=Paper=
{{Paper
|id=Vol-3294/short2
|storemode=property
|title=Combining IGA and KG for Serendipitous Learning Contents Recommendation
|pdfUrl=https://ceur-ws.org/Vol-3294/short2.pdf
|volume=Vol-3294
|authors=Emmanuel Ayedoun,Satoko Inoue,Hiroshi Takenouchi,Masataka Tokumaru
|dblpUrl=https://dblp.org/rec/conf/recsys/AyedounITT22
}}
==Combining IGA and KG for Serendipitous Learning Contents Recommendation==
https://ceur-ws.org/Vol-3294/short2.pdf
Combining IGA and KG for Serendipitous Learning Contents
Recommendation
Emmanuel Ayedoun 1, Satoko Inoue 2, Hiroshi Takenouchi 3, and Masataka Tokumaru 1
1
Faculty of Engineering Science, Kansai University, 3-3-35 Yamate-cho Suita, Osaka 564-8680 Japan
2
Graduate School of Engineering, Kansai University, 3-3-35 Yamate-cho Suita, Osaka 564-8680 Japan
3
Department of System Management, Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku,
Fukuoka, 811-0295 Japan
Abstract
Although there have been few attempts to propose serendipity-oriented recommender systems
in the field of education, such systems appear to lack of the essential ability to support learners’
agency, which is learners’ feeling of ownership and control over their own learning. In this
paper, we propose an Interactive Evolutionary Computation driven recommender system that
enables learners to take control and responsibility of their own learning while recommending
learning resources that are novel and unexpected, yet still relevant to learners’ interests. The
proposed system specifically employs Interactive Genetic Algorithm (IGA) and Knowledge
Graphs (KG) for dynamic recommendation of learning contents related to the history of
scientific discoveries. We conducted both numerical simulations that confirmed the
effectiveness of the learning contents optimization algorithm and an experimental evaluation
which hinted at the meaningfulness of the proposed approach towards inducing serendipity
within learners.
Keywords 1
Recommender Systems, Serendipitous Learning, Interactive Evolutionary Computation
1. Introduction overgeneralization of recommended information
can impair the ability of learning support systems
to provide learners with content that is interesting,
The deployment of recommender systems in
novel and more importantly unexpected [5]. As a
the field of technology enhanced education has
result, such approaches can lead to an overly
attracted increased interest as a promising means
narrow set of suggestions lacking in serendipity
to help learners navigate through suitable learning
and inadvertently placing the learner in what is
resources, given the plethora of available digital
known as a “filter bubble”, according to Pardos
learning resources nowadays [1]. The principal
[6]. That is, proposing recommender systems that
and commonly used techniques to build
also aim at helping learners make serendipitous
recommender systems are collaborative filtering
knowledge acquisition is necessary to tackle the
[2] and content-based filtering [3]. However, in an
filter bubble issue.
educational context, both approaches present
The term “Serendipitous learning” has been
some shortcomings: risk of overgeneralization for
used to refer to learning through gaining new
collaborative recommenders and risk of
insights, discovering interesting aspects and
overspecialization as far as content-based
recognizing new relations, which occurs by
recommenders are concerned. Such issue has been
chance or as by-product of other activities [7], [8].
framed as the “serendipity-problem” [4], to
Serendipitous learning emphasizes the positive
denote that the overspecialization or
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CEUR Workshop Proceedings (CEUR-WS.org)
role of unexpected realization of hidden, informal learning, which depends to a large extent
seemingly unrelated connections or analogies for on individual preferences or choices and is often
learning and research [8], [9]. Although there self-directed [19], could be greatly enhanced by
have been few attempts to propose serendipity- introducing in such learning environments
oriented recommender systems in the field of serendipity-oriented recommender systems. As
education [10], [11], [12], such systems do not evoked in the previous section, it should also be
necessarily support learners’ agency, which is yet noted that most recommender systems dedicated
an essential requirement, as serendipitous to learning support embed recommendation
encounters also owe to the open-minded attitude techniques that could inadvertently place learners
of the seekers, their curiosity, and their into “filter bubbles”, a type of swim-laning of
perspicacity [13]. learners into a particular track based on a machine
Interactive Evolutionary Computation (IEC) is learned stereotype [8]. Meanwhile, it has been
a generic term which refers to a group of suggested that serendipitous experiences are
optimization techniques or algorithms that uses valuable to learning at a personal level [10].
subjective human evaluation instead of a Therefore, to the extent of fostering learners’
numerical fitness function to solve optimization engagement in informal learning settings, the goal
problems when the fitness function cannot be and major contribution of this study is to propose
assumed or appropriately represented in the form a serendipity-oriented recommender system
of a mathematical function [14]. Given such which fulfill the following requirements:
characteristics, IEC techniques have been • Target support of learning in an informal
successfully applied in many fields, such as face learning environment
identification [15], fashion design [16], music • Facilitate learner’s agency by actively
composition [17], hearing aid fitting [18]. In a supporting self-directed learning through
typical scenario of IEC, a small number of exploratory interaction with the learning
solutions (e.g., a population of ten solutions) are environment
shown to a human user who is supposed to assign • Embed a resource recommendation
one of a pre-specified set of ranks (e.g., 1: very algorithm that involves learners in the
bad, 2: bad, 3: average, 4: good, 5: very good) to system recommendation refining process
each solution in the population. by actively gathering their preferences
In this paper, we propose an Interactive
Evolutionary Computation (IEC) driven
recommender system that enables learners to take 2.2. Approach
control and responsibility of their own learning
while exploring learning resources that are novel 2.2.1. Overview of proposed system
and unexpected, yet still relevant to their interests.
The proposed system specifically employs Figure 1 shows an overview of the proposed
Interactive Genetic Algorithm (IGA) and system. In the system, the learning contents are
Knowledge Graphs (KG) for dynamic generation represented in the form of “learning paths”
of learning contents. covering related concepts and presented to
learners via a dedicated interface, shown in Figure
2. Research Goal and Approach
2.1. Problem Statement and
Research Goal
In the domain of technology enhanced
learning, a number of recommender systems have
been proposed. Yet, a closer look to the current
status of their development and evaluation reveals
that such efforts present some limitations. For
instance, available systems seem to target learning
in formal settings, do not sufficiently support Figure 1: Schematic representation of
learners’ agency and evaluate effectiveness only
interactions between the learner and the
from the standpoint of learners’ grade. However,
system
interesting to the user by leveraging IGA, and
generates new paths based on those features. If the
generated path exists in the path database, it is
presented to the user as-is, and if it does not exist,
it is replaced by the most similar path in the path
database and presented to the user (Phase 2). By
repeating the evaluation of the proposed paths, the
system attempts to learn the learners’ taste and
interests, and presents them with novel paths of
greater interest, yet unexpected enough to achieve
Figure 2: System Interface showing the paths recommendation of learning contents that could
navigation window induce serendipity.
2. For instance, the knowledge database used for
the study presented in this paper is a database in 2.2.2. Knowledge graph model
which learning contents (i.e., scientific
discoveries and inventions) are related to each In general, a knowledge graph G= {E, R, F} is
other and such relationships can be quantitatively a collection of entities E, R, and facts F [22]. A
expressed. To this extent, we built the learning fact is a triple (ℎ, 𝑟, 𝑡) ∈ F that denotes a link 𝑟 ∈
contents database of the system using the contents R between the head ℎ ∈E and the tail 𝑡 ∈ E of the
of the book “Science: The Definitive Visual triple. In our proposed system, the relationship
Guide, Adam Hart-Davis (Ed.)” [21]. It is a between nodes and edges is also represented using
comprehensive book which tells the history of the common (ℎ, 𝑟, 𝑡) triples. Note that ℎ and 𝑡
science and technology from the earliest times to represent two different nodes in the knowledge
the present day in chronological order by graph, while 𝑟 represents an edge linking these
capturing every key moment of discovery, and nodes. In the following lines, we provide an
showing how the concepts, the inventions, and the overview of how we define these triples in the
individuals behind them have changed our world. context of this study.
More interestingly, the book illustrates how one First of all, we expressed ℎ as a collection of
discovery is connected to another by presenting the three parameters vectors ℋelement, ℋBefore and
some pointers to events that preceded and ℋAfter.
followed a current discovery or invention. Such ℋelement
structure obviously holds the potential to make it ℎ = [ ℋBefore ] (1)
easier for the reader to realize how scientific ℋAfter
discoveries and inventions in a wide range of ℋelement represents the main contents of a page,
scientific fields are interrelated to each other. In and is expressed as in equation (2), where ℎpage is
the resulting knowledge graph, each piece of the page number of the node, ℎdiscipline is the
information (i.e., major discovery or invention) is discipline (i.e., scientific field), and ℎera is the era
represented by a node, and the relationship
of the node contents.
between related nodes is depicted by an edge. In 𝑒1 ℎpage
other terms, each node holds the contents of each ℋelement = [𝑒2] = [ℎdiscipline] (2)
page of the book, while an edge expresses the 𝑒3 ℎera
relationship between two related pages.
Therefore, what is called “learning path” in the ℋBefore represents the related pages labeled as
context of this study is a collection of nodes and page BEFORE (=B) in the book, which refer to
edges extracted from a knowledge graph. the related pages older than the current page.
Generation and optimization of learning path to be ℋBefore is defined as in equation (3) according to
presented to learners at a given time of the the number of older related pages NB, and each
interaction are achieved by the means of an BEFORE page 𝑏i.
interactive genetic algorithm (IGA), a kind of IEC 𝑏1
algorithm. 𝑏2
In the proposed system, users are first asked to ⋮
explore the knowledge graph and select paths of ℋBefore = 𝑏 = 𝑏 (1 i NB) (3)
i
interest which they evaluate ((Phase 1). Then, the ⋮
system learns the features of the paths that are [𝑏𝑁𝐵 ]
ℋAfter is defined similarly to ℋBefore and quantitatively express the degree of relevance or
represents the related pages labeled as page divergence between two nodes (i.e., learning
AFTER (=A) in the book, as shown in (4). Note contents).
that NA stands for the number of related pages
coming after the current page 𝑎j. 2.2.3. Learning path optimization
𝑎1 algorithm
𝑎2
⋮ Path optimization here refers to the generation of
ℋAfter = 𝑎 = 𝑎j (1 i NA) (4)
new paths of interest to the user by the system. Let
⋮ N be the number of paths generated from the
[𝑎𝑁𝐴 ] knowledge graph G described in the previous
section, and pathk (k ∈ N ), a path arbitrarily
Next, 𝑡 which also represents a content node retrieved from the path database. In this study, each
similarly to ℎ above is defined as follows. Let 𝑡page pathk has a fixed length and is composed of four
denote the page number, 𝑡discipline denote the nodes h1, h2, h3, h4 (h1, h2, h3, h4 ∈ ℎ) and three
discipline, and 𝑡era the era. t is expressed as in (5). edges 𝑟1, 𝑟2, and 𝑟3 (𝑟1, 𝑟2, 𝑟3 ∈ 𝑟).
𝑡1 𝑡page Considering that the edges 𝑟1, 𝑟2, 𝑟3 are
𝑡 = [𝑡2] = [𝑡discipline] (5) defined as in (6), 𝑟𝑝𝑎𝑡ℎ𝑘 which is the vector
𝑡3 𝑡era
Finally, 𝑟 consists of the association of the representing the whole path (i.e., 𝑝𝑎𝑡ℎ𝑘 ) is
following three vectors 𝒳element, 𝒳Before, and 𝒳After, expressed as the sum of 𝑟1, 𝑟2, and 𝑟3 as follows:
as shown in equation (6). 𝑟𝑝𝑎𝑡ℎ𝑘 = [𝑟1, 𝑟2, 𝑟3] (10)
𝒳element In the present study, the process of path
𝑟 = [ 𝒳Before ] (6) optimization using IGA is based the gene
𝒳After information expressed by 𝑟𝑝𝑎𝑡ℎ𝑘 . To such extent,
𝒳element expresses the relation between the the learner first rates some paths presented to him
main contents of node ℎ and the main content of by the system in terms of relevance with their
node 𝑡 in terms of difference between discipline
interests. Here, it seems important to bear in mind
and era parameters, as shown in (7).
|ℎ𝑑𝑖𝑠𝑐𝑖𝑝𝑙𝑖𝑛𝑒 − 𝑡𝑑𝑖𝑠𝑐𝑖𝑝𝑙𝑖𝑛𝑒 | that learners are not prompted to evaluate each
𝒳element = [ ] (7) edge or node, but the whole path with a focus on
|ℎ𝑒𝑟𝑎 − 𝑡𝑒𝑟𝑎 |
the connection between starting nodes and ending
𝒳Before is defined as the difference between
nodes. The intention here, is to make the system
node ℎ and 𝑡 in terms of three parameters: pages
number, discipline, and era, as shown in equation capture how interesting the learner finds the
(8). Note that here 𝒳𝐵𝑖 = 0 if 𝑡 = 𝑏𝑖 (i ∈ NB). connection between several related events across
|𝑏𝑖1 − 𝑡1 | various scientific disciplines and eras. Based on
the obtained evaluation values, the path is
𝒳Before = 𝒳Bi = [ 𝑖2 − 𝑡2 |]
|𝑏 (8)
|𝑏𝑖3 − 𝑡3 | optimized by genetic algorithm processing, and
Similarly, 𝒳After is defined as the difference the next-generation solution candidate (i.e.,
between node ℎ and 𝑡 in terms of three learning path) is presented to the learner. The path
parameters: pages number, discipline, and, and is optimized by repeating this process for a certain
era, as shown in equations (9). Here as well, number of generations. Note that here, the path
𝒳𝐴𝑗 =0 if 𝑡 = 𝑎𝑗 (j ∈ NA). optimization differs from usual implementation of
|𝑎𝑗1 − 𝑡1 | IGA as it requires an additional process that we
call Path retrieval. When generating the next
𝒳After = 𝒳Aj = [|𝑎𝑗2 − 𝑡2 |] (9)
generation of solutions, in most cases, Crossover
|𝑎𝑗3 − 𝑡3 |
or Mutation will cause the generation of candidate
Based on the proposed knowledge graph solutions (i.e., paths) that do not exist in the path
model, our key idea is to let an edge 𝑟 capture database RDB. Therefore, for example, a non-
differences in terms of discipline, era and page
existent path rpathA needs to be “replaced” by an
number between two given nodes, ℎ and 𝑡.
existing path rpathB with the constraint that both
Besides, by expressing era and page number as
time series parameters and adopting a similarity paths are similar enough (i.e., rpathA rpathB). To the
scale for the discipline parameter, we aim to extent of calculating the degree of similarity
between two paths, we adopted the Dynamic Time contents (i.e., path) presented at the last
Warping (DTW) algorithm [23], which is a well- generation was relatively high (M= 3.9, SD:0.92).
known technique to find an optimal alignment Next, DTW values, which indicate similarity
between two given (time-dependent) sequences degrees between the generated path by the
under certain restrictions. The DTW distance algorithm and the one retrieved from the database,
𝐷(𝑝𝑎𝑡ℎ𝑘 , 𝑝𝑎𝑡ℎ𝑙 ) which indicates the degree of tend to converge to a value near 0 around the last
similarity of two different paths 𝑝𝑎𝑡ℎ𝑘 and generation. This suggests that the proposed
𝑝𝑎𝑡ℎ𝑙 is recursively calculated using the system was able to generate paths that are close to
paths which exist in the database. This is a good
following equations:
indication that the proposed DTW-based path
𝐷(𝑝𝑎𝑡ℎ𝑘 , 𝑝𝑎𝑡ℎ𝑙 )
similarity calculation method performed well.
= 𝛿(𝑟𝑝𝑎𝑡ℎ𝑘 , 𝑟𝑝𝑎𝑡ℎ𝑙 ) However, when analyzing the transition of
𝐷(𝑝𝑎𝑡ℎ𝑘 , 𝑝𝑎𝑡ℎ𝑙−1 ) DTW values for some subjects, there were cases
+ min { 𝐷(𝑝𝑎𝑡ℎ𝑘−1 , 𝑝𝑎𝑡ℎ𝑙 ) (11) in which DTW values rose rapidly even near the
𝐷(𝑝𝑎𝑡ℎ𝑘−1 , 𝑝𝑎𝑡ℎ𝑙−1 ) last generation or did not show a decreasing trend
despite the number of generations increased, such
where 𝛿(𝑟𝑝𝑎𝑡ℎ𝑘 , 𝑟𝑝𝑎𝑡ℎ𝑙 ) denotes the distance as in the case of Subject B (Figure 4). Therefore,
we cannot rule the hypothesis that using a method
between respective edges of 𝑝𝑎𝑡ℎ𝑘 and 𝑝𝑎𝑡ℎ𝑙 other than DTW distance as a method for
calculated as: calculating path similarity may lead to higher
𝛿(𝑟𝑝𝑎𝑡ℎ𝑘 , 𝑟𝑝𝑎𝑡ℎ𝑙 ) = |𝑟𝑝𝑎𝑡ℎ𝑘 − 𝑟𝑝𝑎𝑡ℎ𝑙 | (12) performance for path optimization.
From the results of the questionnaire survey,
3. Pilot Evaluation we note that the proposed system was able to
present interesting and surprising learning
contents to two out of three subjects. Moreover,
We conducted an experimental pilot
two subjects also declared that they were able to
evaluation to investigate whether the proposed
experience serendipity through their interaction
system could present information of interest but
with the system. Such results seem to suggest the
yet unexpected enough to the extent to induce
meaningfulness of the proposed approach.
serendipity within participants. The subjects were
3 university students majoring in science-related
fields. Subjects were asked to visit and then
evaluate the paths proposed by the system in terms
of preference level on the scale of 0 to 5. Based on
their ratings, the system generated new paths and
the same operation was repeated until the ending
condition (i.e., 10 generation rounds) was met. At
the end of the interactions, we administrated a
questionnaire survey, to collect participants’
subjective opinions on the meaningfulness of their
interaction with the system.
Figure 3~5 show the transition of the DTW
values and evaluation scores of the most highly
rated paths by each of the three participants
(Subjects A~C) between the first and last
generation rounds.
First, from these results, it can be noted that the
proposed system was able to optimize the paths
according to each user since the highest
evaluation scores from participants seem to
stabilize around the last generations. In other
terms, the system was able to gradually present
subjects with learning contents that were highly Figure 3: Evaluation scores of the best paths and
rated. The average evaluation score of the learning corresponding DTW values (Top to Bottom Subject
A, B and C).
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