Enhance Classroom Preparation for Flipped Classroom using
AI and Analytics
Prajakta Diwanji
1,2
, Knut Hinkelmann
1
and Hans Friedrich Witschel
1
1
School of Business, FHNW, Riggenbachstrasse 16, 4600, Olten, Solothurn, Switzerland
2
Computer Science, University of Camerino, Camerino, Italy
Keywords: Chatbots, Digital Assistants, Intrinsic Motivation, Classroom Preparation, Flipped Classroom.
Abstract: In a flipped classroom setting, it is important for students to come prepared for the classroom. Being prepared
in advance helps students to grasp the concepts taught during classroom sessions. A recent student survey at
Fachhochschule Nordwestschweiz (FHNW), Business School, Switzerland, revealed that only 27.7% students
often prepared before a class and only 7% always prepared before a class. The main reason for not preparing
for classes was lack of time and workload. A literature review study revealed that there is a growth of the use of
Artificial Intelligence (AI), for example, chatbots and teaching assistants, which support both teachers and
students for classroom preparation. There is also a rise in the use of data analytics to support tutor decision
making in real time. However, many of these tools are based on external motivation factors like grading and
assessment. Intrinsic motivation among students is more rewarding in the long term. This paper proposes an
application based on AI and data analysis that focuses on intrinsically motivating and preparing students in a
flipped classroom approach.
1 INTRODUCTION
As described in (Bishop and Verleger, 2013), the
flipped classroom approach is a student-centered
learning approach, in which the traditional teaching is
reversed (see Figure 1), in the sense that students are:-
Exposed to the learning content outside of the
class via videos or articles.
Provided with out of class or online
opportunities to interact and discuss the
material with fellow classmates.
Utilizing their class time to understand the
knowledge through discussions, problem based
learning and presentations.
In a flipped classroom, students are expected to
come prepared to the classroom sessions. Preparation
includes reading articles, watching videos and
multimedia content, preparing presentations, taking
short quizzes and having discussions with fellow
classmates (Lo, Hew and Chen, 2017; Sheppard et al.,
2017). The learning materials and outside class
activities can be delivered via online systems like
learning management systems etc. (Caligaris,
Rodríguez and Laugero, 2016; Wang, 2017; Yilmaz,
2017). In case method-teaching (Hammond, 2002)
students are expected to study real life case studies in
Figure 1: Different stages involved in flipped classroom
setting interpreted from (Bishop and Verleger, 2013;
Caligaris, Rodríguez and Laugero, 2016; Wang, 2017;
Yilmaz, 2017).
details and discuss the case informally with their
peers before coming to the class. In classrooms, the
students bring their own analysis and try to defend
their own perspectives about the case. A recent survey
conducted among the higher degree students of
FHNW School of Business revealed that only 27.7%
students often prepared before a class and only 7%
always prepared before a class. According to a study
by PwC in (PwC Digital Services, 2017), more than
half of the candidates believed that AI had great
potential in delivering personalized and adaptive
Diwanji, P., Hinkelmann, K. and Witschel, H.
Enhance Classroom Preparation for Flipped Classroom using AI and Analytics.
DOI: 10.5220/0006807604770483
In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 477-483
ISBN: 978-989-758-298-1
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
477
education to students and that use of AI-based digital
tutors will be on the rise in the coming 5 years. Please
refer Figure 2. Similarly, there is a growing interest
in teaching analytics, which supports the teacher
awareness, reflection and decision making in both
physical and virtual classroom environments
(Mclaren, Scheuer, and Mikkátko, 2010; Vatrapu et
al., 2011; Holstein, McLaren and Aleven, 2017).
Tools like teachers dashboard are being developed to
support decision making of teachers by providing
them with actionable insights of students learning
activities in real time (Tissenbaum et al., 2016;
Holstein, McLaren, and Aleven, 2017). Based on the
results of the survey and the literature review we
present the argument that AI based tools along with
data analytics would help build the intrinsic
motivation of students for classroom preparation in a
flipped classroom setting. It additionally offers the
opportunity for teachers to personalise, adapt the
teaching to the interests and weaknesses of the
students, and focus on topics that are not clear to
them. The paper initially presents the results of the
survey at FHNW and of the literature review. Later it
presents the concepts related to the AI based
application to support classroom preparation in a
flipped classroom setting.
2 RESEARCH PROBLEM
According to a recent survey conducted among 140
postgraduate students of FHNW School of Business,
56% of the students only prepare sometimes for the
classroom sessions while 8% of students do not
prepare at all. Only 27.7% students prepared often
while 7% students always prepared for a class.
Figure 2: Statistics for classroom preparation at FHNW.
Nearly 88% of students attributed to lack of time
as the main reason for not preparing regularly for the
classroom. Other students felt that classroom
preparation is not necessary as they already had
knowledge about topics; some topics were too
difficult to prepare on their own; that teachers will
anyways teach the concepts in class. Nearly 51% of
students were of the opinion that pre-classroom
preparation would help them to be more engaged in
classroom sessions. Students also specified other
reasons separately (as comments) for not preparing
for the classroom. They are mentioned in table 1
below.
Table 1: Few categories of students' comments.
No
Categories for not preparing for class
1.
Lack of motivation/interest
2.
Workload of other assignments and work
3.
Material not provided well ahead in advance
4.
Not aware of what to prepare exactly
Although studies (Taha et al., 2016; Lo and Hew,
2017) suggest that use of flipped classroom approach
does improve student learning experience and
outcomes, it still has some issues and challenges. As
per the study of (Lo and Hew, 2017) in K-12
education, 1) teachers workload of preparing for
flipped classroom materials increases considerably,
2) students are less engaged in or skip out of the pre-
class activities, 3) pre-class preparation had caused
many of the students to be dissatisfied. At the same
time, the lecturers expect to motivate and engage
students in classrooms. In a one-teacher classroom, it
is not always possible for lecturers to give
personalized attention to each student (Holstein,
McLaren and Aleven, 2017). The lecturers thus
expect to understand beforehand the doubts and
confusion of students so that they can prepare ahead
for the class (Benotti, Martínez and Schapachnik,
2014; Pereira and Juanan, 2016; Holstein, McLaren
and Aleven, 2017). (Tileston, 2010) suggests that
students learn well and are more interested in learning
when they are intrinsically motivated. Intrinsic
motivation is the inner drive that motivates students
to pay more attention to the learning (Tileston, 2010;
Perlman, 2013). External motivations based on
reward systems like marking, giving bonus points,
grading for participation etc. only help students
temporarily to be engaged in learning and is not
healthy in long-term learning (Tileston, 2010). It is
possible to build intrinsic motivation in students by
using effective teaching and communication
techniques (Seifert, 2004). Most of the flipped
classroom approaches use external/extrinsic
motivation factors like assessment/grading for
classroom preparation. Based on this students
consume the preparation material because they are
rewarded with grades (Elliott and Rob, 2014).
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
478
(Maderer, 2017) reports that in spring of 2016,
two AI based assistants were introduced at the
Georgia Tech University in the United States by a
professor in an online course on Artificial Intelligence
(AI). One bot named Stacy interacted with students
during class introductions and posted weekly updates
while another one named Ian answered routine
questions of students. At the end of the class, it was
observed that students were more engaged in the
current class than last semester, as there was a rise in
a number of comments and online interactions with
the AI chatbots. The teacher attributes this increase
in interaction partly to the AI tutors, as they were able
to give fast response to the students than their human
assistants.
The main aim of this research work is to 1)
improve the intrinsic motivation of students 2) help
students and teachers to prepare well in a flipped
classroom setting 3) investigate how AI and data
analytics can be used for enhancing classroom
preparation. In order to carry out the research work,
following research questions have been derived. The
main research question is:
How to build intrinsic motivation of students and
help them as well as teachers to prepare effectively
in a flipped classroom setting?
Sub-research questions are as follows:-
What are the requirements of students for
classroom preparation in a flipped classroom
setting?
What are the requirements/expectations of
teachers for classroom preparation in a flipped
classroom setting?
How to build intrinsic motivation among
students?
How can AI and data analytics be used to help
students and teachers prepare ahead for the
classroom?
3 LITERATURE REVIEW
Chatbots or bots can hold intelligent natural language
conversations with humans in an engaging manner.
These conversations can be either text-based or voice-
based (Fryer et al., 2017). Due to these features of
chatbots/bots, it makes them a good candidate as
assistants to students as well as teachers in the
learning as well as teaching processes. For example,
chatbots have been used to practice English language
lessons with students (Fryer, 2006; Ayedoun,
Hayashi and Seta, 2015; Fryer et al., 2017). In a
1
Differ bot studied from the website https://www.differ.
chat/
recent article (McNeal, 2017), chatbots seem to
reduce the workload of the teachers by answering the
routine questions asked by students and prompt them
to complete the assignments on time. In past few years,
chatbots (Pereira and Juanan, 2016) have come up with
more advanced features. Using machine-learning
techniques, these bots can track the progress of the
student’s assignments and quizzes and tests and
inform the students in a friendly and motivating
language to take an appropriate action if they are
lagging behind. Similarly, they can also inform the
teachers about the student’s progress (Pereira and
Juanan, 2016). There is a growing evidence that use
of chatbots/bots motivates the students for learning
and keeps them engaged in the learning process.
Following are few examples of some
implementations of chatbots.
@DAWEBOT is a bot discussed in (Pereira and
Juanan, 2016), that provides quizzes in form of
conversations with students. The bot helps students to
take quizzes. After the quiz, it gives feedback to
students about their results and understanding of the
topic via a web dashboard. Teachers can also find
how students are mastering each topic and to which
extent the topic is understood by students. This bot
was tested with 23 students of computer science for a
15-week class. After completion of the course, 89%
students thought that using bots for practicing a test
as a good idea and almost 72% students thought that
the bot helped them to be more engaged in their
subject. However, this chatbot was meant to practice
questions for the real exam and is thus based on
external motivation factors like assessment, grades
etc.
Differ
1
(Differ, 2017) developed by EdTech, is a
bot based application that engages the student with
the course material. It automatically recommends and
makes groups of students and kick-starts the group
discussions by posting introductory messages in chat
groups. It helps students with assignments by giving
hints or nudges i.e. subtle messages with a
recommended set of actions to complete their
assignment. As per their website information,
students felt more engaged with the content and felt
more comfortable while asking repetitive questions to
the differ bot. However, the chatbots in Differ only
kick-start the student conversations and do not really
seem to actively participate in them (see figure 3).
Student interaction and learning data logged in
the technology enhanced learning systems has the
potential to reveal insights about learner’s activities
(Tissenbaum et al., 2016). Many learning
Enhance Classroom Preparation for Flipped Classroom using AI and Analytics
479
environments now display learners and teachers
dashboards and visualizations that provide actionable
insights about the state of the whole class as well as
individual student performance for teacher decision
making and providing adaptive feedback (Vatrapu et
al., 2011; Tissenbaum et al., 2016).
Figure 3: Differ bot for introduction in groups and
assignment help (from https: //www.Differ.chat/bots).
Perusall
2
,
3
is an eBook based learning platform
that allows students to prepare online before
classroom sessions. Students read and annotate online
articles with comments that can be shared with the
other fellow students. Students could also ask
questions; discuss answers with their peers related to
the annotations or contents of the online book. This
chat data is analysed to detect the student’s doubts,
confusions and interests and this information are
reported to teachers in the form of confusion reports.
However, Perusall does not have support for
generating auto conversations. It has a grading system
that grades the annotations and questions and answers
of the students in the online book. Hence it is based
on external motivation factors like assessment,
grading. It is lightly based on intrinsic motivation as
it allows student interactions.
The literature review thus describes the latest
tools that support student preparation and their
shortcomings. In the discussion phase, we suggest the
concept of an application based on AI chatbot and
data analysis that support students classroom
preparation, builds intrinsic motivation among
students and gives feedback to teachers for classroom
preparation.
2
Introduction to Perusall from
3
Referred from https://perusall.com/
4 DISCUSSION
In this research work, the focus is to enhance the
classroom preparation in a flipped classroom setting
and build the intrinsic motivation of the students.
Based on the results of literature review it is observed
that use of chatbots engages and motivates students in
learning process. As per research by (Klemm 2002),
conversations are important for knowledge exchange
and motivating people. Conversations between
students-students, teacher-students contribute to
learning and knowledge. This research also suggests
that written form of conversations are more impactful
as writers are more intensely engaged in the content
while writing. For this purpose, we want to build an
application that includes a conversation-based
chatbot that will assist the students and teachers in
preparing for the flipped classroom.
In order to build such an application, we follow
the design science approach. As mentioned by
(Hevner and Chatterjee, 2010; Carcary, 2011), design
science is problem-solving research field that
involves designing and developing innovative
artefacts for real-world complex business problems.
It is aimed to design and build artefacts that are
effective in solving problems. The design artefact in
this context is Intelligent application for classroom
preparation in flipped classroom approach”. In the
awareness phase, the interviews and surveys have
been conducted to understand the exact needs of
classroom preparation of students and teachers. In the
suggestion phase, the requirements and features of the
application will be defined. Figure 4 depicts a
conceptual design of the application. This application
is a platform that includes a chatbot, a data analysis
engine and a dashboard for student and teacher
recommendations. The platform can integrate the data
from different sources like student profiles, learning
management system, course data etc. The main
purpose of the bot will be to introduce the students for
the topics of the upcoming class by 1) engaging them
in small group text based discussions on class topics,
2) answer students question about topics that are
relevant to preparation tasks given by teachers.
The inbuilt chatbot will trigger short
conversations or interactions about classroom topics
among students and actively participate in such
activities.
In order to generate the
conversations/interactions, the chatbot will refer to
the course content data. Moreover, lecturers will be
able to easily configure the bot to trigger appropriate
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
480
Figure 4: Concept of chatbot supported application for classroom preparation in flipped classroom (Free graphics used from
https://pixabay.com/).
conversations or interactions for the classroom
preparation. In order to make student groups for
group conversations/interactions, the bot will first
assess profiles of students and take into account
factors like past experience, similar interests,
diversity etc.(Srba and Bielikova, 2015). This will
create a diverse group of students that could generate
an interesting mix of conversations.
After the interaction, the conversation data will be
analysed in real time to find the doubts, motivation,
interests of the students. Dashboard features will be
provided to students to track their progress for
classroom preparation. While lecturers will be
provided with the information about student’s
motivation level, doubts, and questions. Based on
these insights the chatbot will also give suggestions to
lecturers on teaching strategies and tips to prepare the
classroom material (e.g. presentations) for the
upcoming class. By knowing the doubts of students,
the lecturers can adapt their teaching methods and
content in a timely manner. Please refer Figure 4 for
the chatbot application features. Over the period, the
bot shall learn more about the student’s personalities
from the accumulated student’s insights data. This
data will help the bot to predict when and where each
student might need help during subsequent classroom
preparations. This will give way to more personalized
support to students. In future, there could be multiple
instances of chatbots that could automatically cater to
personalized preparation needs of different students.
5 FUTURE WORK
The research work until now identified the research
problem and the design artefact that we want to build
for classroom preparation in the flipped classroom
approach. In the next stage, i.e. in the suggestions
phase, we want to build the exact requirements or
design of the classroom preparation application.
Figure 5 depicts the boundary of the classroom
preparation application along with the goals,
inputs/outputs, and conversation-based features. The
inputs to the application can be preparation goals and
input materials provided by the teacher. The goals can
be giving students highlights of the next class; basic
concepts of the session; short assignments etc. The
input material given for preparation can be text-based
materials at the start. In future, we can consider video/
and audio materials. For generating conversations we
also need to decide the actors, activities, and
scenarios. For example, the actors can be the bot and
the students. The bot can do the group formation of
the students for the activities based on their profiles
etc. The scenarios depict how the bot will engage in
the conversation-based activities with the students.
The bot can serve as 1) a conversation initiator 2) a
conversational mediator 3) a recommender for
completing tasks/activities. The output of the system
can be the learning outcomes achieved by the
students. Key performance indicators like motivation
and engagement will also be defined during this
phase. In order to define scenarios and activities, we
will conduct expert interviews with lecturers to find
learning activities that could be delivered via the bot
Enhance Classroom Preparation for Flipped Classroom using AI and Analytics
481
Figure 5: Defining the goals, input/output, actors, activities and scenarios of the system.
based application. We will do a literature review to
understand which learning activities/processes are
effective for flipped classroom and how best they can
be supported by the application. For example, we will
be investigating theories like Self-Determination
Theory (SDT) for building intrinsic motivation
(Ryan and Deci, 2000) and Speech act theory
(Searle, 1969; Winograd and Flores, 1986;
Colombetti and Verdicchio, 2002) while designing
the conversations based activities/scenarios in the bot
based application. The proposed application will be
developed using agile methodology (Dingsøyr, Dybå
and Abrahamsson, 2008). At first, the application
prototype will be built with the few set of features,
scenarios and evaluated in actual class settings at the
degree programs at FHNW School of Business. The
students will use the tool for class preparation, and the
conversations will be analysed and KPI’s like
engagement etc. will be measured. At every stage, the
results from the evaluation will be considered for the
design and development of the subsequent prototype.
6 CONCLUSIONS
Classroom preparation is important in a flipped
classroom setting. From the student survey results at
FHNW and literature review results, it is clear that
many students do not prepare for classrooms sessions
in a flipped classroom. Literature review suggests that
intrinsic motivation building in students is more
rewarding than external motivations like grading,
assessments etc. Good conversations among students
facilitate learning and increase their motivation. The
existing tools dealing with classroom preparation
focus mostly on extrinsic motivation factors like
grading, exams, and assessment. With the rise in AI
technologies, chatbots are being used as students and
teachers assistants to facilitate the learning and
teaching processes. The research paper proposes an
initial concept of chatbot supported classroom
preparation application that will enhance the outside
class student preparation process and build the
intrinsic motivation of students by engaging students
in short interesting conversations about classroom
topics. Moreover, this application will also help the
teachers to prepare in advance for the class by
understanding students doubts and weaknesses.
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