Enhance Classroom Preparation for Flipped Classroom using

AI and Analytics

Prajakta Diwanji

1,2

, Knut Hinkelmann

and Hans Friedrich Witschel

School of Business, FHNW, Riggenbachstrasse 16, 4600, Olten, Solothurn, Switzerland

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

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.

Categories for not preparing for class

Lack of motivation/interest

Workload of other assignments and work

Material not provided well ahead in advance

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

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

(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

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.

Introduction to Perusall from

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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