Conference PaperPDF Available

The Use of Alexa for Mass Education

Authors:

Abstract

The amount of digitalized information increases day by day. Everyone connected to the internet through different devices or interfaces can acquire information of specific topics and interests at every time on every place. Nowadays, nearly every child owns a mobile device or at least has access to a mobile device connected to the internet. The main focus of children is to watch videos or play games on their device, so they always see it as an interesting, entertaining virtual friend. The use of mobile devices containing apps which deliver selected information opens a very interesting channel to deliver knowledge in a hidden way to children. This research deals with the development of an Alexa skill prototype to train 1x1 calculation skills of children in primary school in a very funny and playfully way. To evaluate the speech-assistant technology connected to an education game, a field study in was realized. As the Alexa speech-assistant is anyway absolutely magnetic to children, the feedback of the 1x1 calculation skill was better than expected and motivated us to enhance the base functionalities.
Draft originally published in: Schoegler, P., Ebner, M. & Ebner, M. (2020). The Use of Alexa for Mass Education. In
Proceedings of EdMedia + Innovate Learning (pp. 721-730). Online, The Netherlands: Association for the Advancement of
Computing in Education (AACE). Retrieved July 13, 2020 from https://www.learntechlib.org/primary/p/217374/.
The Use of Alexa for Mass Education
Peter Schoegler
Educational Technology, Graz University of Technology, Austria
peter.schoegler@student.tugraz.at
Markus Ebner
Educational Technology, Graz University of Technology, Austria
markus.ebner@tugraz.at
Martin Ebner
Educational Technology, Graz University of Technology, Austria
martin.ebner@tugraz.at
Abstract: The amount of digitalized information increases day by day. Everyone connected to the
internet through different devices or interfaces can acquire information of specific topics and
interests at every time on every place. Nowadays, nearly every child owns a mobile device or at
least has access to a mobile device connected to the internet. The main focus of children is to watch
videos or play games on their device, so they always see it as an interesting, entertaining virtual
friend. The use of mobile devices containing apps which deliver selected information opens a very
interesting channel to deliver knowledge in a hidden way to children. This research deals with the
development of an Alexa skill prototype to train 1x1 calculation skills of children in primary school
in a very funny and playfully way. To evaluate the speech-assistant technology connected to an
education game, a field study in was realized. As the Alexa speech-assistant is anyway absolutely
magnetic to children, the feedback of the 1x1 calculation skill was better than expected and
motivated us to enhance the base functionalities.
Introduction
Prior to the digital era, information was not accessible by most people (Wikramanayake, August 2005).
Further (Wikramanayake, August 2005) describes in his publication, that even those accessed were unable to obtain
current information as we are able today. While observing information only through traditional sources like school,
teachers or libraries, there was also a strong dependency and limitation in education. The technology advantages
have opened up many avenues of learning as information is accessible from anywhere by everyone
(Wikramanayake, August 2005).
Computer games, the internet, and other new communication medias are often seen to pose threats and
dangers to young people (David Buckingham, 2013). Adding, that they also provide new opportunities for creativity
and self determination. It’s in the parent’s responsibility to monitor their children’s interaction on the internet. So
called internet parental styles must be applied (M.Valcke, September 2010).
Applications in form of digital games provide a perfect environment by making use of the information on
the internet and deliver data in a controlled way to the user.
In recent years, electronic games have assumed an important place in the life of children and adolescents. Children
acquire digital literacy informally, through play, and neither schools nor other educational institutions take sufficient
account of this important aspect (Gros, Feb 2014). Further (Gros, Feb 2014) explains, that multimedia design for
training and education should combine the most powerful features of interactive multimedia design with the most
effective principles of technologically mediated learning.
This paper describes the implementation of an Amazon Alexa Skill for children in primary school with the
aim, to increase the 1x1 calculation skills of a child by playfully integrate calculation questions in an interactive
story game. Evaluated by two field tests, the following research questions were tackled and processed:
Draft originally published in: Schoegler, P., Ebner, M. & Ebner, M. (2020). The Use of Alexa for Mass Education. In
Proceedings of EdMedia + Innovate Learning (pp. 721-730). Online, The Netherlands: Association for the Advancement of
Computing in Education (AACE). Retrieved July 13, 2020 from https://www.learntechlib.org/primary/p/217374/.
- Is the Alexa speech-assistant technology suitable for conversations with children?
- Are children able to follow calculation tasks given by Alexa?
- Which difficulties are children facing when interacting with Alexa?
- Are there similar difficulties for children answering some specific questions?
- Is there a reliable way to permanently store specific data via the Alexa Skill?
- Is it possible to build a simple authentication flow with the Alexa Skill via the API of the TU Graz 1x1
Trainer?
To answer those questions properly, we developed an Alexa Skill prototype at Graz University of
Technology (TU Graz) and installed the application on an Amazon Echo. We evaluated the Application in a live test
at the Maker Days at TU Graz. After integrating the positive feedback, we planned an additional field test in a
primary school in Austria.
Connected Topics
Learning Styles
According to (Annette Vincent, 2001) a learning style indicates a students or child’s preferred method of
learning. It guides the development of instructional strategies that integrates the appropriate content and context. In
some respects, learning styles function as teaching blueprints.
In general, there are three different main learning styles which are defined as the followings (Gilakjani,
2012):
1) Visual learning type
Those types of learners need some kind of visualization of the problem. They think in pictures and visual
images. To foster their understanding and increase their ability to gather information, non-verbal cues like
body language are very helpful. Visual learners prefer sitting in front of the classroom (Gilakjani, 2012).
Strategies for teaching visual learners are (Annette Vincent, 2001):
- Using video equipment
- Providing assignments in writing
- Using charts and pictures
2) Auditory learning type
Auditory learning types learn best from listening, talk things through or discussions with other
persons. By listening to tone of voice, speed and other nuances, they interpret the underlying
meaning of the heard speech. These persons may have less understanding of some written text
(LdPride, n.d.).
Strategies for teaching auditory learners are (Annette Vincent, 2001):
- Record class notes and learn by listening to them
- Remember details by trying to remember hearing previous discussions again
- Always try to participate in class discussions
- Ask questions to start conversations
- Read assignments out loud and answer correct question by saying it loud
3) Kinesthetic learning type
These are the typical persons who cannot sit still for long periods of time and get unfocused after some
time. They have to explore everything on their own and learn best through a hands-on approach (LdPride,
n.d.).
Draft originally published in: Schoegler, P., Ebner, M. & Ebner, M. (2020). The Use of Alexa for Mass Education. In
Proceedings of EdMedia + Innovate Learning (pp. 721-730). Online, The Netherlands: Association for the Advancement of
Computing in Education (AACE). Retrieved July 13, 2020 from https://www.learntechlib.org/primary/p/217374/.
Strategies for teaching kinesthetic learners are (Annette Vincent, 2001):
- provide many hands-on activities to allow students to participate and explore
- enable physical movement within the classroom
- provide action triggered stories
- encourage children to summarize their daily experiences in their notes
But teaching a student or child by applying exactly his or her preferred learning style doesn’t automatically
lead to the most valuable result. Daniel T. Willingham, professor of cognitive psychology at the University of
Virginia says: “What cognitive science has taught us is that children do differ in their abilities with different
modalities, but teaching the child in his best modality doesn't affect his educational achievement.” (Willingham,
2005). He further explains: “What does matter is whether the child is taught in the content's best modality. All
students learn more when content drives the choice of modality.” (Willingham, 2005).
So, the best learning results could be achieved by combining the most suitable modality for the processed topic
combined with the recommended teaching techniques for all learning types.
Learning Mathematics
Children nowadays grow up in a technology focused everyday live, which is based on mathematical
concepts. Computers, mobile phones and other assistance devices guide their childhood. It’s crucial in child’s
education process to evolve a healthy and easy understanding for numbers and their combinations and interactions.
(Jeremy Kilpatrick, 2001) states the mathematical proficiency in five strands:
- conceptual understanding understanding the mathematical concept and their operations und
relations
- procedural fluency ability or skill to project procedures in different problem formulations
- strategic competence formulate, represent and solve problems
- adaptive reasoning skill to generate logical links
- productive disposition see mathematics as important, useful and efficient
(Jeremy Kilpatrick, 2001) also adds, that the most important observation of those five strands is the
interdependency of each other. These strands are the fundamental pillars of how children learn to solve
mathematical problems and gives direction in how teachers have to integrate those in their process.
The first interaction with numbers often starts in very early stages in childhood when parents encourage
their children to count their sweets to share it equally with their siblings. Or for example counting all the stairs when
going to bed from the first to the second floor. Connecting simple games with mathematical problems which have a
link to everyday questions is always the first approach to explain mathematic to a child. This often results in a basic
understanding for whole numbers and the way to sum or multiply them in a very fundamental (Easy Ways to Add
Math to Everyday Routines: A Home-School Connection Post, 2015).
(Neil Mercer, 2008) explains in their publication how to use language to support mathematical
understanding and solve problems. The study describes the benefit of discussing problems with a partner or within a
group and use language as a tool to work on mathematical problems by their “thinking together” approach. The
study draws out the importance of getting different perspectives of a problem while getting input from other
members in the group. Furthermore, it outlines the importance of a teacher’s role on how to explain problems and
how to closely guide students or children on the problem-solving way.
Digital Game Based Learning
(Hilda K. Kabali, 2015) study “Exposure and Use of Mobile Media Devices by Young Children” in 2015
outlines the children’s ownership of media platform like tablets or smartphones in the age from 1- 4 years. The
result of the study shows, that more than 80% of children by the age of 4 make daily use of mobile devices to watch
Draft originally published in: Schoegler, P., Ebner, M. & Ebner, M. (2020). The Use of Alexa for Mass Education. In
Proceedings of EdMedia + Innovate Learning (pp. 721-730). Online, The Netherlands: Association for the Advancement of
Computing in Education (AACE). Retrieved July 13, 2020 from https://www.learntechlib.org/primary/p/217374/.
videos, play games or use apps (Hilda K. Kabali, 2015). This also means that children are surrounded by mobile
devices and games since they can think and it has become an integrated part in their social and cultural environment
(Oblinger, 2004). With the attraction of video games and the motivation of increasing levels and solve given
problems, also the motivation for traditional curricular contents decreases, so (Prensky, 2003). He describes the
approach of Digital Game Based Learning (DGBL) with his conclusion: “It therefore makes a great deal of sense to
try to merge the content of learning and the motivation of games…” (Prensky, 2003).
The attributes of games provide a very powerful and effective learning environment to people. (Oblinger,
2004) describes the following attributes in his publication “The next generation of educational engagement”:
- games include elements of urgency and complexity
- they support the trial-and-error principle and motivate with scoring points
- support active learning, experimental learning and problem-based learning
- games are learner-centered and provide immediate feedback
- adopt to the level of the player
- games allow players make use of learnings from previous quests and apply those to a novel
one
- games can be played with other users and provide a platform for knowledge exchange
- games (at least mobile games) can be played everywhere at every time
The aspect of DGBL is more present than ever. There are more and more publications discussing the power
of digital games in terms of education and their impact on young children. (Eck, 2006) calls them “Net Generation”
or “digital natives” in (Eck, 2006). He also says, that those children get disengaged by traditional instructions. They
would assume multiple information streams and require quick interaction with content paired with demanding visual
effects. A very important indicator is the increasing popularity of games. The video game revenue increased by 18%
from 2017 to 2018 as the total revenue tops $43 billion in 2018 referring to (Shieber, 2019).
Smart Speakers and Alexa Skills
Beside laptops, smartphones and tablets, a new area of human assistance was enabled by providing speech
recognition systems like Amazon’s Alexa. A study from Deloitte Deutschland reveals, that 13% of all homes in
Germany are using a smart speaker in 2018 whereas the number of installed devices increases since 2015 (Deloitte,
2018). The concept is quite simple, so when you ask an Alexa enabled device like the Amazon Echo “How is the
weather going to be tomorrow?” the device is recording the voice and sends it to the Amazon Alexa Voice Service
(AVS) to be analyzed. The Voice Service then applies complex operations such as Automatic Speech Recognition
(ASR) and Natural Language Understanding (NLU). AVS processes the response and uses external sources to fetch
the latest information about the asked question (Krishnan, 2018).
Alexa’s Apps are called Skills. Amazon provides a Skill KIT for developers to build custom apps for Alexa enabled
devices. This opens new possibilities of fetching data on demand by just asking a simple question to a device. The
functional scope of an Alexa Skill is not limited in answering just only one question, it provides space for
completely customizable logic and decision trees combined with an internal session storage.
This allows developers to make use of a new interface to implement interactive, session-based learning games. After
publishing Skills with a developer account they can be downloaded by Amazon enabled device via the Amazon
Store if there are no explicit restrictions set.
There are already approaches of math training Skills for children in the Amazon Skills Store which are trying to help
children to improve their skills in basic mathematical operations with whole integers. The Skill is kind of virtual
teacher for a child asking some math questions and helping them to solve it.
Intelligent 1x1 Trainer
The TU Graz started in 2011 the project of an intelligent math trainer “Intelligenter 1x1 Trainer” focused
on multiplications of numbers from 1 to 10. The aim of the trainer is to support children in primary school in
Draft originally published in: Schoegler, P., Ebner, M. & Ebner, M. (2020). The Use of Alexa for Mass Education. In
Proceedings of EdMedia + Innovate Learning (pp. 721-730). Online, The Netherlands: Association for the Advancement of
Computing in Education (AACE). Retrieved July 13, 2020 from https://www.learntechlib.org/primary/p/217374/.
learning and manifesting the small one-off by individually align the speed of the learning progress to the child. An
intelligent algorithm validates the answers given by the child and remembers weaknesses of specific calculation
tasks. The system ranks the child according to the given answers and tries to improve its skills by asking previously
wrong answered questions in defined intervals. The goal of this procedure is to motivate the child by starting with
easy questions and only increase the level of complexity after the calculation tasks of the current level have been
manifested in the child’s mathematical skill (Ebner, 2011).
By now, a couple of web and mobile learning apps for children make use of this trainer. All those apps are collected
on https://schule.learninglab.tugraz.at/math and can be used by primary schools for free.
Research Design
As already mentioned, there is an existing platform showing currently available learning apps developed by
TU Graz. To prove the concept, the reliability of the technology, the fun-factor for children and most important the
learning effect for each child, field tests with groups of children are indispensable. The base to enable and conduct a
meaningful field test is the reliability of the technology of the Speaker. Smart Speakers are quite sensible when
listening to speech in noisy areas. Especially in groups of children, someone is always talking and generates
disturbing background noise for the device. To validate the impact on the speaker, the field tests took place in noisy
areas with a large amount of children surrounded. The second part was to validate the children’s behavior while
interacting with the implemented decision tree of the Alexa Skill.
In the beginning of the first field test, a group of children surrounded a table with an Amazon Echo on it. They were
briefly introduced on how the game is structured and how they start the game. To challenge the sensibility of the
Speaker and the error handling of the Skill, always two children were asked to play the game together by helping
each other giving the correct answers. After finishing the game, another team of two children were invited to start
the Skill and play the math game. While playing the game, we monitored the following points precisely:
- are there problems to start the Skill?
- how does ambient noise influence the Amazon Echo?
- are there problems following the game?
- How motivated are children to play the game and answer all questions?
- Are the similar problems occurring in the teams of children?
To document the field tests photos and videos have been taken. After the first field test, we tried to get some
feedback of each child. Children in this age respond with very short und unprecise answers on detailed feedback
questions or are often too shy to give a proper answer. For this reason, we tried to focus on a more visual focused
feedback sheet. We prepared one sheet for each child which shows 4 emojis for each feedback question. It was quite
easy for the children to share their impressions by marking one of the provided emojis as an answer to our question.
After discussing the feedback and analyzing positives and difficulties of the behavior of the technology in
combination with the Skill Model, we implemented and enhanced version of the Skill. The goal of the enhanced
version was to focus on the learning curve of the user by integrating the user authentication of the API of the 1x1
Trainer of TU Graz in the Alexa Skill. This allows to train the user according to the level expertise.
Draft originally published in: Schoegler, P., Ebner, M. & Ebner, M. (2020). The Use of Alexa for Mass Education. In
Proceedings of EdMedia + Innovate Learning (pp. 721-730). Online, The Netherlands: Association for the Advancement of
Computing in Education (AACE). Retrieved July 13, 2020 from https://www.learntechlib.org/primary/p/217374/.
Alexa Skill Prototype
The Amazon Alexa Skill named “Bake a cake 1x1” aims to train a child’s small one-off skills. It’s available
for all Alexa enabled devices. The Skill can be downloaded and enabled via the Alexa App connected to an Alexa
enabled device like the Amazon Echo.
System Architecture
Figure 1. Overview of the base System Architecture
Figure 1 describes the basic System Architecture and gives an overview of all interacting components when
talking to the Amazon Echo and getting some response. The Amazon Web Service (AWS) provides all the
necessary functionalities and takes care about the hosting. The following steps briefly describe the flow model.
Amazon Echo
The Skill has to be installed on the Amazon Echo with the connected Amazon Account and the Amazon
App. After installing the skill, the endpoint to send the data to is known to the Amazon Echo. To start the Alexa
Skill, a specified “Skill Invocation Name” followed by the word “Alexa” triggers the launch of the installed
Application. The invocation name has to be set in the Alexa developer console.
Alexa Skills Kit
A so-called Interaction model has to be added in the Alexa developer console. The skill interface in the
Alexa Skills Kit parses the input speech and maps it according to the defined interaction model. After finding a
match, an event will be sent to the specified endpoint the AWS Lambda Function.
Draft originally published in: Schoegler, P., Ebner, M. & Ebner, M. (2020). The Use of Alexa for Mass Education. In
Proceedings of EdMedia + Innovate Learning (pp. 721-730). Online, The Netherlands: Association for the Advancement of
Computing in Education (AACE). Retrieved July 13, 2020 from https://www.learntechlib.org/primary/p/217374/.
AWS Lambda Function
The lambda function implements some defined event listeners to handle the parsed speech of the user
accordingly. Despite the skill service implementation, the lambda function establishes a connection to the TU Graz
e-learning Server via the SOAP API. In this way, questions will be fetched and answered will be sent and evaluated.
DynamoDB
The linked database enables the persistent storage of user data or. After the first field test, we enhanced the
system by this DB to enable user authentication with the TU Graz e-learning Server and provide a mechanism to
link the Amazon user of the Amazon Echo to a registered user in the e-learning system.
Game Flow
We built the Alexa Skill in two steps to integrate the feedback of the first version into the second one.
There is a group mode and a personal mode to select at the beginning. To keep it simple, we split up the prototype
into two version. The basic and the extended Version are launched by saying the words: “Alexa, starte
Mathekuchen!”. With the invocation text the Skill starts the game flow and prepares the data accordingly.
Basic Version
The basic Version was concepted to focus on the Speaker technology by influencing it with noise
disturbances of the environment. The Skills launches and introduces the user to the math game with an introduction
text which says: “Los geht’s, rechnen wir gemeinsam einen Kuchen aus. Ich lese das Rezept vor und du hilfst mir
beim Ausrechnen der Mengen der Zutaten. [Ok, let’s bake a cake together. I will read through the recipe and you
need you to help me calculating the ingredients]”. We know that children love cake and integrating the cake baking
thought as a learning game in our Skill.
Alexa directly starts to ask the first question with the words: “Als erstes brauchen wir <firstNumber> mal
<secondNumber> Gramm Butter. Wie viel ist <firstNumber> mal <secondNumber>? [First we need <firstNumber>
times <secondNumber> gram of butter. What gives < firstNumber > times <secondNumber >?]”. For the answer
text, there are also some excepted phrases configured in the previously described Skill interface. The Skill then tries
to evaluate the answer and gives positive feedback to the user to motivate for the next questions. Alexa also tells
what it understood as the answer of the user by repeating it. If the result is incorrect, Alexa also repeats the given
answer and asks the question in a motivating way again to give the user a second chance to think about it. If the
given answer could not be matched against the Skill Interface Model, Alexa kindly asks to repeat the given answer
with the feedback, that it could not be recognized in a human way.
The core of the learning System is of course the integration of the TU Graz 1x1 Trainer. The basic version
establishes a connection with the Server by authenticating with a default user. All the questions are fetched from the
1x1 Trainer. All the correct and wrong answers are also sent back to the Server to store the question and answers for
further learning processes. During a gaming session, the algorithm of the 1x1 trainer tries to figure out math
weaknesses. If questions are answered wrong, the same question will appear again in one of the next questions asked
by Alexa to train those iteratively. For the base version, the authenticated default user of the 1x1 trainer is not
directly associated to exact a specific child or player. This also influences the parameters for the algorithm as there
are many players having different strengths and weaknesses.
Extended Version
For the extended Version we tried to find a simple and feasible way to map players with users in the e-
learning system. The aim is to use the full power of the learning algorithm by authenticating with exactly one user
for the Alexa Skill. Therefore, a user has to be registered initially on the TU Graz e-learning platform. The SOAP
Interface provides some user management routes to fetch a user’s id by the given username. By extending our Alexa
Interface scheme we provide the functionality to just simply spell the username and Alexa converts it to the full
word. All the questions and answers can then be fetched and posted with the associated user by just adding the
Draft originally published in: Schoegler, P., Ebner, M. & Ebner, M. (2020). The Use of Alexa for Mass Education. In
Proceedings of EdMedia + Innovate Learning (pp. 721-730). Online, The Netherlands: Association for the Advancement of
Computing in Education (AACE). Retrieved July 13, 2020 from https://www.learntechlib.org/primary/p/217374/.
user’s id. This mechanism empowers us to use the learning algorithm for exactly one child and increase the learning
curve in the best way.
Additionally, to the authentication process, we tried to improve the user onboarding progress. While the first
approach always needed the username spelling before, we could map the user, we came up with a second approach
to store some mapping. The idea is to store amazon user id which can be accessed from the skill in combination with
the responded user id from the 1x1 Trainer. Therefore, we introduced the Amazon DynamoDB. We don’t want to
store any sensible user data and keep the prototype GDPR aligned. By just storing ids according to the user mapping
of two completely different systems in the DynamoDB, no additional values are needed.
So, for next invocation of the Skill, our logic tries to find the mapping in the connected DynamoDB and continues
the training according to the previously question, answer history.
Field Studies
As we built our prototype in two versions, we tried to conduct the first field test in a very early stage of the
development process. The first field test took place at TU Graz in the course of the so-called Maker days were
children are able to get some insights into interesting and for children relevant topics. Beside some others, we had a
station with a table, some chairs around it and the magnificent Amazon Echo in the middle of it. Children were able
to attend the exercises on each station. To avoid chaos, groups of 4 to 6 children were assigned to the stations.
We prepared our setup by connecting the Amazon Echo to the internet and tried to get a not too noisy location as it
was quite loud anyways. For our test groups we setup our Skill to authenticate with the default user and setup 7
calculation questions. We prepared some introduction for all the attendees on what this is about, how it works and
how to play the game. Mostly every child already knew or at least heard about the Amazon Alexa, but the
excitement while talking to it was always very as you could see it in the face of each child. Most of the children
already knew how to invoke an intent and which built-in functionality exist, so there were no difficulties to handle
the Amazon Echo in the first steps.
We managed to play the game with 12 children in the age of 6 to 13. All in all, 92 questions were answered
as some children wanted to start the game again after it was finished. Only 4 questions were answered incorrect and
have been corrected with the second try. To parse the answers, we prepared some different options in our Skill
Interface to enable more phrases for giving an answer. But we didn’t need any other phrase than the exact result as
the children always answered with just one word the result number.
The feedback of the children was great and we encountered some points to enhance and focus on for the next field
test. The first thing we observed was, that if it’s too noisy, the Skill is just not working as it understands some wrong
words while waiting for the answer and even stops the application in some cases. We cannot handle this behavior at
all.
There have been three main outcomes of the first field test. The first thing was, that German words, especially the
sound of the number 9 in German sounds like the word “no” which led the Skill to quit the application as “no” is a
general escape word for the skill if not handled. The second thing was the invocation name for starting the Skill. We
setup the Skill Interface to listen to a too long sequence for words for the invocation name which often resulted in
wrong understanding. The third thing we observed was the long latency when fetching data and evaluating it with
the SOPA API of the TU Graz Server. We tried to analyze the day and especially the three main problems in detail
and worked out some solutions for the second field test.
The second field study took place in a primary school in Austria. Therefore, we reimplemented our flow of
API calls to decrease the use of API calls and optimized the network latency time. Furthermore, we tried to handle
the wrong understanding of the number 9 in German language by handling the word “no” in the Skill Interface. To
start the game, we also reduced the length of the invocation name to just 2 words to avoid misunderstandings of long
phrases.
For the field test in school, we setup our device in a classroom to avoid disturbing surrounding sounds. We
conducted the field test with 12 children in the age of 9 to 10. Not more than 4 children are in the classroom at the
same time. We introduced the game to the children and allowed them, to play the game together with one partner to
help each other.
Draft originally published in: Schoegler, P., Ebner, M. & Ebner, M. (2020). The Use of Alexa for Mass Education. In
Proceedings of EdMedia + Innovate Learning (pp. 721-730). Online, The Netherlands: Association for the Advancement of
Computing in Education (AACE). Retrieved July 13, 2020 from https://www.learntechlib.org/primary/p/217374/.
The children answered about 122 questions. They all asked for restarting the game after the first try and were super
excited. The interaction with the API was way fast and Alexa understood all the answers perfectly as there was less
noise in the background. The Speaker always allowed some whispering between each team to discuss the answer.
The feedback of the children reflected our impressions at the field test. All of the children answered our
feedback sheet with the happiest smiley for all our feedback questions. For some of the children, the small one-off
was not very challenging. Others had to struggle especially with high numbers and needed the help of their friends.
We noticed, that the skill level of children who are in same age and in the same school class is quite different.
Conclusion
In this research we analyzed the opportunities and also the technical limits of currently available smart
speakers in combination with the Amazon Alexa Skill Kit. Always in our minds, how to open new ways of game-
based learning while making use of speech assistance techniques. As nowadays nearly every eighth household uses
some kind of a smart speaker, it would be fantastic to always use it as a learning friend for the children living is this
household. The feedback of the realized field studies was very positive and lead us to focus on the technology and
the possibilities in detail. Especially with the easy connection and authentication with the TU Graz 1x1 Trainer, we
provide a mighty learning engine connected to a simple and attractive user interface the Amazon Alexa Skill. As
the launch of the Skill and the learning game is done with just three words at home in the living room, children can
improve their Skills whenever they want in very small sessions. The algorithm in the background validates the
answers and trains the child in a very individual way. With the story of baking a cake and the short duration of a
game children stay by calculating the amount of ingredients and finish the game. We strongly recommend the use a
game-based learning in combination with interesting new technologies. By increasing the use of the TU Graz 1x1
Trainer the algorithm will be trained and improved. Common mistakes or weaknesses will be analyzed and
integrated in the algorithm which leads to a much better learning curve. We think that this is the way to guide
children in a playful way through their education.
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