A Cognitive Knowledge-based Framework
for Adaptive Feedback
Andrew Thomas Bimba1(&), Norisma Idris1, Rohana Binti Mahmud1,
and Ahmed Al-Hunaiyyan2
1
Faculty of Computer Science and Information Technology,
University of Malaya, 50603 Kuala Lumpur, Malaysia
[email protected],
{norisma,rohanamahmud}@um.edu.my
2
Computer and Information Systems Department,
Public Authority for Applied Education and Training, Kuwait City, Kuwait
[email protected]
Abstract. Adaptive learning environments provide personalization of the
instructional process based on different parameters such as: sequence and difficulty of task, type and time of feedback, learning pace and others. One of the
key feature in learning support is the personalization of feedback. Adaptive
feedback support within a learning environment is useful because most learners
have different personal characteristics such as prior knowledge, learning progress and learning preferences. In a computer-based learning environment,
feedback is considered as one of the most effective factors which influence
learning. Although, there are various tools that provide adaptive feedback in
learning environments, some problems still exist. One of the problems we are
looking into is How to design effective tutoring feedback strategies? We propose
a cognitive knowledge based framework for adaptive feedback, which combines
the three facets of knowledge (pedagogical, domain and learner model) in a
learning environment, using concept algebra.
1 Introduction
Adaptive learning environments provide personalization of the instructional process
based on different parameters such as: sequence and difficulty of task, type and time of
feedback, learning pace and others [1, 2]. It is a more personalized, data-driven and
technology-enabled approach to learning, which has a potential to improve student
retention, measure student learning and improve pedagogy [3]. An adaptive learning
system alters its behavior based on how a learner interacts with it. These alterations are
decided based on the learner’s characteristics which are represented in the learner
model [4]. It involves the accurate tracking of learner’s activity, monitoring their
individual characteristics and providing timely adaptive feedback according to effective
pedagogical principles [5]. In a computer-based learning environment, feedback is
considered as one of the most effective factors which influence learning [6–8].
Tailoring feedback according to learner’s characteristics and other external parameters
is a promising way to implement adaptation in computer-based learning environment
© Springer International Publishing AG 2017
S. Phon-Amnuaisuk et al. (eds.), Computational Intelligence in Information Systems,
Advances in Intelligent Systems and Computing 532, DOI 10.1007/978-3-319-48517-1_22
246
A.T. Bimba et al.
[5] Adaptive feedback unlike generic feedback is dynamic, as learners work through
instructions different learners will receive different information [8]. A computer-based
learning environment represents knowledge in form of models. The three key models in
a computer-based learning environment are the pedagogical model, domain model and
learner model. For a computer-based learning system to provide appropriate feedback
to a student, it has to have a knowledge of effective approaches to teaching (pedagogical model), the subject been learned (domain model) and the learner (learner
model) [8]. Knowledge base modeling and representation techniques such as expert
knowledge base and ontologies have been used in representing pedagogical, domain
and learner knowledge [9, 10]. However, ontologies are most frequently used in
modeling pedagogical, domain and learner knowledge because, it could afford a consistent structure through a means of describing and processing concepts, terms and the
relationship between them [11].
Feedback is frequently provided in a typical classroom setting, however most of the
information is poorly received, because feedback is presented to groups and so often
students do not believe such feedback is relevant to them [7]. Currently, the gap
between students who excel the most and those who excel less is a challenge that
teachers, school administrators, and government officials face frequently [12].
Although, there is an increase in the development of adaptive feedback-based learning
environments [6, 13, 14], some problems still remain unresolved. One of the questions
faced is How to design effective tutoring feedback strategies? [5]. Several researchers
have recommended frameworks with methods for implementing adaptive feedback [15,
16], however, these methods are not fully grounded in thorough educational theories
and their empirical evaluation with students has been lacking. The design, implementation and evaluation of adaptive feedback strategies is quite challenging, due to
various variables such as learning strategies and style, prior knowledge, motivational
state and cognitive skills, which can hinder or facilitate the effectiveness of feedback in
learning. A background on adaptive feedback in learning environments and knowledge
modeling will be discussed in Sect. 2. In order to provide effective feedback, we
propose a cognitive knowledge-based approach, which will be discussed in Sect. 2.2.
A mechanism to evaluate our proposed technique will be briefly introduced in Sect. 3
and we will conclude in Sect. 4.
2 Background
The two main approaches to teaching and learning are the deductive and inductive
techniques [17]. Traditionally, using the deductive approach, Science and Engineering
are taught by introducing a topic on general principles; then using the principles, models
or equations are derived; afterwards, an illustration of the application of the models are
provided; assignments with similar derivations are given for practice and finally student’s
abilities are tested in exams [17]. There is little attention on what real-world phenomena
these models explain. No explanation on the practical problems they can solve and why
the students should care about it. A preferable substitute is the inductive approach. Instead
of starting with the general principles, the lesson commences with real-world occurrences
such as a case study to analyze, experimental data to interpret, or a complex real-world
A Cognitive Knowledge-based Framework for Adaptive Feedback
247
problem to solve [17]. As the students try to solve the problem, they create a need for
rules, procedures, facts and guiding principles, only at this point are they provided with
the needed information or guided to discover it. However, in practice, teaching is not
purely inductive or deductive. Learning involves moving in both directions, where student infer rule and principles from new observations and test the principles through
deducing consequences and applications that are verifiable experimentally. The inductive
approach is learner-centered, enabling students to take responsibility of their own
learning. Instructional methods which adopt the inductive approach include problembased learning, inquiry learning, project-based learning, case-based teaching, discovery
learning and just-in-time teaching [17]. These methods are in-line with the constructivist
idea that knowledge is constructed and re-constructed in an individual’s brain, rather than
simply absorbing what is given by the teacher [17–20].
In a computer-based learning environment the pedagogical model represents the
knowledge and expertise of teaching. Specific knowledge represented in the pedagogical model includes, effective teaching techniques (deductive and inductive); the
various instructional methods (lectures, problem-based learning, inquiry learning etc.);
Instructional plans that define phases, roles and sequence of activities [21]; feedback
types, depending on a learner’s action; assessment to inform and measure learning [12].
The domain model represents knowledge of the subject been learned. It mainly consists
of concepts such as how to add, subtract, multiply numbers; newton’s law of motion;
how to structure an argument; different approaches to reading etc. [12]. The size of the
domain knowledge which represents concepts can differ between computer-based
systems, according to the domain size, application area and the choice of the designer
[22]. In complex settings, concepts can have relationships between each other, resulting
in a conceptual network representing the domain’s knowledge. Common relationships
used by most systems include prerequisite (where the learner has to know the first
concept before studying the next related concept), is-a (where a concept is an instance
of another concept) and part-of (where a concept is part of another concept) [22, 23].
The learner model represents the student’s knowledge of the domain, individual
characteristics and personal interactions with the computer [12, 22].
The learner model contains information about the user’s current knowledge of the
domain; user characteristics such as preferences, learning style, cognitive style etc. and;
interactions with the system such as student’s current activity, previous achievements
and difficulties, concepts mastered, emotional state, time spent on task, kinds of errors
made, misconceptions, response to feedback etc. [12, 22, 24]. This information can then
be used by the domain and pedagogical models to assess the learner and decide the next
most appropriate interaction [12]. Various knowledge modelling techniques have been
used to model domain knowledge. Cognitive Tutors [10] used production-rules, while
Ontologies have been used by Munoz-Merino (2011) [25]. A semantic network is used
by Web PVT to represent relationships among concepts of the domain knowledge [23].
Most computer-based learning environments do not clearly state the knowledge base
modeling technique used in the pedagogical, domain and learner models. In the next
sections we will discuss the characteristics of adaptation and feedback, the types of
adaptive feedbacks, previous works on adaptive feedback based on adaptive characteristics, knowledge modeling in a cognitive knowledge base and how it can represent
pedagogy, domain and learner models to support adaptive feedback.
248
2.1
A.T. Bimba et al.
Approaches to Adaptive Feedback in Learning Environments
Brusilovsky (1996) defined systems that model student’s learning style, prior knowledge, goals and preference as adaptive [26]. According to Chieu (2005), there are five
main adaptation techniques which are related to the key components of constructive
learning environment [27]. He highlighted them as follows:
• Adaptive presentation of learning contents. The course designer should define
which learning contents are appropriate to a specific learner at any given time, for
example simpler situations and examples for a novice learner than for an expert one.
• Adaptive use of pedagogical devices. The course designer should define which
learning activities are appropriate to a specific learner, for instance simpler tasks to a
novice learner than to an expert.
• Adaptive communication support. The course designer should identify which peers
are appropriate to help a specific learner, for example learners with more-advanced
mental models help learners with less-advanced ones.
• Adaptive assessment. The course designer should identify which assessment
problems and methods are appropriate to determine the actual performance of a
specific learner, for instance simpler tests for a novice learner than for an expert.
• Adaptive problem-solving support. The tutor should give appropriate feedback
during the problem-solving process of a specific learner, for example to show the
learner his or her own difficulties and provide him or her with the way to overcome
those difficulties.
These adaptation techniques rely on a learner model; an essential component
which, among other student relevant data, keeps data about the student’s knowledge of
the subject domain under study.
Adaptive learning involves multiple disciplines such as Educational Psychology,
Cognitive Science and Artificial Intelligence. This complexity prompted the structuring
of research on adaptation along the methodological questions distinguishing means,
target, goal and strategy [28].
• Adaptation Means: what information about the learner such as knowledge level,
cognitive style, learning style, gender, student’s current activity, previous
achievements and difficulties, misconception is known and used for adaptation?
• Adaptation Target: what aspect of the instructional system (pedagogy and domain
model) is adapted based on the learner model?
• Adaptation Goal: what are the pedagogical reasons for the system to adapt to the
learner model? Is the system aiding inductive or deductive learning; is the system
adapting to a specific instructional method based on the learner model?
• Adaptation Strategy: what are the steps and techniques used to adapt the system to
the learner model, and how active or reactive are the learners and system to the
adaptation process?
In a learning environment, feedback is seen as the teacher’s (artificial or real)
response to the student’s action. There are four main characteristics of feedback:
function, timing, schedule and type [29]. Although, other researchers [15] suggested
other characteristics of feedback, we adhere to the characterization by Carter (1984)
A Cognitive Knowledge-based Framework for Adaptive Feedback
249
Table 1. Characteristics of Feedback.
Characteristics of
feedback
Function
Timing
Scheduling
Type
Explanation
Feedback can be provided in relation to the instructional goals and
objectives. For example, feedback is provided based on, cognitive
functions such as promoting information processing, motivational
functions such as developing and sustaining persistence or provide
correct response
Feedback can be given with respect to timing. It could be in advance,
appearing before an action; it could be immediate, appearing
immediately after an action or delayed, appearing at a longer time after
the action has been made. The feedback is intended to advise, notify,
recommend, alert, inform or motivate the learner about some concerns
Feedback can also be made available at scheduled instances. For
example, when the learner exceeds a certain time threshold, expertise
level, after solving certain questions or after every subtopic
There are various feedback types resulting from function, timing and
scheduling. For example, verification feedback, avoidance feedback,
correction feedback, informative feedback, cognitive feedback,
emotional feedback, scheduled feedback, dynamic feedback,
immediate feedback, advanced feedback, delayed feedback,
comparative feedback, isolation feedback etc.
because it encompasses all other characterizations [29]. These characteristics are briefly
explained in Table 1.
For developing an effective adaptive feedback framework, the characteristics of
adaptation and feedback have to be taken into consideration. The pedagogy, domain
and learner models have to be represented in a knowledge base where effective
algorithms can be used to implement effective feedback strategies. The next section
discusses a promising knowledge representation technique for modeling knowledge in
an adaptive learning environment.
2.2
Knowledge Modeling in Cognitive Knowledge Base
Conventional technologies for knowledge base modeling and manipulation such as
linguistic knowledge base, expert knowledge base and ontology are man-made rather
than machine built. The absence of thorough and sufficient operations on acquired
knowledge, inflexible to learn knowledge synergy, and weak trans-formability among
different knowledge bases gave rise to a novel approach, the cognitive knowledge base
(CKB) [30]. Based on the previous studies in cognitive science and neurophysiology
[31, 32], the foundations of human knowledge in the long-term memory can be represented by an object-attribute-relation model based on the synaptic structure of
human memory, which represents the hierarchical and dynamic neural clusters of
knowledge retained in memory as well as the logical model of knowledge bases [33].
250
A.T. Bimba et al.
The cognitive knowledge base is a structure that manipulates knowledge as a dynamic
concept network like the human knowledge processing [34, 35]. In CKB a concept is a
cognitive unit which identifies and models real-world concrete entities and a
perceived-world (abstract entity) [30]. The basic unit of knowledge in a CKB is a
formal concept represented as an OAR model according to concept algebra [36]. While
complex knowledge such as a theme are represented as multiple associate concepts,
which forms a partial dynamic concept network (DCN).
The CKB structure as shown in Fig. 1 consist of the logical model, physical model,
linguistic knowledge base and knowledge manipulation engine. The logical model of
knowledge bases shared by humans and cognitive systems is known as the
object-attribute-relation model [33]. The logical structure is modeled as a hierarchical
network of concepts and themes. The logical knowledge base represents knowledge as
Cartesian products of formal concepts. The physical knowledge base implements the
memory structures of knowledge as a DCN. The linguistic knowledge base comprises
of the initial words as modeled in WordNet and the representation of these words and
their relation in form of the OAR model.
Fig. 1. Structure of a cognitive knowledge base [33]
The cognitive processes of concept memorization and knowledge fusion, similar to
human and cognitive systems learning processes, are used for knowledge acquisition in
CKB. Concept memorization involves acquiring concepts based on the formal structure
model as shown in Fig. 2.
A Cognitive Knowledge-based Framework for Adaptive Feedback
251
Fig. 2. Structure of a formal concept [33].
Where:
- set of attributes that denotes the intention of the concept.
- set of objects that instantiated the extension of the concept.
- set of internal relations between objects and attributes.
- set of input relations between the concept and other concepts.
- set of output relations between the concept and other concepts denotes the
intention of the concept.
The newly acquired item is retained as a formal concept based on the structure in
Fig. 2. The concept memorization phase consists of two algorithms, the EnterConcept
process model and the ConceptMatch process model. The former checks the condition
of the knowledge base if available it indexes the current concept and its time
stamp. The latter implements a content-addressed search mechanism to match a set of
existing concepts in the knowledge base to the new concept.
The use of a cognitive knowledge base that models pedagogy, domain and learner
knowledge and their connections can be used as a base for generating adaptive feedback in computer-based learning environment. This research focuses on designing such
feedback framework. The next section discusses our proposed framework for adaptive
feedback.
3 Proposed Framework for Adaptive Feedback
The relationships between pedagogical (knowledge and expertise of teaching), domain
(Knowledge of subject taught) and learner (learner’s knowledge, characteristics and
interactions) models are quit complex. However, in other to provide effective feedback
which is compliant with the characteristics of adaptation, a link has to be established
between these three models. The cognitive-PDL model proposed in Fig. 3 shows the
connections of these model using a cognitive knowledge base. The decision to use a
CKB arises from the fact that concepts can be represented with both internal (relationship between concepts and attributes in a specific model) and output (relationships
between concepts and attributes across models) relationships.
What distinguish our approach is the specification of the relationships between
pedagogy, domain and learner knowledge. In practice, it means that instead of looking
252
A.T. Bimba et al.
Fig. 3. Structure of a cognitive knowledge based pedagogical, domain and learner model
at all these components in isolation, we look at each concept as a 5-tuple. By definition
each concept in a sub-model is represented as shown in Eq. 1.
Where:
– O is a nonempty set of an instance of the concept,
where
denotes a power set of wU
– A is a nonempty set of attributes,
– Rc O A is a set of internal relations.
– Ri C 0 C is a set of input relations, where C 0 is a set of external concepts.
– Ro C0 C is a set of output relations [33].
Knowledge acquisition in the Cognitive-PDL model will be through concept
memorization and knowledge fusion. An algorithm is required to index a current
concept to the Cognitive-PDL model after verifying its availability. Another algorithm
searches the knowledge base to establish relationship between the new concept and a
set of existing concepts in order to incorporate the new concept as acquired knowledge.
A third algorithm decides the appropriate feedback to be provided based on the learners
current state and pedagogical principles.
A Cognitive Knowledge-based Framework for Adaptive Feedback
253
4 Conclusion
The need for adaptive feedback in learning environment cannot be over-emphasized.
Learners have different personal characteristics such as prior knowledge, learning
progress and learning preferences. Providing feedback based of an individual’s characteristics is key to bridging the gap between students who excel most and those who
excel less. Although, there are various tools that provide adaptive feedback in learning
environments, some problems still exist. One of the problems we are looking into is
How to design effective tutoring feedback strategies?. Most researchers have proposed
methods that are not fully grounded in pedagogical principles. Also, the three main
knowledge components of a learning environment (pedagogical, domain and learner
model) are mostly treated in isolation. We propose a cognitive knowledge based
framework for adaptive feedback, which combines the three facets of knowledge in a
learning environment, using concept algebra. The decision to use a CKB arises from
the fact that concepts can be represented with both internal (relationship between
concepts and attributes in a specific model) and output (relationships between concepts
and attributes across models) relationships.
Acknowledgment. This work was supported by the University of Malaya Research Grant
[RP040B-15AET, 2015].
References
1. Stoyanov, S., Kirchner, P.: Expert concept mapping method for defining the characteristics
of adaptive e-learning: ALFANET project case. Educ. Technol. Res. Dev. 52, 41–54 (2004)
2. Brusilovsky, P.: Adaptive and intelligent technologies for web-based eduction. Ki 13, 19–25
(1999)
3. Advisors, E.G.: Learning to adapt: a case for accelerating adaptive learning in higher
education. In: Education Growth Advisors, March 2013
4. Lo, J.-J., Chan, Y.-C., Yeh, S.-W.: Designing an adaptive web-based learning system based
on students’ cognitive styles identified online. Comput. Educ. 58, 209–222 (2012)
5. Narciss, S., Sosnovsky, S., Schnaubert, L., Andrès, E., Eichelmann, A., Goguadze, G., et al.:
Exploring feedback and student characteristics relevant for personalizing feedback strategies.
Comput. Educ. 71, 56–76 (2014)
6. Thurlings, M., Vermeulen, M., Bastiaens, T., Stijnen, S.: Understanding feedback: a learning
theory perspective. Educ. Res. Rev. 9, 1–15 (2013)
7. Hattie, J., Gan, M.: Instruction based on feedback. In: Mayer, R., Alexander, P. (eds.)
Handbook of Research on Learning and Instruction, pp. 249–271. Routledge, New York
(2011)
8. Le, N.-T.: A classification of adaptive feedback in educational systems for programming.
Systems 4, 22 (2016)
9. Zhihong, T., Liu, W., Libing, L., Zeqing, Y.: The application of ontology model in
intelligent tutoring system. In: Proceedings - International Conference on Computer Science
and Software Engineering, CSSE 2008, pp. 1176–1179 (2008)
10. Anderson, J.R., Corbett, A.T., Koedinger, K.R., Pelletier, R.: Cognitive tutors: lessons
learned. J. Learn. Sci. 4, 167–207 (1995)
254
A.T. Bimba et al.
11. Bimba, A.T., Idris, N., Al-Hunaiyyan, A., Mahmud, R.B., Abdelaziz, A., Khan, S., et al.:
Towards knowledge modeling and manipulation technologies: a survey. Int. J. Inf. Manag.
36, 857–871 (2016)
12. Luckin, R., Holmes, W.: Intelligence unleashed: an argument for AI in education (2016)
13. van der Kleij, F.M., Eggen, T.J., Timmers, C.F., Veldkamp, B.P.: Effects of feedback in a
computer-based assessment for learning. Comput. Educ. 58, 263–272 (2012)
14. Narciss, S.: Designing and evaluating tutoring feedback strategies for digital learning.
Digital Educ. Rev. 23, 7–26 (2013)
15. Economides, A.A.: Adaptive feedback characteristics in CAT. Int. J. Instr. Technol. Distance
Learn. 3, 15–54 (2006)
16. Ana, G.S., Macario, D.-S.J.: Designing feedback to support language acquisition using the
InGenio authoring tool. Procedia Soc. Behav. Sci. 1, 1239–1243 (2009)
17. Prince, M.J., Felder, R.M.: Inductive teaching and learning methods: definitions, comparisons, and research bases. J. Eng. Educ. 95, 123–138 (2006)
18. Driscoll, M.P.: Psychology of Learning for Instruction. Allyn & Bacon, Boston (2005)
19. Gillani, B.B.: Learning Theories and the Design of e-Learning Environments. University
Press of America, Lanham (2003)
20. Yilmaz, K.: The cognitive perspective on learning: its theoretical underpinnings and
implications for classroom practices. Clearing House J. Educ. Strat. Issues Ideas 84, 204–
212 (2011)
21. Scheuer, O., Loll, F., Pinkwart, N., McLaren, B.M.: Computer-supported argumentation: a
review of the state of the art. Int. J. Comput. Support. Collaborative Learn. 5, 43–102 (2010)
22. Kazanidis, I., Satratzemi, M.: Adaptivity in a SCORM compliant adaptive educational
hypermedia system. In: Leung, H., Li, F., Lau, R., Li, Q. (eds.) ICWL 2007. LNCS, vol.
4823, pp. 196–206. Springer, Heidelberg (2008)
23. Virvou, M., Tsiriga, V.: Web passive voice tutor: an intelligent computer assisted language
learning system over the WWW. In: Proceedings - IEEE International Conference on
Advanced Learning Technologies, ICALT 2001, pp. 131–134 (2001)
24. Carmona, C., Conejo, R.: A learner model in a distributed environment. In: De Bra, P.M.,
Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 353–359. Springer, Heidelberg (2004)
25. Muñoz-Merino, P.J., Pardo, A., Scheffel, M., Niemann, K., Wolpers, M., Leony, D., et al.:
An ontological framework for adaptive feedback to support students while programming. In:
International Semantic Web Conference (2011)
26. Brusilovsky, P.: Methods and techniques of adaptive hypermedia. User Model. User-Adap.
Inter. 6, 87–129 (1996)
27. Chieu, V.M.: Constructivist learning: an operational approach for designing adaptive
learning environments supporting cognitive flexibility. Universiteit Twente (2005)
28. Specht, M.: Adaptive Methoden in computerbasierten Lehr/Lernsystemen.
GMD-Forschungszentrum Informationstechnik (1998)
29. Carter, J.: Instructional learner feedback: a literature review with implications for software
development. Comput. Teach. 12, 53–55 (1984)
30. Wang, Y.: Concept algebra: a denotational mathematics for formal knowledge representation
and cognitive machine learning. J. Adv. Math. Appl. 4, 1–26 (2015)
31. Hampton, J.A.: Psychological representation of concepts. In: Conway, M.A. (ed.) Cognitive
Models of Memory, pp. 81–110. MIT Press, Cambridge (1997)
32. Leone, N., Pfeifer, G., Faber, W., Eiter, T., Gottlob, G., Perri, S., et al.: The DLV system for
knowledge representation and reasoning. ACM Trans. Comput. Logic (TOCL) 7, 499–562
(2006)
33. Wang, Y.: On a novel cognitive knowledge base (CKB) for cognitive robots and machine
learning. Int. J. Softw. Sci. Comput. Intell. (IJSSCI) 6, 41–62 (2014)
A Cognitive Knowledge-based Framework for Adaptive Feedback
255
34. Wang, Y., Tian, Y., Hu, K.: The operational semantics of concept algebra for cognitive
computing and machine learning. In: 2011 10th IEEE International Conference on Cognitive
Informatics & Cognitive Computing (ICCI*CC), pp. 49–58 (2011)
35. Wang, Y.: On concept algebra: a denotational mathematical structure for knowledge and
software modeling. Int. J. Cogn. Inf. Nat. Intell. (IJCINI) 2, 1–19 (2008)
36. Valipour, M., Yingxu, W.: Formal properties and rules of concept algebra. In: 2015 IEEE
14th International Conference on Cognitive Informatics & Cognitive Computing
(ICCI*CC), pp. 128–135 (2015)