Adaptive Remediation Solutions Design Framework and Implementation for Student Success

IOSR Journal of Research & Method in Education (IOSR-JRME)
e-ISSN: 2320–7388, p-ISSN: 2320–737X Volume 5, Issue 4 Ver. II (Jul. - Aug. 2015), PP 104-119
www.iosrjournals.org
DOI: 10.9790/7388-0542104119 www.iosrjournals.org 104 | Page
Adaptive Remediation Solutions Design Framework and
Implementation for Student Success
Bhushan Talwar1
, Shradha Kanwar2
, Vijay Mandke3
1
(Educational Technology, NIIT University, India)
2
3
Abstract: E-learning has established a critical presence in the 21st
century learning environment. With the rise
of convergence technologies, different models of e-learning have emerged obliterating the barriers of time and
space and delivering deep learning. Teaching-learning interplay has been further enhanced because of novel
interactive process centric interventions and e-tutoring is emerging as an effective teaching learning solution.
Specifically, the paper addresses the current e-learning challenges by designing and implementing Adaptive
Remediation Solutions Framework to e-tutoring. The e-tutoring Adaptive Remediation Solutions Framework
deals with improvement of the overall learning experience by identifying the gap and adopting Learning to
learn driven remedial interventions with the specific cognitive requirements of the learner traditionally not
catered to in a standard (conventional) e-learning environment. The paper ascertains the improvement in the
student success rate by early identification of learners at-risk followed by timely, continuous and multi-tiered
teaching-learning interventions. Convergence Technology (CT) enabled Educational Technology (ET) has been
leveraged to offer innovative pedagogies by matching learning model in Adaptive Remediation Solutions
Framework with learners abilities & differentiated assessment. This has been evaluated through data obtained
from an e-learning course offered by a leading e-learning institute. The results clearly demonstrate that the
Adaptive Remediation Solutions Framework assists in creating an effective learning environment resulting in
improved student success rate.
Keywords: Adaptive Remediation Solutions Framework, Differentiated Instructions, e-Tutoring, Innovative
Pedagogies, Personalized learning
I. Introduction
Convergence Technology (CT) constitutes convergence of computing (Net and Mobile Computing
included), wireless sensory networks and telecommunications, consumer electronics and content. CT enabled
ET has productized the classroom and provided abundant opportunities for higher education worldwide in the
form of e-learning supporting teacher-learner, learner-learner, and learner-content communication. Over the past
ten years, e-learning has become a popular form of learning and more and more training organizations have
incorporated e-learning into their training delivery. According to the Australian Flexible Learning Framework‟s
2009 E-learning Benchmarking Report, 90 per cent of training students and 87 per cent of trainers in the
national training system use some form of e-learning . In Western Australia, the number of units involving e-
learning at registered training organizations jumped 13 per cent from October 2008 to October 2009. So strong
is its demand that it is now a significant deciding factor for students thinking of enrolling in a particular course
(the rise of e-learning, 2009) [1]. Different models of e-learning have emerged over a period of time and gained
popularity and acceptance because of their ability to obliterate the barriers of time and space. As a result,
enrollments in higher education institutes having e-learning has been increasing with better accessibility
opportunities to almost everyone. However, the student dropout rate has increased with time. In 2009, according
to a U.S. Department of Education study, over one-third of first-year undergraduate students reported enrolling
in remedial or developmental courses (2011)[2]. This is an indication of the dip in higher education standards.
Therefore, retaining students remains a top priority for colleges and universities in the US and across the globe.
The current e-learning system is abundant with rich resources and easy access that allows learners to engage in
independent learning, but it has certain limitations in terms of relevant information sharing and monitored
knowledge progression. A major limiting factor is the inadequate analysis of learning engagement and
effectiveness and a disjointed assessment process.
Davidovic, Warren and Trichina (2003) in the paper „Learning benefits of structural example-based
adaptive tutoring systems‟ illustrated and evaluated a generic adaptive tutoring environment, structural example-
based adaptive tutoring system (SEATS), based on the theory of cognitive knowledge acquisition. SEATS was
evaluated with a recursion tutorial used by 117 students in a 1-hour tutorial session. Results indicated that using
adaptation in combination with the structural example-based feature produced an effect on rate and extent of
learning significantly greater than when the features were used alone, or when both were absent [3]. Tutoring

Adaptive Remediation Solutions Design Framework And Implementation For Student Success
has a history as a tool to improve students‟ academic achievement in the United States (Cohen, Kulik, and Kulik
1982; Wasik and Slavin 1993; Invernizzi 2002) [4] [5] [6]. Ramsden (1992) argues that effective teaching
facilitates effective learning by students [7]. E-tutoring applied with Adaptive Remediation Solutions
Framework focuses on creating a personalized, customized and timely anchored instructional interplay between
the teacher and the learner leading to adequate analysis of learner‟s engagement and effective and constructive
assessment process. E-tutoring can be defined as teaching, support, management and assessment of students on
programs of personalized study that involve a significant use of online technologies (TechLearn, 2000) [8].
Researchers have provided guidelines for effective and efficient education. It is suggested that the sound
learning theories are incomplete or unrealistic if they do not include a whole person view, integrating both
cognitive and affective aspects, implying that no educational program can be successful without due attention to
the personal learning needs of individual students (Snow and Farr 1977) [9]. The educators should identify and
acknowledge learning differences and make maximum use of the available technology to serve them
accordingly (Russell 1977) [10]. Brusilovsky advocates, using adaptive hypermedia to support individual
learning. By `adaptive„, he means to adapt both the content and presentation of the course based on the profile of
the learner. (Brusilovsky 2002) [11]. Paolucci addresses the importance of individualization that any strategy
should be adaptive and personalized (Paolucci 1998) [12]. Therefore, one of the most formidable tasks for
educators is shaping their presentations of core knowledge to meet the needs of individual learners having
varied and diverse cognitive and psychological traits (Whitehurst, Powell and Izatt 1998) [13].
In the last 30 years education theory has shifted to advocate a constructivist theory of learning (Ng &
Cheung, 2007) where the role of the university professor is „the guide on the side‟ rather than the „sage on the
stage‟ (King, 1993) [14][15]. Collison, Elbaum, Haavind and Tinker (2000) propose that this is also the most
appropriate role for leading a virtual learning community. They suggest that the tasks of an online instructor or
e-tutor should include; being aware of all postings within discussion forums, encouraging participation and
keeping track of the involvement of individual students; keeping the discussion focused; and encouraging higher
order thinking [16]. However Cox et al. (2000) suggest that the unique role of the e-tutor requires continual
reappraisal in an environment where technology change is constant [17]
Tutoring is a successful system for traditional learning as Prensky (2002) among others points out.
Prensky states that tutorial learning is more efficient than learning in traditional classes because of the personal
one-on-one interaction between the tutor and the student. And although tutoring in traditional (physical)
environments is still an expensive form of learning, Prensky reminds us that the current technology enables us to
connect more students with a single tutor at lower costs and over greater distances. (Prensky, 2002) [18]. A
similar opinion is shared by Bork, who emphasizes learning over teaching. In his opinion the currently
prevailing information transfer educational paradigm, in which the teacher transfers “knowledge” to often
passive students should be changed. In his opinion the future lies in the tutorial learning paradigm, in which
learning is seen as an active process in which learners play the leading role. This paradigm will focus on
learning in smaller groups with the support of a tutor. The key elements of the tutorial learning paradigm are
interactivity, individualization, adaptability, creativity, collaboration etc. (Bork, 2000) [19].
E-tutors support e-learners through different roles. Authors have different classifications of roles tutor
perform in e-learning environments. An often cited classification made by Berge defines four basic roles of an
online tutor: pedagogical, managerial, social and technical (in McPhearson & Nunes, 2004) [20]. In the
pedagogical role tutors support the learning process itself by providing instructions, stimulating questions,
examples, feedback, motivation etc. to the learners (Teles et al., 2001). The managerial role requires the tutor to
perform basic course administration, track student progress and data etc. (Teles et al., 2001) [21]. The tutor‟s
social role includes the efforts to establish a friendly and comfortable environment and a community that
stimulates learning, while the technical role requires the tutor to acquaint the students and himself/herself with
the ICT that is used for e-learning, and also to provide some technical support to the students (McPhearson &
Nunes, 2004) [20]. The ARS framework supports e-tutors/learners to be successful in all the required roles.
Teaching-learning interplay happens wherein there is interplay between contextualization and de-
contextualization, between constructive conflict and deconstructive conflict, between internal regulation and
external regulation and between teacher and learner. Therefore, teaching-learning interplay has been further
enhanced because of novel interactive processes that provide multiple learning pathways and e-tutoring is
emerging as an effective intervention in this regard. Based on the above indications of Adaptive Remediation
Solutions, there is a need to study & analyze the current student retention framework and develop a
comprehensive Adaptive Remediation Solutions Framework for Student Success. This new framework has
been designed to address the problem of student retention by early identification of students at-risk followed by
continuous teaching-learning interventions (differentiated instructions) matched with learning intelligences of
students.

II. Approach
The focus of this research is on designing of a framework to meet differentiated learning abilities of
learners. The work involves identification of drivers for enhancing the e-tutoring experience by exploring and
documenting best practices and key-learning for designing the Adaptive Remediation Solutions Framework.
Based on the Framework‟s findings, a generic e-tutoring instruction on providing a holistic interface with the
learner is created and implemented. As a part of the implementation of this model, tutors are trained in the
process of training & tutoring for Learning to learn. In the process, tutors are sensitized to customized,
personalized and timely interventions for learners leading to improved participation, engagement and
attributions in the learning process. The researchers have critically studied & analyzed the Adaptive
Remediation Solutions Framework through early signs of learner at-risk, different learning styles, importance of
learner‟s engagement and timely pedagogic interventions to meet differentiated needs of learners. The factors
of risk profiling and student advising are leveraged to propose an Adaptive Remediation Solutions Framework
for Student Success. The effectiveness of the model is further analyzed and validated by its application to 411
learners spread across four semesters of a leading e-learning institute. Based on this a generic systems view of
an internetworked, integrated Adaptive Remediation Solutions Framework has been developed and presented in
this research paper.
III. Adaptive Remediation Solutions Framework
This section of the research paper presents two critical components of the Adaptive Remediation
Solutions Framework for Student Retention and Performance.
3.1 Risk Profiling
Risk profiling is a necessary step in recognizing the possibility of a learner‟s non-participation,
disengagement and subsequent withdrawal from an assigned e-course. It is an important criterion to evaluate the
extent of threat to the teaching-learning process and an early indicator of students at-risk towards designing the
necessary remedial intervention. The continuous risk profiling helps in early identification of students at- risk by
categorizing students as Red, Amber and Green on the basis of the below mentioned two parameters.
3.1.1 Active Participation & engagement
The best learning occurs when students are engaged in active learning – when they are doing things
instead of sitting passively and listening. A classic study by the National Training Board US found that students
retained only 5% of the information they received in lecture twenty-four hours later. Retention rates increased to
75-90% when active learning involving peer teaching was used instead of lectures. Other active learning
methods (e.g., demonstration and discussion) also resulted in higher retention rates (30% and 50%,
respectively). In another study of the effectiveness of lectures (McLeish 1968; cited in Fink 2003), students
were tested on their understanding of facts, theory, and application after hearing a lecture that was specially
designed to be effective. Despite being able to use their own lecture notes and a printed summary of the lecture,
average student recall after the lecture was only 42%. A week later recall had dropped to only 20% [22] [23]. In
a recent review of the effectiveness of active learning, Prince (2004) found extensive, widespread support for
active learning approaches, especially when activities were designed around important learning outcomes and
promoted thoughtful engagement [24]. Many instructors recognize that active learning results in significant
improvements in student knowledge retention, conceptual understanding, engagement, and attitudes about
learning. According to Vonderwell and Zachariah (2005, p.214), “Learner participation is an essential element
for active and engaged learning”[25]. Although student participation is not a direct measure of learning, its
necessary in order for a discussion to occur in the first place; and through the discussion, it is more likely that
learning takes place (Dennen, 2005) [26]
So, active participation promotes learning in the classroom and is important for receiving the full
benefit of class discussions, announcements, and learning materials. If learners do not attend regularly, interact
with classmates and class materials, and invest themselves actively in the learning process, they are unlikely to
succeed academically. Therefore active participation and engagement is a major aspect of risk profiling and thus
needs to be monitored and managed on a regular basis. In order to analyze this correlation, tracking of students
is done on the basis of learners engaging in one of the following activities: a) submission of a course
assignment, b) participation in a course discussion thread by posting a comment, question, or response related to
a course topic, c) submission of an “Ask the Instructor” question in the course management system, or d)
submission of a Quiz or Exam. All these activities are tracked by LDA (Last Day of Access) feature of the
Learning Management System (LMS). Instructors are encouraged to track student participation and engagement
using this feature and to contact learners who appear to be disengaging from the class. This analysis of active
participation and engagement of learners in the ARS framework is a useful indicator to profile the learning risk.

3.1.2. Attribution in the classroom
There have been studies in the past to ascertain the correlation between the kind of attributions students
make and reasons for making those attributions. One of the most important findings from these studies is that
different students make different kind of attributions. Some differences are related to gender, others are related
to student‟s perceptions of ability and few are related to the ways teachers respond to students (Stipek, 1993)
[27].A review by Peterson (1990) found that student‟s negative attributional styles are related to low grades, less
help seeking, vaguer goals, poorer use of strategies and lower performance expectations [28]. Best practices of
face-to-face tutoring in Socratic mode also apply to online tutoring. However, some students resist the guided
discovery learning process. Miller suggests that “it‟s important to communicate to the student why you‟re doing
it this way and that it won‟t take long before they get it on their own” (S. Miller, personal communication, April
21, 2001) [29].
Studies focusing on help seeking behavior in particular have reported that many learners do not seek
help because doing so provides an explicit low-ability cue to one‟s peers. The non-help seeking behavior is
tracked by the un-attempted assignments. The number of un-attempted assignment is a good indicator of
negative attribution. The students with higher number of un-attempted assignments are most likely to dropout in
a course. This risk is tracked by the number of un-attempted assignments for a particular course in the LMS and
managed by providing strategies for mastering course concepts where comprehension appears to be lacking.
To summarize, the performance of a learner in a course is a reflection of his/her active participation &
engagement and attribution in the learning process. The performance of a learner is measured by the grade
percentage and calculated basis student performance for graded activities on a weekly basis. In case of a non-
graded activity, on completion of the activity, the student is awarded a 100% score. This is an important criteria
and truly reflects a student‟s risk profile. Students with high grades are less likely to be dropped than students
with low grades. This metric has been used in the pilot to evaluate performance of learners.
The Fig 1 below shows the effectiveness of Teaching-Learning process by early identification of
learners at-risk followed by timely, continuous and multi-tiered Teaching-Learning interventions.
Fig 1. Effectiveness of Teaching-Learning process
The detailed criteria for representing learners as Red, Amber and Green is shown in Table 1 below.
Table 1. Criteria for representing learners as Red, Amber and
Green
Risk Profile Engagement
Activities
(LDA)
Number of un-
attempted
assignments (UA)
Current Grade
Percentage (CGP)
Red LDA > 2
weeks
UA > 4 CGP ≤ 64%
Amber LDA > 1 week UA ≤3 65% ≤ CGP ≥ 74%
Green LDA ≤ 1week UA = 0 CGP≥75%

The Red category students are most likely to drop out or not achieve success in the course. The Amber
category students meet minimum standards and have an opportunity to improve and become Green students.
The Green students achieve and exceed all standards and need to be motivated to set higher standards for
themselves. The above representation helps to monitor a student‟s progress at intermediate steps because the
progress at each intermediate step is closely tied to the final goal.
The technology has been leveraged to monitor student‟s performance on a weekly basis. The Risk
profiling tracker has been developed in Microsoft Excel which is widely used as an effective data analysis tool.
This tracker has two sheets in it i.e. a) Input sheet b) Output sheet. The Microsoft Excel „Macro‟ functionality
has been used to define rules & algorithm that specifies how input data should be mapped to the output
sequence. The output sheet classifies students into Red, Amber & Green on each parameter as per the
established calculation explained above and calculates the overall status of the student in terms of Red, Amber
& Green. If a student is Red in one of the three parameters, his/her overall status becomes Red irrespective of
the fact that he might be Amber/Green in rest of the two parameters. Likewise, if a student is Amber in one of
the three parameters, his/her overall status becomes Amber even if he/she is Green in rest of the two parameters.
A student needs to be Green in all three parameters to secure an overall Green status. This not only makes first
level data analysis task less tedious, less repetitive and less error-prone, but also facilitates the process of
illustrating information on a real time basis to enable timely interventions (decision making). A sample student
profiling output sheet is shown in Table 2 below
Table 2. A sample student profiling output sheet
S
No
Stude
nt
ID/PI
N
Last
Name
First
Nam
e
Current
Grade
Percenta
ge
Last Day of
Access
Un-
attempted
Assignment
Instruct
or
Name
Current
Grade
Status
LD
A
Sta
tus
Un-
attempted
Assignment
Status
Overall
Status
1 1 Q a 74%
8/13/2014
13:56
1 z Green
Gr
een
Amber Amber
2 2 W s 34%
8/4/2014
0:07
7 x Red
Re
d
Red Red
3 3 E d 47%
7/28/2014
22:01
4 c Red
Gr
een
Red Red
4 4 R f 62%
8/5/2014
0:07
1 v Red
Gr
een
Amber Red
5 5 T g 87%
8/11/2014
0:04
2 b Green
Gr
een
Amber Amber
6 6 y h 82%
8/12/2014
22:40
0 n Green
A
mb
er
Green Amber
7 7 u h 63%
8/6/2014
19:38
2 m Red
A
mb
er
Amber Red
8 8 i j 13%
8/4/2014
10:28
8 l Red
Re
d
Red Red
9 9 o k 91%
8/12/2014
7:29
0 k Green
Gr
een
Green Green
3.2 Process Centric Interventions
Process-centric interventions are designed to facilitate the teaching-learning interplay by providing
customized and differentiated instructions on the basis of analysis of the learning risk. The key to improving
success rate is to monitor progress at an intermediate level since the progress at an intermediate level is tied with
the success in the final goal. In the event, the best strategy is to deploy an adaptive remediation program that is
personalized to student‟s needs. As shown in Fig 2, the process centric interventions are broadly categorized in
two areas.
3.2.1 Learning Skills
Learning to learn is the ability to pursue and persist in learning, to organise one‟s own learning,
including through effective management of time and information, both individually and in groups. This
competence includes awareness of one‟s learning process and needs, identifying available opportunities, and the
ability to overcome obstacles in order to learn successfully. This competence means gaining, processing and
assimilating new knowledge and skills as well as seeking and making use of guidance. Learning to learn
engages learners to build on prior learning and life experiences in order to use and apply knowledge and skills in
a variety of contexts: at home, at work, in education and training. Motivation and confidence are crucial to an
individual‟s competence (European Communities, 2007, p. 8) [30]. As per Fredriksson and Hoskins, one of the
basic skills for success in the knowledge society is the ability to learn. With increasingly rapid changes in the
work place, in part due to changing technology and as a result of changing societal needs in the context of

globalization, citizens must learn to learn in order that they can maintain their full and continued participation in
employment and civil society or risk social exclusion. In this context learning to learn is a quintessential tool for
lifelong learning and thus education and training needs to provide the learning environment for the development
of this competence for all citizens and through different learning environments (formal, non-formal and
informal) (Fredriksson and Hoskins, 2007) [31].
There has been a shift of focus in learning to learn from the subject-specific knowledge aspect of
today‟s assessed learning to the diverse cognitive and affective factors that guide learning. These factors not
only direct the learning process but they are also reflected in the way in which learning is applied to novel tasks.
The focus of education is shifting from “teaching” to “learning” today. Faculty roles are changing from lecturing
to being primarily “designers of learning methods and environments” (Barr and Tagg 1995, cited in Fink 2003)
[23] [32]. The NRC (2000) recommends that the goal of education shift from an emphasis on comprehensive
coverage of subject matter to helping students develop their own intellectual tools and learning strategies. These
intellectual tools and learning strategies enable learners to benefit from instructions [33].
Learning to learn skills become more important in the online learning environment. At times, working
in the online environment is new for both tutors and students. Miller recommends “trying to encourage the
students as much as possible because they often tend to feel quite lost, alone and discouraged. Let them know
that the online procedure is new and will get easier” (S. Miller, personal communication, April 21, 2001) [29].
Tutors are trained to develop Learning to learn skills by encouraging and motivating learners to set short-term
and long-term study goals (weekly assignments vs. application of concepts), assist in cognitive restructuring and
information processing. Learners are encouraged to use Schema Activation, Guided Questioning & Deep
learning strategies to encode complex information.
3.2.1.1 Schema Activation
Schema activation refers to various methods designed to activate learners‟ relevant knowledge prior to
a learning activity. The central idea underlying schema activation is that new knowledge always builds on prior
knowledge; that is, a foundation of well-understood information will help students comprehend new
information and will guide their thinking about the new topic. In summary, schema activation is any teaching
procedure that helps students form conceptual bridges between what they already know and what they are to
learn. It‟s done by encouraging learners to describe examples from thier previous experiences, perform
experiments, review previous learning and use the context in which the new learning is presented.
3.2.1.2 Guided Qustioning
Asking and answering questions about a text or teacher-presented information can greatly improve
comprehension, especially when those questions prompt students to think about and discuss material in specific
ways, such as comparing and contrasting, inferring cause and effect, evaluating ideas, explaining and justifying.
Research suggests that answering questions while performing a task is more useful than answering questions
while learning about a task. This may occur because students fail to integrate information completely or are not
fully prepared for inference –type questions until they actually attempt to perform the task. Guided Questioning
is applied while responding to learners queries using ATI (Ask the Instructor) feature of the Learning
Management System.
3.2.1.3 Deep Processing
The information can be processed in the below mentioned two ways.
o Deep processing is centered on meaning. This involves semantic processing, which happens when we
encode the meaning of a word and relate it to similar words with similar meaning.
o Shallow processing refers to keying on superficial aspects of new material. It takes two forms i.e. structural
processing (appearance) & phonemic processing (sound)
As per Savin-Baden and Major, there are both „surface‟ and „deep‟ approaches to learning (Savin-
Baden and Major 2004) [34]. Surface approaches to learning concentrate on memorization (Bloom‟s lowest
level: knowledge). In surface learning, the learner‟s goal is often to complete required learning tasks by
memorizing information needed for assessments. Surface learners mostly focus on facts without integration,
they are generally unreflective, and they see learning tasks as external impositions. In contrast, students with
deep approaches to learning have an intention to understand. They generally engage in vigorous interaction with
content, relate new ideas to old ones, relate concepts to everyday experience, relate evidence to conclusions, and
examine the logic of arguments. Deep processing is encouraged by focusing on the semantic base or meaning of
the new information so that the information is stored in a semantic memory code and is well-remembered.
These skills are referred as Learning to Learn in the Fig 2 below.

3.2.2 Subject Matter Skills
The subject matter learning involves more than the delivery of standard instructions. The knowledge
of the ideas, facts and theories of a subject is one aspect of the subject matter skills whereas understanding &
application of the subject is another aspect of it. Whether or not such understandings are explicit goals of
instruction, students develop ideas about the subjects they study. Beers (1988) argues that while epistemological
issues are rarely made explicit in classrooms, they are implicitly represented in the organization and content of
curriculum, in the interaction between teachers and students, and in the nature of classroom activity and
discourse [35]. What teachers need to know about the subject matter they teach extends beyond the specific
topics of their curriculum. Shulman (1986) argues that "teachers must not only be capable of defining for
students the accepted truths in a domain. They must also be able to explain why a particular proposition is
deemed warranted, why it is worth knowing, and how it relates to other propositions" (p. 9) [36]. This kind of
understanding encompasses an understanding of the intellectual fabric and essence of the subject matter itself.
For example, while English teachers need to know about particular authors and their works, about literary
genres and styles, they also needs to know about interpretation and criticism (Grossman, in press) [37]. A
history teacher needs detailed knowledge about events and people of the past but must also understand what
history is: the nature of historical knowledge and what it means to find out or know something about the past.
Scheffler (1973) writes that this kind of subject matter understanding "strengthens the teacher's powers and, in
so doing, heightens the possibilities of his art" (p. 89) [38].
In this research paper, Subject matter refers to knowledge and comprehension of technical/subject
skills. In the ARS framework, these skills are continuously honed for deep learning and better retention and are
developed by sharing additional and preparatory content for students to supplement standard instructions,
clarifying students‟ doubts by understanding the context of the difficulty, providing quick resolution through
synchronous learning (online chat) and sharing digital resources for quick reference and reinforcement of
learning. Rich media, multimodal interplays provided through CT enabled ET in the form of internet, email,
online chat, discussion forums, collaborating tools, social networking platforms as shown in Table 3 have been
leveraged extensively.
Table 3. Description of CT enabled ET interventions
Intervention Description
Student Cal Connect (check) Calling students to understand issues and motivating them to log in
and be regular with work
Mentoring e-mails Sending e-mail asking students to log in (for students not logging
into the portal).
Ask-the-Instructor feature Ask-the-Instructor feature to facilitate 1:1 clarification
Cognitive Hooks E-mailing students tips and tricks in complex subject areas as a hook
to initiate their learning and reinforce concepts
Concept Guide Sending additional content and preparatory content for students who
feel standard instructions are not enough for them
Learning Alerts Sending alert on upcoming milestone (automated alerts triggered by
Learning Management System)
Synchronous Chat To clarify students doubts by understanding the context in which
he/she has difficulty and providing quick resolution in the form of
synchronous learning
Asynchronous discussion &
collaboration
Discussion forms to encourage peer learning and social learning
Digital resources Sharing digital resources for quick reference
These skills are referred to as Subject Help in the Fig 2 below.

Fig 2. Process centric interventions including learn to learn and subject help
Apart from the above, „Know your Learner‟ (KYL) process has been initiated to discover learner‟s
preferred learning style. This is important since learning style greatly affects the learning process, and therefore
its outcomes (Hong and Kinshuk 2004) [39]. The underlying idea of a learning style approach is that a learner is
more effective when information is presented in a manner that matches his/her preferred methods of acquiring
and processing information. Learning styles are defined differently by different researches. For example, Alonso
defined learning styles according to cognitive psychology as “personal manners to perceive and process
information, and how they interact and respond to educational stimuli” (Alonso 1993) [40], while Keefe defined
learning styles as “characteristic cognitive, affective and psychological behaviors that serve as relatively stable
indicators of how learners perceive, interact with and respond to the learning environment” (Keefe 1979) [41].
In the ARS framework, this has been introduced in the nascent stage and needs to be further explored and
assessed.
Personalized interventions for Red, Amber and Green learners is further illustrated below in Table 4.
Table 4. Personalized interventions for Red, Amber and Green learners
Classification of student Personalized Interventions
Red  Calling students to understand concerns and motivating them to log in
and be regular with work
 Differentiated Instructions to meet varying needs of learners
 Sending advising emails asking students to participate in the learning
process
 Sending academic alerts to the resident dean of each school
Amber  Differentiated Instructions to meet varying needs of learners
 Sending advising email asking learners to participating in the learning
process
 Sending academic alerts to residents dean of each school
Green  Encouraging emails to applaud students on achieving defined milestones
 Sending additional questions and or supplemental instructions that
provide challenge to the high ability group

IV. The Experiment (Pilot)
In order to validate effectiveness of the Adaptive Remediation Solutions (ARS) Framework for Student
Success, learners‟ data was collected from an e-learning course offered by a leading e-learning institute.
1.1 Data Collection and validation
The below mentioned process was followed for data collection and validation
 The learners pass rate data was collected for 4 semesters (Dec-2013, March-2014, June-2014, Sep-2014)
from a leading e-learning institute.
 Each semester had multiple sections randomly assigned to e-tutors
 The selection of the e-leanring institute was on the basis of getting an opportunity to create differentiated
learning experience for learners.
 The pass rate is percentage of learners successfully completing the course over the total number of learners
enrolled for the course. The Pass Rate in this experiment has been collected on the completion of the course
and used to determine effectiveness of ARS framework.
 The group A learners were tutored under ARS Framework for student success. It included weekly/periodic
learner centric advising and differentiated instructions from instructors.
 The group B learners were provided standard instruction irrespective of learners‟ learning preference. In
other words, the group B was provided with standard instructions where all available learning objects were
presented in a default sequence independent of the learners‟ learning styles
 Group A had 26 learning observations (e-tutoring outcomes) impacting 411 learners whereas group B had
34 learning observations impacting 513 learners in all the four semesters
 E-learning observation (e-tutoring outcome) is the final pass rate of a particular section in a semester.
 The allocation of learners was done by the e-learning institute.
 The colleage mathematics1 course was selected for this experiment The duration of the course is 11 weeks
 The standard/uniform course content was made available for all learning observations
The pass rate completion data for Group A & B for all the semesters is shown below in Table 5
Table 5. Pass rate data for Group A & B
Dec-2013 Mar-2014 Jun-2014 Sep-2014
Group A Group B Group A Group B Group A Group B Group A Group B
73% 42% 69% 47% 75% 63% 89% 72%
93% 58% 53% 60% 75% 50% 83% 67%
65% 83% 93% 40% 83% 62% 82% 20%
38% 50% 50% 43% 100% 67% 88% 82%
63% 35% 79% 36% 54% 64% 81%
41% 58% 64% 50% 82%
67% 23% 71% 67% 73%
56% 50% 46% 59%
56% 57% 54% 41%
38% 67%
54%
4.2 Preliminary Analysis
The below mentioned relationships were studies to derive the first impression of the data in the
preliminary analysis phase.
 Minimum Pass Rate
 Maximum Pass Rate
 Mean Pass Rate
 Median Pass Rate
The group A&B data is illustrated below in Table 6 & 7 respectively.
Table 6. Group A data
Group A
Semester
Learning
observations Min Value Max Value
Mean
Value Median
Sep-14 5 64% 89% 80% 83%
June-14 5 36% 100% 74% 75%
March-14 7 50% 93% 68% 69%
Dec-13 9 38% 93% 61% 63%
(Dec13 to Sep14) 26 36% 100% 71% 70%

Table 7. Group B data
Group B
Semester
Learning
observations Min Value Max Value
Mean
Value Median
Sep-14 10 20% 82% 67% 69%
June-14 11 38% 67% 55% 54%
March-14 4 40% 60% 59% 45%
Dec-13 9 23% 83% 50% 50%
(Dec13 to Sep14) 34 20% 83% 58% 55%
4.2.1 Minimum Pass Rate
The minimum value is the lowest number in a data population. The minimum pass rate signifies the
lowest pass rate in a particular semester. The minimum pass rate of Group A & B is illustrated in Fig 3 below.
Fig 3. Minimum pass rate for group A & B
Inferences :
 The minimum pass rate for Group A is better than minimum pass rate for Group B for the semester Dec-13,
March-14 and Sep-14 by 38%, 20% and 69% respectively
 The minimum pass rate for Group B for the semester June-14 is 6% better than Group A
 The overall minimum pass rate for group A is 45% better than the group B
4.2.2 Maximum Pass Rate
The maximum value is the largest value in a data set. The maximum pass rate signifies the highest pass
percetnage of learners in a particular semester. The maximum pass rate of Group A & B is illustrated in Fig 4
below.
Fig 4. Maximum pass rate for group A & B

Inferences
 The maximum pass rate for Group A is better than maximum pass rate for Group B for the semester Dec-
13, March-14, June-14 and Sep-14 by 11%, 36%, 33% and 7% respectively
 The overall maximum pass rate for all the semesters for Group A is 17% better than the group B
4.2.3 Mean Pass Rate
The mean value is the sum of a collection of numbers divided by the count of numbers in the
collection. In this experiment, mean pass rate is the percentage of learners successfully completing the course
over the total number of learners enrolled in the course. The mean pass rate of Group A & B is illustrated in Fig
5 below.
Fig 5. Mean pass rate for group A & B
Inferences
 The mean pass rate for Group A is better than mean pass rate for Group B for the semester Dec-13, March-
14, June-14 and Sep-14 by 19%, 14%, 25% and 17% respectively
 The overall mean pass rate for Group A is 20% better than the group B
4.2.4 Median Pass Rate
The median is the numerical value separating the higher half of a data from the lower half. The median
pass rate of Group A & B is illustrated in Fig 6 below.
Fig 6. Median pass rate for group A & B

Inferences :
 The median pass rate for Group A is better than median pass rate for Group B for the semester Dec-13,
March-14, June-14 and Sep-14 by 20%, 35%, 18% and 17% respectively
 The overall median pass rate for Group A learners is 21% better than the group B learners
All relationship metrics and inferences indicate that group A learners have outperformed group B
learners in all the semesters. This indicates that teaching learning process is effective when accompanies with
ARS framework.
4.3 Analysis and variance validation
The preliminary inferences were further strengthened and validated by analyzing pass rate outcomes as
per Rubrics defined by the e-learning Institute and statistical analysis.
4.3.1 Pass rate outcome as per Rubrics
E-learning institute has provided Rubrics to all e-tutors to ensure grading is consistent and as per the
defined parameters. The learner are classified into Novice, Basic, Proficient & Advanced on the basis of their
Final Grade at the end of the semester. This is illustrated in Table 8 below.
Table 8. Learners pass rate classification as per Rubrics
Classification Final Grade
Novice 0-64%
Basic 65-74%
Proficient 75-84%
Advanced 85-100%
While the above mentioned Rubrics was prescribed by e-learning institute to classify learners into
Novice, Basic, Proficient & Advanced on the basis of their final grade at the end of the semester, author has
used it to classify learning observations into Novice, Basic, Proficient & Advanced on the basis of its pass rate.
The table 9 below illustrates all 60 learning observations for Group A & B.
Table 9. Classification of all learning observations as per defined Rubrics
Group
Novice Basic Proficient Advanced Total Learning
observations
0-64% 65-74% 75-84%
85-100%
Group A 9 6 6 5 26
Group B 24 6 4 0 34
The number of learning observations are different for group A&B. As a result, percentage of learners in
each category was calculated to derive inferences. The percentage classification of all learning observations is
illustrated in the Table 9 & Fig 7 below.
Table 10. Percentage classification of all learning observations as per defined Rubrics
Group
Novice Basic Proficient Advanced Total
Learning observations
0-64% 65-74% 75-84% 85-100%
Group A 27% 50% 60% 100% 43%
Group B 73% 50% 40% 0% 57%

Fig 7. Percentage classification of all learning observations for group A &B
Inferences
 100% of the Advanced learning observations belong to group A. No observation from group B has qualified
for the Advancec category.
 60% of the Proficient learning observations belong to group A whereas 40% belong to group B. Group A
has 50% additional Proficient learning observations as compared to Group B learners.
 Learning observations in the Basic category are 50% each in the group A&B.
 27% of the Novice obseverations belong to group A whereas 73% belong to group B. The group A has 63%
less learning observations in the Novice category as compared to group B
4.2.2 Statistical Analysis
In most comparative studies, the mean is the most important piece of data. But there are also times
when the variation is also studied. This is particularly useful in studying the stability of the processes. In
statistical literature, F-test is used to compare the variation of two data sets and T-test is used to compare the
means of two data sets. In this experiment, both the tests have been used to analyze variation and to determine
significance of the mean for group A & B.
4.2.2.1 F-Test
An F-test checks whether or not the two data sets have the same variation. It is also necessary to do an
F-test when comparing the means of two samples not coming from a paired experiment. The pass rate data
collected for this experiment from group A & B do not qualify for the paired experiment. As with all scientific
experiments, we started with formulating two hypothesis along with rules for rejecting the null hypothesis,
which are as follows:
 Null Hypothesis: The variation of the two samples coming from group A and group B is the same.
 Alternative Hypothesis: The variation of group A and group B is not equal; they have different standard
deviations. In statistics, standard deviation is the measurement of variation. In an F-test it compares the
standard deviation of two data sets instead of the mean (which is done in a T-test). Actually, what is being
analyzed is the “variance,” which is the square of standard deviation.
 Rules for rejecting the null hypothesis: If P value is less than 0.05, then the variance or the standard
deviations of the two sample sets are not equal. It is significantly different.
F-test calculation
`F-Test Two-Sample for Variances‟ analysis tool within the Data Analysis tab of MS Excel was used
for this calculation.
Table 11. F-Test Two-Sample for Variances
Group A Group B
Mean 0.695361 0.557204473
Variance 0.030274 0.024811593
Observations 26 34
df 25 33
F 1.220143
P(F<=f) one-tail 0.292763
F Critical one-tail 1.843577

The P-value is 0.293, which is greater than 0.05. Hence, we cannot reject the null hypothesis and
conclude that the variations of the two samples are the same. It means group A and group B pass rate have the
same variation. In this case comparison of means is to be done by using the “Two Sample Equal Variance” T-
test.
4.2.2.2 T-test
A T-test is used to compare two data sets. The below mentioned requirements have been compiled
before conducting T-test on the data collected for this experiment.
 The data is obtained using a random sampling method. The pass rate data for this experiment has been
collected for College Mathematics 1 course offered by an e-learning institute. . The Institute has randomly
allocated learning opportunities to e-tutors in all the semesters.
 The data is quantitative and has been collected for four semesters i.e. Dec-13, March-14, June-14, Sep-14
 The sample size is 20+ for better experimental accuracy. The pass rate data has got 60 learning observations
in both the groups.
As mentioned in the data collection and validation section, group A learners were tutored under
ARS Framework for student success. It included regular advising and differentiated instructions from
instructors whereas group B learners were provided standard instruction irrespective of learners‟ learning
preference. In other words, the group B was provided with standard instructions where all available learning
objects were presented in a default sequence independent of the learners‟ learning styles
 Null Hypothesis: There is no effect on the pass rate even if the ARS Framework is applied. In other words,
the means of the two groups are the same and the ARS Framework is not effective.
 Alternative Hypothesis: There is a significant difference in the mean. The mean of group A applying ARS
Framework is significantly better than group B.
 Rules for Rejecting the Null Hypothesis: If P value is less than 0.05 (95% confidence level of the results),
then the difference is significant; otherwise, accept the null hypothesis.
t-Test calculation:
`t-Test : Two-Sample Assuming Equal Variances‟ analysis tool within the Data Analysis tab of MS
Excel was used for this calculation. This is as per the F-test findings.
Table 12. t-Test: Two-Sample Assuming Equal Variances
Group A Group B
Mean 0.695361 0.557204473
Variance 0.030274 0.024811593
Observations 26 34
Pooled Variance 0.027166
Hypothesized Mean Difference 0
df 58
t Stat 3.217426
P(T<=t) one-tail 0.001059
t Critical one-tail 1.671553
P(T<=t) two-tail 0.002118
t Critical two-tail 2.001717
The P value is 0.002. Since it is less than 0.05, we will reject the null hypothesis and conclude that
mean pass rate for group A (applied with ARS framework) is significantly better than mean of group B
(standard instructions). In other words, it‟s established that e-tutoring ARS Framework improves the overall
learning experience by identifying the gap and adopting Learning to learn driven remedial interventions with the
specific cognitive requirements of the learner traditionally not catered to in a standard (conventional) e-learning
environment. This establishes the improvement in the student pass rate by early identification of learners at-risk
followed by timely, continuous and multi-tiered teaching-learning interventions.
The results demonstrate that the risk profiling and continuous pedagogic interventions of Adaptive
Remediation Solutions Framework help in creating an effective learning environment and thus significantly
enhances the average pass rate of learners.
.
V. Conclusion
Adaptive Remediation Solutions Framework In The Form Of Student Profiling & Regular Pedagogic
Interventions Is Observed To Improve The Overall Learning Experience Of Students. The Framework Supports
E-Learning Delivery And Assists To Improve The Student Pass Rate By Creating An Engaging And Conducive
Environment For Learners For Steady Progress Towards The Completion Of Course Activities. In Other Words,
It Helps In The Academic Integration Of Students Resulting In Higher Academic Performance. This Is

Especially Important For E-Learning Students Who Apparently Don‟t Get Sufficient Support From
Conventional Learning. The ARS Framework Assists In Meeting Specific Cognitive Requirements Of The
Learners, By Identifying The Gap And Adopting Learning To Learn Driven Remedial Intervention,
Traditionally Not Catered To In A Standard E-Learning Environment. The Effectiveness Of The Model Has
Been Analyzed And Validated By Its Application To 411 Learners Spread Across Four Semesters Of A Leading
E-Learning Institute. In This Experiment, Learners Have Been Classified Into Two Groups To Study And
Analyze The Impact Of ARS Framework Over Standard Instructions. The Group A Learners Have Been
Tutored Under ARS Framework For Student Success Whereas The Group B Learners Have Been Provided With
Standard Instruction Irrespective Of Their Learning Preference. The Preliminary Analysis Has Been Completed
By Studying And Comparing Maximum, Minimum, Mean And Median Pass Rate Of Both The Groups. All
Relationship Metrics And Inferences Have Indicated Group A Learner‟s Outperformance Over Group B
Learners. The Comparisons Made In This Study Have Been Tested For Statistical Significance At The .05 Level
Using The Student‟s T Statistic To Ensure That The Differences Are Larger Than Those That Might Be
Expected Because Of Sampling Variation. T-Test Has Further Strengthen The Preliminary Inferences And Has
Proved That Group A Average Pass Rate Is 20% Better Than The Group B Learners And This Is Not By
Chance Or Coincidence. Thus, Students Assisted With Adaptive Remediation Solutions Are More Likely To
Succeed Academically Than The Students Who Go Through Standard Instructions. Given The Known Positive
Impact, The Researchers Intend A Further Study Of The Impact Of Adaptive Remediation Solutions Framework
Along With Differentiated Instructions Is Required To Measure And Quantify Benefits Of The Adaptive
Remediation Solutions Framework For Student Success.
References
[1]. The rise of e-learning. (2009, Nov/Dec). Government of Western Australia, Department of Training and Workforce Development.
Article ID Training/07. Retrieved from http://dtwd.wa.gov.au/ employeesandstudents/ training/ otherinformation/ trainingmatters/
previousversions/Documents/November%20-%20December%202009/07_tm_web.pdf
[2]. Aud, S., Hussar, W., Kena, G., Bianco, K., Frohlich, L., Kemp, J., Tahan, K. (2011). The Condition of Education 2011 (NCES
2011-033). U.S. Department of Education, National Center for Education Statistics. Washington, DC: U.S. Government Printing
Office.
[3]. Davidovic, A.; Warren, J., Trichina, E. (2003). Learning benefits of structural example-based adaptive tutoring systems Published in
Education, IEEE Transactions on (Volume: 46 , Issue: 2)
[4]. Cohen, P.A., Kulik, J.A., and Kulik, C.L.C. (1982). Educational Outcomes of Tutoring: A Meta-analysis of findings. American
Educational Research Journal, 19, 237–248.
[5]. Wasik, B.A., and Slavin, R.E. (1993). Preventing Early Reading Failure With One-to-One Tutoring: A Review of Five Programs.
Reading Research Quarterly, 28, 179–200.
[6]. Invernizzi, M. (2002). The Complex World of One-on-One Tutoring. In S. Neuman and D. Dickinson (Eds.), Handbook of Early
Literacy Research. (pp.459–470). New York: Guildford Press.
[7]. Ramsden, P. (1992). Learning to teach in higher education. London, Routledge.
[8]. TechLearn (2000) Online Tutoring Briefing Paper. Retrieved from http://www.techlearn.org.uk/cgi-bin/techspec.pl?l=7
[9]. Snow, R, and Farr, M. (1977). Cognitive-conative-affective processes in aptitude, learning, and instruction: An introduction,
Conative and affective process analysis, 3.
[10]. Russell, T. (1977). Technology wars: Winners and losers, Educom Review, 32(2).
[11]. Brusilovsky, P. (2002). Developing adaptive educational hypermedia systems: from design models to authoring tools, in T. Murray,
S. Blessing and S. Ainsworth (Eds.) Authoring Tools for Advanced Technology Learning Environments: Toward Cost-Effective
Adaptive, Interactive, and Intelligent Educational Software, Ablex, Norwood
[12]. Paolucci, R. (1998). Hypermedia and learning: The relationship of cognitive style and knowledge structure, In the Proceedings of
ED-MEDIA/ED-TELECOM, Freiburg, Germany, June 20–25, 1998.
[13]. Whitehurst, R., Powell, C. and Izatt, J. (1998). Utilizing the Student Model in Distance Learning, In the Proceedings of the ITiCSE‟
98, ACM.
[14]. Ng, C. S. L. & Cheung, W. S. (2007). Comparing face to face, tutor led discussion and online discussion in the classroom.
Australasian Journal of Educational Technology, 23(4), 455-469. http://www.ascilite.org.au/ajet/ajet23/ng.html
[15]. King, A. (1993). From a sage on the stage to guide on the side. College Teaching, 41(1), 30-35
[16]. Collison, G., Elbaum, B., Haavind, S. & Tinker, R. (2000). Facilitating online learning: Effective strategies for moderators.
Madison, WI, USA: Atwood Publishing.
[17]. Cox, E. S., Clark, W. P., Heath, H. & Plumpton, B. (2000). Key facilitation skills for effective online discussion groups: Herding
cats through Piccadilly Circus. Proceedings from Distance Education: An open question, 11-13 September, Adelaide, SA. [verified
10 Jul 2010]. http://www.unisanet.unisa.edu.au/cccc/papers/refereed/paper11/paper11-1.htm
[18]. Prensky, M. (2002). E-Nough! Retrieved 2. 11. 2006 from: http://www.marcprensky.com/writing/Prensky%20-%20e-Nough%20-
%20OTH%2011-1%20March%202003.pdf
[19]. Bork, A. (2000). Teaching and learning are often mentioned together. Retrieved 2. 11. 2014 from:
http://www.educause.edu/ir/library/pdf/ERM0010.pdf
[20]. McPherson, M. & Nunes, M. B. (2004). The role of tutors as an integral part of online learning support. Retrieved 16. 4. 2015 from:
http://www.eurodl.org/materials/contrib/2004/Maggie_MsP.html
[21]. Teles, L., Ashton, S., Roberts, T. & Tzoneva, I. (2001). The role of the instructor in e-learning collaborative environment. Retrieved
20.04. 2015 from: http://www.Telestaininglobal.com/press/media/techknowlo.article.pdf
[22]. McLeish, J., 1968, The Lecture Method. Cambridge Institute of Education, Cambridge, England. National Research Council, 1999,
How People Learn: Bridging Research and Practice. National Academy Press, Washington D.C., 346 p.
[23]. Fink, L.D., 2003, Creating Significant learning Experiences: An Integrated Approach to Designing College Courses. Jossey-Bass
Publishers, San Francisco, CA, 295 p.
[24]. Prince, M., 2004, Does Active Learning Work? A Review of the Research. Journal of Engineering Education, v. 93, p. 223-231.

[25]. Vonderwell, S. (2004). Assessing online learning and teaching: Adapting the minute paper, TechTrends, 48(4), 29–31.
[26]. Dennen, V. P. (2005). From message posting to learning dialogues: Factors affecting learner participation in asynchronous
discussion. Distance Education, 26(1), 127-148.
[27]. Stipek, D. J. (1996). Motivation and instruction.In D.C. Berliner & R. C. Calfee (Eds.), The handbook of educational psychology
(pp. 85-113), New York:Macmillan
[28]. Peterson, C. (1990). Explanatory style in the classroom and on the laying field. In S. Graham & V. Folkes (Eds.), Attribution
theory: Applications to achievement, mental health, and interpersonal conflict (pp. 53-75). Mahwah, NJ:Erlbaum
[29]. Miller, S. (2001). Tutoring online to retain students and promote success. In Dalziel and M. Payne (Eds.), Student services: Vol. 2.
Quality enhancing practices in distance education. Washington, DC: Instructional Technology Council.
[30]. European Commission (2007) Commission staff working paper Progress towards the common objectives in Education and training
Indicators and benchmarks. Brussels: European Commission.
[31]. Fredriksson, U. and Hoskins, B. (2007). The development of learning to learn in a European context. Curriculum Journal Vol. 18,
No.2, pp. 127 - 134.
[32]. Barr, R.B., and Tagg, J., 1995, From Teaching to Learning: A New Paradigm for Undergraduate Education. Change, v. 27, p. 13-25.
[33]. National Research Council, 2000, How People Learn: Brain, Mind, Experience and School. National Academy Press, Washington
D.C., 374 p.
[34]. Savin-Baden M., and Major C.H., 2004, Foundations of Problem-Based Learning. Society for Research into Higher Education and
Open University Press, Berkshire, England, 197 p
[35]. Beers, S. (1988). Epistemological assumptions and college teaching: Interactions in the college classroom. Journal of Research and
Development in Education, 21 (4), 87-94.
[36]. Shulman, L. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15 (2), 4-14.
[37]. Grossman, P. L. (in press). Subject matter knowledge and the teaching of English. In J. Brophy (Ed.), Advances in research on
teaching Vol. 2. Greenwich, CT: JAI Press.
[38]. Scheffler, I. (1973). Reason and teaching. New York: Bobbs-Merrill.
[39]. Hong, H., and Kinshuk.(2004). Adaptation to student learning styles in web based educational systems, In L. Cantoni & C.
McLoughlin (Eds.), Proceedings of EDMEDIA 2004 - World Conference on Educational Multimedia, Hypermedia &
Telecommunications (Lugano, Switzerland), June 21-26.
[40]. Alonso, C. (1993). Educational technology and learning styles: Rethinking the Roles of Technology in Education, In the
Proceedings of the Tenth International Conference on Technology and Education, pp 1277-1279.
[41]. Keefe, J. (1979). Learning style: an overview”, In J.W. Keefe, editor, Student learning styles: diagnosing and prescribing programs.
NASSP, 1979.

Adaptive Remediation Solutions Design Framework and Implementation for Student Success

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to Adaptive Remediation Solutions Design Framework and Implementation for Student Success (20)

More from iosrjce (20)

Recently uploaded (20)

Adaptive Remediation Solutions Design Framework and Implementation for Student Success