Track 12. Educational Innovation Authors: Tatiana Person Montero, José Miguel Mota Macias, Maria Del Carmen Listán, Iván Ruiz-Rube, Juan Manuel Dodero, Fernando Rambla Barreno, Concepción Muriel Patino, Adrián Ruiz Serván and Juan Manuel Vidal Pérez https://youtu.be/6DrraroOiG0

1. Authoring of
educational
mobile apps for
the mathematics
learning analysis
Tatiana Person
Jose Miguel Mota
Maria del Carmen Listán
Fernando Rambla Barreno
Concepción Muriel Patino
Adrián Ruiz Serván
Juan Manuel Vidal Pérez
Iván Ruiz-Rube
Juan Manuel Dodero
TEEM 2018

2. TABLE OF CONTENTS
1. Introduction
2. Context
3. Case study
4. Evaluation
5. Conclusion
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3. 1.
Introduction
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4. MOBILE DEVICE USAGE
▸ 78 out of every 100 people have a
smartphone globally
▸ The use of mobile applications accounts
for 54% of the time we spend in the
digital world
Can we use apps as resources to enrich
the processes we are performing?
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(*) Informe Mobile en España y en el Mundo 2017 de Ditendria.
http://www.amic.media/media/files/file_352_1050.pdf
1.1

5. MOBILE APPS WITH LEARNING
ANALYTICS (1)
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1.2
▸ In several works the authors propose mobile applications
developed to assist in the learning of different concepts. This
suggests that the literature considers the use of mobile
applications as teaching materials to be of interest.
▸ In addition, other authors suggest that the use of mobile
applications in the classroom makes students more motivated.
Following the above, the development of mobile applications
that assist and motivate students to learn complex concepts can
be helpful for teachers.

6. MOBILE APPS WITH LEARNING
ANALYTICS (2)
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Users without
programming
knowledge
Store data
Select
database
type
Query data
Select typeof queries
Learning
analytics
capabilities
Select data
1.2

7. 2.
Context
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8. 8/28
▸ A authoring tool is a computer application that
facilitates the creation, publication and
management of different materials in digital format.
APPLICATION AUTHORING TOOLS (1)
Google Forms MIT App Inventor 2
2.1

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APPLICATION AUTHORING TOOLS (2)
Visual authoring tools
2.1

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Textual authoring tools
APPLICATION AUTHORING TOOLS (3)2.1

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LEARNING ANALYTICS
▸ Learning analytics aim to process and analyse the data flows
generated during learning experiences in order to better
understand and optimise the learning process and the
environments in which they happen.
▸ In general, the learning analytics process consists of the following
stages: capture, report, predict, act and refine.
▸ The analysis of data collected during learning experiences can help
teachers to improve their teaching activities, improving the
learning conditions for individual students of groups of students.
▸ In addition, it can facilitate the evaluation of each student by
providing individual data for each of them.
▸ Moreover, the use of mobile applications that incorporate learning
analytics capabilities allows students to take control and reflect on
their learning progress outside the classroom.
2.2

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VEDILS
1212
www.vedils.uca.es
2.3

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VEDILS (2)
Definition of the
content of the
screens by means
of drag and drop
of the elements.
2.3

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VEDILS (3)
Programming of the
functionality using a
block-based
language.
2.3

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Available features on VEDILS
Available
extensions:
Augmented
reality
Virtual Reality
Emotiv Epoc+
Leap
Motion
Myo
armband
Sphero robot
Learning
analytics
2.4

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LEARNING ANALYTICS EXTENSIONS
FOR VEDILS
▸ ActivityTracker: Record information related to interactions between
users and mobile devices and finally register it in a database.
▸ ActivitySimpleQuery: Allow the simple query of the data recorded in
the database by the ActivityTracker component.
▸ ActivityAggregationQuery: Allow the aggregate query (group by and
aggregate operators) of the data recorded in the database by the
ActivityTracker component.
▸ Chart: present a specific data set in graphical form, which will be the
result of a previous query using the ActivitySimpleQuery or
ActivityAggregationQuery components.
▸ DataTable: present a specific data set in tabular form, which will be the
result of a previous query using the ActivitySimpleQuery or
ActivityAggregationQuery components.
2.5

17. 3.
Case study
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18. 18/28Mobile app for vector analysis
learning (vectorialZ)
Google Play Store URL
Application features:
▸ User identification: user will enter their email address.
The mobile app will use this identifier to record the
interactions produced by this user.
▸ Recognise AR mark: user can launch the camera
(includes AR features) to identify the predefined markers.
▸ Display the 3D model on the screen: mobile application
will show the 3D model designed and linked with the
used marker.
3.1

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Mobile app for vector analysis
learning (vectorialZ) (2)
▸ Create 3D models: First, the 3D models must be created which will
be viewed from the mobile application. These models have been
created using the Wolfram Mathematica program.
▸ Design the screens: Later, from VEDILS, the screens of the
application were designed, making use of the visible components
available in the MIT App Inventor 2 catalogue.
Application design:
3.1

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Mobile app for country-flag names
learning (vectorialZ) (3)
▸ Configure the link between the marks and the 3D
models: the ARMarkerTracker and AR3DModelAsset
components are added and configured in the app
using the “StickTo” property.
Application design:
3.1

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Mobile app for country-flag names
learning (vectorialZ) (4)
▸ Configure the record of learning interactions: The ActivityTracker
component has been configured to record the user identifier of the
mobile app and the numerical-ID marker of the 3D model visualized.
Application design:
3.1

22. 4.
Evaluation
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23. 23/28
User evaluation
▸ To evaluate the implemented app, an initial assessment was carried
out with 15 students in the third year of the Degree in Mathematics
in the subject “Análisis vectorial” at the University of Cádiz (course
2017/18).
▸ A set of questions were asked to assess the usefulness of the
mobile app. The results indicated the following:
● 60% of the students considered
the application to be very useful
for vector analysis. On the other
hand, 33.3% of students thought
the application was quite useful.
Finally, only 6.7% of the students
thought that the application was
of little useful for learning vector
analysis.
● For 53.3% of the students it was
very useful to be able to turn and
expand the 3D model for the
activity. On the other hand, for 40%
of students it was quite useful.
Finally, only for 6,7% of students it
was little useful to be able to turn
and expand the 3D model.
4.1

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Learning evaluation
▸ To evaluate the learning process, the ActivityTracker
component was configured in the app to report the number
of times each student viewed each 3D model from each
exercise. The goal of collecting this data was to identify which
exercises were most difficult for the students.
▸ More than 1280 rows were collected during the evaluation in
the Fusion Tables sheet configured in the app.
● The first model was the most viewed. This
could identify that the students should
strengthen the knowledge applied in this
exercise.
● The third model was visualised a smaller
number of times. This may suggest that this is
the exercise that was the easiest for the
students or that they had best learned the
concepts.
4.2

25. 5.
Conclusion
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26. 26/28
Conclusion
▸ This paper presents a mobile application (vectorialZ) whose aim is
to facilitate the learning of vectorial analysis in mathematics. This
app has been developed using VEDILS mobile application authoring
environment.
▸ From the app the students will be able to visualise a series of 3D
models that will represent vector functions that they will have to
identify later.
▸ In order to evaluate the usefulness of the app, an evaluation has
been carried out with 15 students of the degree in Mathematics
(UCA). The results look promising, as most the students agreed
that the app facilitated the learning of vector analysis. For this
reason, it will be proposed for use in the following courses.
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Conclusion (2)
▸ On the other hand, the ActivityTracker component was configured
to record the number of times each student viewed each of 3D
models. With the record obtained, the teachers were able to
observe which exercises seemed to be more complex for the
students and thus be able to strengthen their knowledge.
▸ Finally, as future work, an evaluation with a larger number of
students will be carried out to obtain more reliable data. In
addition, a greater number of interactions will be collected, making
use of streaming analysis to get real-time feedback from students
from the mobile app.
▸ Besides, it is expected that other possible educational applications
will be developed to promote the learning of concepts using
mobile apps, recording the interactions of the students and analysis
them later.
5.1

28. THANKS!
Any questions?
You can find me at tatiana.person@uca.es
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