)

Learning analytics are gradually being incorporated into the LMS. Some have them but some do not.  Some are more extensive than others. Furthermore, it is variant as to how many students and instructors actually utilize them to differentiate instruction or learning.

As stated by Kim Read (2015) in Learning Analytics, “…educators need to be trained and willing to use analytics appropriately. Administration needs to be provide instructors with adequate knowledge and resources to use learning analytics successfully. It is important that those managing and using analytics focus on what they need to know to make decisions about teaching and learning, and what particular data is needed to best provide the needed knowledge (Long & Siemens, 2011, p. 32).”

Parse the Technology

For differentiated instruction to be successful, curriculum standards and outcomes must be set for all learners, but the avenues used to successfully reach those standards and outcomes can differ. Differentiated instruction according to Tomlinson must involve content, process and product. A standard curriculum must be set, but teachers can differentiate the content, process and product to help learners learn based on their learning preferences and needs. (Tomlinson & Allan, 2000)

Differentiated Instruction based on Culture and Language

In an article by Michelle Trotman Scott (2014), “Using the Blooms-Banks Matrix to Develop Multicultural Differentiated Lessons for Gifted Students”, focus was placed on differentiated instruction for gifted Black and Hispanic students. Scott maintains that “Differentiating instruction is an instructional strategy used in classrooms throughout the nation. However, the content being differentiated most likely focuses on the dominant culture. At this point in time, it should be common knowledge that Black and Hispanic students are underrepresented in gifted education classes. Therefore, I argue that all students, more specifically, Black and Hispanic students would increase their academic performance if the curriculum peaked their interest. “

Scott further offers a color-coded matrix and quadrant guide with definitions of activities integrating the Bloom—Banks Matrix and the Ford—Harris Matrix maintaining that, “All of the quadrants support critical foundation work that aids all gifted students as they delve into deep, rigorous multicultural content. Differentiation, done the correct way, meaning the rigorous and culturally responsive way, will enable all students to increase their levels of knowledge and skills in their area(s) of strengths and they include advanced or accelerated multicultural educational options.” (Scott, 2014)

A study done by Deniz Erguyan (2014), “Instructors’ Perceptions Towards The Use Of An Online Instructional Tool In An Academic English Setting In Kuwait”, sought responses from English as a Second Language (ESL) and English as a Foreign Language (EFL) faculty members on the benefits of differentiated instruction using Computer Aided Language Learning (CALL) and Information and Computer Technologies (ICT). “The questions have been designed to seek responses about faculty members’ perceptions of using the branded program in English for Academic Purposes (EAP) practice, perceptions related to the strengths and weaknesses of the program, opinions about the contribution to student learning, and student attitudes towards web-based instruction. The analysis of the data reveal that participants have positive views towards differentiated instruction and seem to think this is one of the major strengths of the ICT tool. In addition to this instructors think ICT motivates students, adds variety to class, but it makes them question their role in the classroom, and also aggravates the already existing plagiarism endemic among students.”

 

Differentiated Instruction for the Exceptional Learner.

As Christy Keeler (2014) et al define, “The term exceptional learners is a generic one and means different things to different people. One population of exceptional learners is students with disabilities. As defined by the Americans with Disabilities Act (ADA)… In addition to students with disabilities, we use the term to refer to students with other special academic needs. This includes the 6.3% of the U.S. student population who are academically gifted, the increasingly large number of students with limited English proficiency and students who are struggling in school due to nontraditional learning styles or poor preparation for learning. Obviously, the needs of such diverse learners are different, whether receiving instruction in the classroom or through online courses.”  Through their study on students with disabilities, as well as, students that are academically gifted it was recommended that “To meet the needs of exceptional learners, online courses should be both accessible and supportive.”

Differentiated Instruction based on Disabilities

Examples of differentiated instruction based on disabilities is illustrated by Alice Ann Darrow (2015), in, “Differentiated Instruction for Students with Disabilities: Using DI in the Music Classroom.” Some of these examples are “A musical example might be having a choir student who has autism and is nonverbal find information about the composer of the choral piece the choir is working on and then distribute the information to the class. A student with multiple disabilities might be learning to access vocal music on an iPad or a computer….It was recommended that “All students are better served when instruction is flexible and there are options for learning and responding.”

Differentiated Instruction based on Gender

As stated by Kim Goebel (2014) in, Differentiated Instruction for Girls, “The female learner has unique needs that can be addressed through differentiated instruction…The curriculum should incorporate activities that utilize female students’ natural proclivities. Classroom activities should integrate group discussions in order for female learners to make meaning of the content. The learning environment should be a safe place for self-expression. The female learner has a strong grasp of emotions. The students should be allowed to make meaning through rehearsing and applying what they have learned. The female learner should also be given positive feedback for accomplishments.” Goebel goes on to make recommendations that differentiated instruction for females should contain the following: “Interpersonal connections when presenting material; Consider their ideas and encourage them to verbally expand upon their thoughts; Assign project based coursework; Present curriculum with relevant connections to the real world and relationships; Provide positive feedback whenever possible.”

 

Automated Differentiated Instruction

Automation of differentiated instruction and learning is something that is proposed in this paper. At the moment there is opportunity for differentiated instruction and learning, but must be manually sought out and done by the instructor or student. The resources may be there to choose from or the performance metrics available to determine that something more is needed. The whole intention for an automated system is to implement the appropriate action or resources for the instructor and student instead of this be needed or done by the instructor or student.

The Underlying Theory

Differentiation is most notably rooted in brain research. According to Schreiner et al (2013), in “Using Brain Research To Drive College Teaching: Innovations In Universal Course Design”, findings from neuroscience and research in areas such as the Brainstem, Limbic Area/Hippocampus, The Pre-Frontal Cortex and the Cerebellum and brain disorders contribute to identifying some of the factors for differentiation. “Because students’ brains can be expected to function differently, appealing to an imaginary “average” student not only fails to inspire excellence in teaching, but, in fact, presents barriers to learning…” Differentiated instruction allows for strategies to accommodate students based on these varying levels of brain functionality.

Multiple intelligences was introduced by Howard Gardner. In his book, Frames of mind: the theory of multiple intelligences /Howard Gardner (2011), he identified seven intelligences as Musical, Visual-spatial, Verbal–linguistic, Logical–mathematical, Bodily–kinesthetic, Interpersonal, Intrapersonal. Each of these various intelligences will exhibit differing abilities for which differentiated instruction can accommodate.

In Amy Brualdi’s (1996), “Multiple Intelligences: Gardner’s Theory”, “Accepting Gardner’s Theory of Multiple Intelligences has several implications for teachers in terms of classroom instruction. The theory states that all seven intelligences are needed to productively function in society. Teachers, therefore, should think of all intelligences as equally important. This is in great contrast to traditional education systems which typically place a strong emphasis on the development and use of verbal and mathematical intelligences. Thus, the Theory of Multiple Intelligences implies that educators should recognize and teach to a broader range of talents and skills.”

The Concept Map of Differentiated Learning

di_20concept_20map
(Tomlinson, C. A., & Allan, S. D., 2000)

According to Tomlinson and McTighe (2006), “Differentiated instructions offers a framework for addressing learner variance as a critical component of instructional planning.” These variances or factors can stem from brain research, multiple intelligences, culture, language, ability/disability, gender, learning styles, and more.

For differentiated instruction to be successful, curriculum standards and outcomes must be set for all learners, but the avenues used to successfully reach those standards and outcomes can differ. Differentiated instruction according to Tomlinson must involve content, process and product. A standard curriculum must be set, but teachers can differentiated the content, process and product to help learners learn based on their learning preferences and needs. (Tomlinson & Allan, 2000)

Content

“A teacher can differentiate content. Content consists of facts, concepts, generalizations or principles, attitudes, and skills related to the subject, as well as materials that represent those elements. Content includes both what the teacher plans for students to learn and how the student gains access to the desired knowledge, understanding, and skills. In many instances in a differentiated classroom, essential facts, material to be understood, and skills remain constant for all learners” (Tomlinson & Allan, 2000)

Process

“A teacher can differentiate process. Process is how the learner comes to make sense of, understand, and “own” the key facts, concepts, generalizations, and skills of the subject. A familiar synonym for process is activity. An effective activity or task generally involves students in using an essential skill to come to understand an essential idea, and is clearly focused on a learning goal. A teacher can differentiate an activity or process by, for example, providing varied options at differing levels of difficulty or based on differing student interests. He can offer different amounts of teacher and student support for a task. A teacher can give students choices about how they express what they learn during a research exercise—providing options, for example, of creating a political cartoon, writing a letter to the editor, or making a diagram as a way of expressing what they understand about relations between the British and colonists at the onset of the American Revolution.” (Tomlinson & Allan, 2000)

Product.

“A teacher can also differentiate products. We use the term products to refer to the items a student can use to demonstrate what he or she has come to know, understand, and be able to do as the result of an extended period of study. A product can be, for example, a portfolio of student work; an exhibition of solutions to real-world problems that draw on knowledge, understanding, and skill achieved over the course of a semester; an end-of-unit project; or a complex and challenging paper-and-pencil test. A good product causes students to rethink what they have learned, apply what they can do, extend their understanding and skill, and become involved in both critical and creative thinking. Among the ways to differentiate products are to:

  • Allow students to help design products around essential learning goals.
  • Encourage students to express what they have learned in varied ways.
  • Allow for varied working arrangements (for example, working alone or as part of a team to complete the product).
  • Provide or encourage use of varied types of resources in preparing products.
  • Provide product assignments at varying degrees of difficulty to match student readiness.
  • Use a wide variety of kinds of assessments.
  • Work with students to develop rubrics of quality that allow for demonstration of both whole-class and individual goals.” (Tomlinson & Allan, 2000)

Tomlinson continues further to identify student characteristics where teachers can differentiate. They are categorized into readiness, interest and learning profiles. It is further reflected that, “Attending to learner variance and need historically has made common sense in a classroom. This approach also reflects decades of proliferating knowledge about the brain, learning styles and varieties of intelligence, the influence of gender and culture on how we learn, human motivation, and how individuals construct meaning. Teachers and school leaders who spend time in a classroom see the significant array of learner differences. People who study the scholarship of this field understand differences and the need to attend to them, if we are to serve properly the children and families who trust us.” (Tomlinson & Allan, 2000)

While Tomlinson offers a good framework for what constitutes differentiation and how to approach it, in a publication offered by Gibson, Hasbrouk and Associates (n.d.), titled, Differentiating Instruction Guidelines for Implementation,   a good framework was offered for a differentiating instruction system itself.

di_20system
(Gibson, Hasbrouk and Associates, n.d.)

All these elements are crucial to having a successful differentiated instructional system which can be considered the administrative umbrella associated with the differentiation strategies and approaches as specified by Tomlinson.

I believe the capabilities of a smart system for differentiated instruction and learning incorporated into the learning management system with automation or semi-automation can reduce significantly the time it takes for the teacher and can be self-regulated and directed by the student, as well.

Having an automated smart learning management system that takes the results from learning analytics can then produce for the student and instructor the most appropriates instruction and learning components.

The Technology in Practice

There are many smart components to the current learning management system. Many of them can been used to address differentiated instruction and learning.

According to Emily White, (2015) in Self-Pace Learning Systems, “Self-paced Learning Systems are created to allow for instructors to optimize the learning experience for each individual student. Self-paced learning systems offer flexibility in the delivery, pace, and timing of instruction. These systems go beyond the traditional classroom as they enable knowledge to be transmitted seamlessly to those who need it (Magill, 2008). In some instances, the learner controls their learning process and have to be willing to learn. On the contrary, some self-paced learning systems are still controlled by the instructor. Self-paced Learning Systems mix the traditional instructional model with an asynchronous computer-based discussion. Self-paced learning systems allow for true student-centered learning on each and every topic. “

According to Christopher Ozarka, (2015), in Analysis of Game-Based Assessment Student Response System, “With formative assessment and gamification being pushed more and more in the classroom, many applications are being created to help teachers gain more formative data in a fun way in order to better gauge where their students are at in terms of understanding…A popular formative assessment tool known as Kahoot!, as well as many other formative assessment tools (i.e. Socrative, Zaption, Google Forms, etc.) are becoming an ever larger part of a teacher’s toolbox. Kahoot! remains one of the more utilized and loved formative assessment tools as of late due to its gaming nature and its fun competitiveness.”

Learning Analytics

Learning Analytics is a crucial component to the automated online differentiated instructional system. It can be seen already in dashboards, performance monitors, activity and completion tracking to name a few.

According to Xu Tian, (2015) in Learning Analytics in Education, “The use of learning analytics offers tremendous potentials to transform education from a one-size-fits-all delivery system into a proactive and responsive framework adapted to meet individual needs and interests. Learning analytics enables commercial companies or education institutions to gather data on students’ learning experiences to improve learning and teaching experiences, enable personalized learning, identify learning problems and at-risk students, and assess courses and programs. (Horizon Report, 2014)”

According to Kim Read (2015), “Analytics in education must be transformative, altering existing teaching, learning, and assessment processes, academic work, and administration.The data that feeds learning analytics originates from many places. The following is an incomplete list:

  • Time spent in the entirety of a course
  • Time spent in each course activity
  • Social interaction
  • Use of course resources
  • Assignment completion
  • Assignment grades
  • Detailed assignment grades such as which questions students got right or wrong
  • Number of discussion posts
  • Number of logins”

An example for an automated system is outlined in the work by Gaeta et al (2011), “Metacognitive Learning Environment: A Semantic Perspective”, “a web-based educational environment that sustains metacognitive self-regulated learning processes by means of Semantic Web and Social Web methods and technologies is proposed”. Three metacognitive skills are addressed:  Meta-Memory, Meta-Comprehension and Self-Regulation.

Many technology enhanced learning environments are rich with knowledge domains. However, depending on the student, they may not be utilized to the extent that they can be. Metacognitive artifacts to improve self-regulated learning is lacking.  There is “a weak relationship between self-regulated learning and technologically-driven functionalities.”

Gaeta et al further define e-Learning experiences as: Target Concepts, A Learning Path and A Presentation.

elearning_20experiences_20definition_20process
(Gaeta et al, 2011)

Assessment points are placed within the Presentation. The results of the assessments will then automatically build remedial works thereby combining personal learning with self-directed learning based on what the student needs.

This personalization takes into account the instructional design and best learning method, as well as, learning paths and learning content adaptation through just-in time processing based on learners’ knowledge, preferences and learning scores.

Processing is based on Object-Driven Learning where the learner can declare in simple natural language their learning needs in order to receive this personalized experience. Furthermore, based on the learner’s cognitive state and through the use of user- to-user collaboration based on similar concept maps, concept and learning objective utilization and educational social networks the learning is enhanced.

This automation can be extended further to address differentiated instruction and learning with the driving data from learning analytics.  It would be a matter of using the results from learning analytics as input into the smart differentiated instructional system to direct the automatic interaction and delivery to both the instructor and student.

Critical Reflection

Differentiating instruction and learning is not new. Furthermore, many successful strategies have been implemented with rewarding outcomes. But, they are limited to the differentiation they address. Not all strategies work for all needs. Furthermore, the educational technology and innovation is constantly moving forward, thus posing a challenge to any existing differentiating strategy.

The online classroom is a popular offering for higher educational institutions. According to the eLearning Industry, “Today, e-Learning is a $56.2 billion industry, and it’s going to double by 2015…Today, it’s estimated that about 46% college students are taking at least one course online. However, by 2019, roughly half of all college classes will be eLearning-based.” (e-Learning Industry, 2014).

Learning analytics is a current trend making its way into eLearning systems. However, there is still work being done on what data to analyze, as well as, a reliance on a data analyst, the instructor or student to interpret the results.

As Xu Tian (2015) states, “…research hasn’t clearly identified which variables should be tracked and what types of outcomes these data could have impact on.”

As stated by Kim Read (2015), “There’s also a concern that analysis of learning data may lead to inaccurate or questionable conclusions and recommendations. Another concern is oversimplification. Booth warns that without careful implementation and a focus on learning assessment, learning analytics runs the risk of, “becoming a reductionist approach for measuring a bunch of ‘stuff’ that ultimately doesn’t matter” (2012, p. 52). Booth calls for learning analytics to be used as a part of a larger assessment whole and acknowledges that analytics can’t see or measure all learning (p. 52). Long and Siemens (2011) point out that learning analytics can’t measure online students’ behavior outside of the LMS, for example, their use of online library databases, social media, or internet searching. They also don’t capture offline behavior such as physical trips to the library, academic advisors, or writing center. “

Both Kim Read and Xu Tian point out the risk in bias, prejudice, responsibility and ownership in what data to analyze and how.

Kim Read states, “Profiling and biases are another concern. Will data identifying a student as a low achiever stay with the student from course to course? What kinds of biases could this fuel? Will they be encouraged to take classes below their aptitude level? Will they be discouraged from taking challenging classes?

Questions of responsibility have also been raised. If the outcome of analytics recommends that students take certain actions to mitigate their coursework or grades, what happens if they ignore those recommendations? Where does the responsibility fall if they follow the recommendations but are still unsuccessful?”

Xu Tian states, “There are also legal, ethical and political challenges involved, such as data ownership, student privacy etc. If students feel their privacy is being invaded, they may be reluctant to allow their data to be used for research and analysis.”

Conclusion and Recommendations

I think this is an important proposal and I think is important to start working on it now especially with the growing popularity of eLearning. Some of the criticism for eLearning is based on this lack of differentiated instruction or even quality instruction. Furthermore, eLearning has been around but still the approach to it is in its infancy due to the slow growth and acceptance of this type of learning. There is still much doubt and criticism on the quality of this type of education and it needs to be resolved. I think automatic differentiated instruction and learning in a user-friendly and accurate way is a good strategy to allow for e-learning to be accepted and on par with that of the traditional academic institution.

Having a universal design for a differentiated instructional system is a doable proposition. With the current trends in smart systems and learning analytics coupled with the growing popularity of eLearning, automatically addressing differentiated instruction and learning merely needs to be designed and implemented. Furthermore, the challenges with the effective use of differentiated instruction and learning can be minimized with the automation providing that the crucial components for the factors to address and the analytics used for the input to direct the system is on point. Also, the time and effort needed by the student and instructor to seek out the various differentiated instruction and learning components will be eliminated. They will be readily available and appropriate for use for both the instruction and the learning and an improvement even over the traditional face-to-face classroom.

References

Brualdi, A. C., (1996). Multiple Intelligences: Gardner’s Theory. ERIC Clearinghouse on Assessment and Evaluation.  ERIC Digest. n.p.: 1996. ERIC. Web. 31 Jan. 2015.

Darrow, A. (2015). Differentiated Instruction for Students with Disabilities: Using DI in the Music Classroom. General Music Today 28.2 (2015): 29-32. EBSCO MegaFILE. Web. 30 Jan. 2015.

Gaeta M., Mangione G.R., Orciuoli F., Saverio, S. (2011). Metacognitive Learning Environment: a semantic perepctives. Journal of e-Learning and Knowledg Society, English Edition, v. 7, n.2., 69-80.

eLearning Industry, (2014). The Top eLearning Statistics and Facts For 2015 You Need To Know. Retrieved from http://elearningindustry.com/elearning-statistics-and-facts-for-2015

Erguvan, Deniz. (2014). Instructors Perceptions towards the Use of an Online Instructional Tool In An Academic English Setting In Kuwait. Turkish Online Journal of Educational Technology 13.1: 115-130. Education Research Complete. Web. 30 Jan. 2015.

Gardner, Howard. Frames Of Mind : The Theory Of Multiple Intelligences / Howard Gardner. n.p.: New York : Basic Books, c2011., 2011. EBSCO A to Z. Web. 31 Jan. 2015.

Gibson, Hasbrouk & Associates. (n.d.). Differentiating Instruction Guidelines for Implementation. Retrieved from http://www.gha-pd.com/samples/DIMod1Websample.pdf.

Goebel, K. (2015). Differentiated Instruction For Girls. Online Submission (2010): ERIC. Web. 30 Jan. 2015.

Keeler, C. G., et al. (2007) “CHAPTER 8: Exceptional Learners: Differentiated Instruction Online.” What Works in K-12 Online Learning. 125-141. n.p.: International Society for Technology in Education, 2007. Education Research Complete. Web. 30 Jan. 2015.

McCarthy, J. (2010). Differentiated Instruction Toolbox. Retrieved from http://learningclassrooms.pbworks.com/w/page/15831374/B1%20-%20DI%20Tools%20for%20Your%20Toolbox.

Ozarka, C. (2015). Kahoot! – Analysis of a Game-Based Formative Assessment Student Response System. Retrieved from https://cgscholar.com/community/profiles/user-27292/publications/61627.

Padalisky, S. (2015). Utilizing Learning Management System (LMS) Tools to Achieve Differentiated Instruction. Chapter 2. Retrieved from http://www.irma-international.org/viewtitle/114287/.

Read, K. (2015). Learning Analytics. Retrieved from https://cgscholar.com/community/profiles/user-96549/publications/61634.

Scott, M. T. (2014). Using the Blooms–Banks Matrix to Develop Multicultural Differentiated Lessons for Gifted Students. Gifted Child Today, 37(3), 162-168. doi:10.1177/1076217514532275.

Simon, H. (2013). Learning Analytics: Leveraging Education Data. Retrieved from http://edcetera.rafter.com/learning-analytics-leveraging-education-data-infographic/.

Tian, X. (2015). Learning Analytics in Education. Retrieved from https://cgscholar.com/community/profiles/xu-tian-78740/publications/61640.

Tomlinson, C. A., & Allan, S. D. (2000). Leadership for Differentiating Schools & Classrooms. Alexandria, Va: Association for Supervision and Curriculum Development.

Tomlinson, C. A., & McTighe, J. (2006). Integrating Differentiated Instruction & Understanding by Design: Connecting Content and Kids. Alexandria, Va: Association for Supervision and Curriculum Development.

White, E. (2015). Self-Pace Learning Systems. Retrieved from https://cgscholar.com/community/profiles/user-19078/publications/61623.

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