The Teacher as a Facilitator for Learning

Flipped Classroom in a Master’s Course on Artiﬁcial Intelligence

Robin T. Bye

Software and Intelligent Control Engineering Laboratory, Department of ICT and Natural Sciences,

Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology

NTNU, Postboks 1517, NO-6025 Ålesund, Norway

Keywords:

Flipped Classroom, E-Learning, Active Learning, Constructive Alignment, Problem-Solving, CodinGame,

edX, C-4 Dynamite for Learning.

Abstract:

In this paper, I present a ﬂipped classroom approach for teaching a master’s course on artiﬁcial intelligence.

Traditional lectures in the classroom are outsourced to an open online course to free up valuable time for active,

in-class learning activities. In addition, students design and implement intelligent algorithms for solving

a variety of relevant problems cherrypicked from online game-like code development platforms. Learning

activities are carefully chosen to align with intended learning outcomes, course curriculum, and assessment to

allow for learning to be constructed by the students themselves under guidance by the teacher, much in accord

with the theory of constructive alignment. Thus, the teacher acts as a facilitator for learning, much similar

to that of a personal trainer or a coach. I present an overview of relevant literature, the course content and

teaching methods, and a recent course evaluation, before I discuss some limiting frame factors and challenges

with the approach and point to future work.

1 INTRODUCTION

The ﬂipped, or inverted, classroom teaching method-

olody can perhaps most simply be deﬁned as swap-

ping learning activities that traditionally have taken

place in-class with learning activities that tradition-

ally have taken place out-of-class (Lage et al., 2000).

However, as noted in a survey of research on ﬂipped

classroom by Bishop and Verleger (2013), ﬂipped

classroom constitutes much more than a mere reorder-

ing of activities performed at home or in class. They

give a more useful deﬁnition as the ﬂipped classroom

being “an educational technique that consists of two

parts: interactive group learning activities inside the

classroom, and direct computer-based individual in-

struction outside the classroom.” Elaborating on this

deﬁnition, Bishop and Verleger (2013) highlights hu-

man interaction as the key component of the in-class

activities, with a foundation in student-centred learn-

ing theories such as those of Piaget (1968) and Vy-

gotsky (1978), whereas explicit instruction methods

based on teacher-centred learning theories are out-

sourced to automated computer technology.

Similarly, Abeysekera and Dawson (2015) em-

phasise that in a ﬂipped classroom, “the information-

transmission component of a traditional face-to-face

lecture is moved out of class time [. . . and replaced

with] active, collaborative tasks.” Consequently, stu-

dents prepare for class by outside-class activities nor-

mally covered by traditional lectures, thus freeing up

valuable in-class time to student-centred learning ac-

tivities. After class, students can continue working on

in-class tasks they did not ﬁnish, explore some topics

in more detail, revise material, and further consolidate

knowledge (Abeysekera and Dawson, 2015).

1.1 Active Learning

According to Sotto (2007), higher education is domi-

nated by the transmission method of teaching, which

can be popularly rephrased as teaching by telling.

Synthesising research on the effectiveness of tradi-

tional lectures, Bligh (1998) shows that they are not

very effective for developing skills, values or personal

development, all of which are natural learning goals

in higher education. Flipped classroom moves this

one-way passive learning activity outside the class-

room and replaces it with active learning, which can

be deﬁned as any teaching method that engages the

students in the learning process (Prince, 2004). How-

ever, pointed out by Bishop and Verleger (2013), this

deﬁnition could in principle also include lectures

184

Bye, R.

The Teacher as a Facilitator for Learning - Flipped Classroom in a Master’s Course on Artiﬁcial Intelligence.

DOI: 10.5220/0006378601840195

In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 1, pages 184-195

ISBN: 978-989-758-239-4

where students reﬂect, take notes, and ask questions.

Thus, to maintain a contrast with teacher-centred

learning, it can be useful to separate lecture-based

methods from traditional lectures and deﬁne active

learning as students participating in the learning pro-

cess, doing more than just passive listening (Bon-

well and Eison, 1991). In line with this, active learn-

ing is indeed an inseparable part of ﬂipped classroom

(Schaathun and Schaathun, 2016).

Bishop and Verleger (2013) identify several ac-

tive learning paradigms, such as constructivism and

collaborative learning (originating from the theory

of cognitive conﬂict by Piaget (1968)), cooperative

learning (based on the theory of Vygotsky (1978)

on the zone of proximal development), peer-assisted

learning (deﬁned by Topping and Ehly (1998) as

“the acquisition of knowledge and skill through ac-

tive helping and supporting among status equals or

matched companions”), and peer-tutoring (e.g., see

Topping, 1996, for a review). Another active learning

method is problem-based learning, which has over-

lap with learning methods under the umbrella of peer-

assisted learning but can also be done individually

(Bishop and Verleger, 2013).

Several metastudies show that active learning in

science, technology, engineering, and mathematics

(STEM) has several advantages regarding perfor-

mance, ability to reproduce material, and motiva-

tion and engagement (e.g., Prince, 2004; Schroeder

et al., 2007; Freeman et al., 2014), whilst cooperative

learning strategies have been shown to be particularly

effective for achieving deep learning (e.g., Foldnes,

2016; Bowen, 2000; Johnson et al., 1998; Springer

et al., 1999).

1.2 Constructive Alignment

For the last decades, there has been a dramatic change

in higher education worldwide, with many more stu-

dents enrolling, from a wider diversity of background,

and with a broader range of approaches to learning

(Biggs and Tang, 2011). In engineering, students

of today conceive learning as anything between sim-

ple memorisation of deﬁnitions, applying equations

and procedures, or making sense of physical con-

cepts and procedures, to seeing phenomena in the

world in a new way or changing as a person (Marshall

et al., 1999). The big variation among students in

this taxonomy ranging from surface learning (mem-

orisation) to deep learning (learning associated with

understanding and ultimately changing as a person),

with an added category of strategic learning, in which

students aim for good grades with minimal effort (En-

twistle and Ramsden, 1983), has necessarily had an

impact on how higher education is being taught

(Felder and Brent, 2005).

Because it is well documented that students’ ap-

proaches to learning has a signiﬁcant effect on achiev-

ing learning outcomes (e.g., Gynnild, 2001; Marton,

1981), many studies have tried to identify factors that

promote deep learning (e.g., Marton and Booth, 1997;

Prosser and Trigwell, 1999). A popular answer for

dealing with the challenges of today’s diverse stu-

dent mass is the theory of constructive alignment

(Biggs, 1996). Constructive alignment (CA) merges

the constructivist view that students learn by doing

with aligning the teacher, the students, the teaching

context, the learning activities, and the learning out-

comes (Biggs and Tang, 2011). This is achieved by

working backwards when designing a course, starting

with the intended learning outcomes (ILOs), deﬁn-

ing assessment tasks closely related to the ILOs, and

ﬁnally choosing teaching methods and learning ac-

tivities aligned with the ILOs and assessment tasks

(Biggs and Tang, 2011).

Biggs (e.g., Biggs, 1996, 2011; Biggs and Tang,

2011) is especially concerned about the assessment

tasks. Since many students are strategic in their ap-

proach to learning, the exam (assessment tasks) ef-

fectively deﬁnes the curriculum, as only those parts

of the course that will affect their ﬁnal grade will be

prioritised. The answer in CA is to incorporate as-

sessment tasks with grades well aligned with ILOs to

ensure students achieve the latter.

1.3 Cognitive Load Theory

As noted in the literature review by Schaathun and

Schaathun (2016), active learning adopts the con-

structivist view that learners must construct their own

knowledge. However, at the same time, in order to

move new information from a working memory with

very limited capacity into long-term memory, deep

cognitive processing is required (Anderson, 2015).

This process, referred to as cognitive load theory

(Clark et al., 2011), represents a bottleneck in learning

when to complex problems and information must be

processed in too short a time. A remedy suggested by

several authors (e.g., see Sotto, 2007) is to break prob-

lems into small, manageable exercises in order to re-

duce the cognitive load. In ﬂipped classroom, where

typically online video lectures are offered outside the

classroom, students can manage this cognitive load by

regulating pace and rewatching video lectures (Abey-

sekera and Dawson, 2015), as well as completing their

other learning tasks in their own order of preference,

with possible repetitions if necessary.

The Teacher as a Facilitator for Learning - Flipped Classroom in a Master’s Course on Artiﬁcial Intelligence

185

1.4 Facilitating Learning

According to Biggs (2011), the teacher must create

a learning environment that facilitates learning activ-

ities that in turn make the students achieve the de-

sired learning outcomes. In CA, the key to success

is to make sure that all teaching and learning compo-

nents such as the curriculum and the ILOs, the teach-

ing methods, and the assessment tasks are aligned to

each other. However, it is commonly accepted that

lack of self-monitoring and self-regulation among

students will lead to poor academic results (e.g., Lan,

1996; Borkowski and Thorpe, 1994). Consequently,

the learning environment itself is not sufﬁcient to

achieve ILOs, since students’ individual skill in self-

monitoring and self-regulation, that is, selecting and

structuring the material to be learnt, will highly af-

fect the learning outcomes (Gynnild et al., 2008). To

cope with this challenge, (Gynnild et al., 2007) sug-

gests that the teacher must adopt a role as a facilita-

tor for learning, much similar to a personal trainer at

the gym, guiding the trainee to do the right exercises,

adjusting the “weights,” or cognitive load, whilst en-

couraging and supporting the trainee, and eventually

making the trainee self-monitored and self-regulated.

Obviously, this is a bold ambition, and increas-

ingly so with large classes, however, ﬂipped class-

room may just be the tool that makes it possible.

1.5 Outline

In the following sections, I ﬁrst give an overview of a

master’s course on artiﬁcial intelligence (AI) in which

I have employed ﬂipped classroom. Next, I present

the teaching methods I have used and present a course

evaluation that includes feedback from students in the

2016 cohort. Finally, I discuss some limiting frame

factors and challenges to my approach, point to future

work, and draw some conclusions.

2 COURSE OVERVIEW

The elective third-semester master’s course IE502014

Topics in Artiﬁcial Intelligence (TAI) has been run

with two different cohorts (autumn 2015 and 2016)

since the inauguration in autumn 2014 of a new mas-

ter of science programme in simulation and visual-

ization at NTNU in Ålesund. The course consists of

7.5 ECTS

credits and runs for 14 weeks, where all

in-class activities are constrained to a single weekly

teaching day, referred to as workshops. Providing an

European Credit Transfer and Accumulation System.

introduction to the ﬁeld of AI and a number of se-

lected topics therein relevant for solving real-world

problems, students study AI with respect to

• modelling a variety of problems in suitable state space

• design and implementation of intelligent search, opti-

mization, and classiﬁcation algorithms

• simulation and testing of models and algorithms

• visualisation and analysis of the results

The ILOs of TAI are deﬁned within the three cate-

gories of knowledge, skills, and general competence.

Speciﬁcally, upon completion of the course, students

should be able to

• describe AI as the analysis and design of intelligent

agents or systems

• explain terms such as perception, planning, learning,

and action as fundamental concepts in AI

• deﬁne problems in suitable state space depending on

choice of solution method

• solve problems by means of search methods, evolution-

ary algorithms, swarm algorithms, neural networks, or

other AI methods

• collect information from scientiﬁc publications and

textbooks and reformulate the problem, choice of meth-

ods, and results in a short, concise manner

• discuss and communicate advantages and limitations of

AI as a science

The course is split in two parts provided by two

teachers. Part A, taught by me, focusses on solving

problems by means of intelligent agents and the use

of various search algorithms, dealing with both unin-

formed and informed (heuristic) search, local search,

and adversarial search, whereas Part B, taught by my

colleague Associate Professor Ibrahim A. Hameed,

focusses on optimisation and constraint satisfaction

problems, as well as machine learning topics such

as fuzzy expert systems, artiﬁcial neural networks

(ANN), and hybrid intelligent systems. In relation to

this paper, the teaching methodology I apply for Part

A is the most relevant but I may refer to Part B or the

course as a whole where appropriate.

TAI has the following main components:

• course textbooks and scientiﬁc literature

• the learning management system (LMS) Fronter

• three mandatory assignments

• ﬁnal oral exam

• online code development platforms

• massively open online courses (MOOCs)

• full-day in-class workshops

In the following, I will present each of these compo-

nents in turn.

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186

2.1 Textbooks and Topics

The course relies heavily on the following textbooks:

• Artiﬁcial Intelligence: A Modern Approach

(AIMA) (Russell and Norvig, 2013),

• Artiﬁcial Intelligence: A Guide to Intelligent Systems

(AIGIS) (Negnevitsky, 2005), and

• Learning for Data: A Short Course

(LFD) (Abu-Mostafa et al., 2012).

Both times we have run the course, the following ten

topics in the textbooks were studied (topics 1–5 con-

stitute Part A, topics 6–10 constitute Part B):

1. Introduction to AI (AIMA Ch. 1)

2. Intelligent Agents (AIMA Ch. 2)

3. Solving Problems by Searching (AIMA Ch. 3)

4. Beyond Classical Search (AIMA Ch. 4)

5. Adversarial Search (AIMA Ch. 5)

6. Constraint Satisfaction Problems (AIMA Ch. 6)

7. Fuzzy Expert Systems

(AIGIS Ch. 2–4, case study Ch. 9)

8. Artiﬁcial Neural Networks for Pattern Recognition

(AIGIS, Ch. 6, case studies Ch. 9)

9. Hybrid Intelligent Systems

(AIGIS Ch. 8, case studies Ch. 9)

10. Nonlinear Transformation and Feature Extraction

(LFD Ch. 3)

2.2 Fronter

Fronter

is the ofﬁcial LMS in use at NTNU in Åle-

sund. Every course has its own “room” in Fronter that

enrolled students can access. The course room for

TAI contains a Frontpage with a summary of all rele-

vant information that is continously being posted, in-

cluding a welcome message; information about where

to buy the course textbooks; and course material

(course overview, oral exam information, assignment

handouts, and workshop material). The Frontpage

also contains internal hyperlinks to the course mate-

rial (usually pdfs) contained in a well-structured Doc-

uments directory. Students can quickly and easily ac-

cess the material from the Frontpage, which is impor-

tant if using a smartphone, but can also choose to nav-

igate the Documents directory for complete access to

all material. Assignments are handed in through the

Fronter submission system and detailed feedback on

students’ performance is also posted there. Using the

Fronter news functionality, we inform students when-

ever new material is available or when already posted

material has been updated.

The teachers have tried to encourage the Fron-

ter forum functionality by posting answers to several

http://www.itslearning.eu/fronter

questions that students ask in class or by email but

there has been zero activity from students in the fo-

rum. This is likely due to students being on-campus

and seeing each other steadily, thus reducing the need

for an online forum. Still, the teachers ﬁnd the forum

functionality useful but then acting more as a knowl-

edge bank of frequently asked questions (FAQs) with

answers.

2.3 Assignments and Exam

Three mandatory assignments (two for Part A, one

for Part B) must be passed for permission to en-

ter the end-of-semester ﬁnal oral exam. The assign-

ments typically consist of both theoretical short an-

swer questions and project-like programming exer-

cises. Employing a grading scheme consisting of the

letters A to F, where A is best and F is fail, a pass

grade is awarded if the work has the quality of a C

or better. However, the grade on assignments does

not contribute to the ﬁnal grade, which is determined

solely by a ﬁnal individual oral exam. Students usu-

ally get 3–4 weeks to complete and submit their as-

signments on Fronter.

Individual assignment reports, as well as a laptop

with computer code, can be brought to the exam to

provide individual entry points of discussion. The

oral exam is 25 mins and students are examined by

the course teachers.

2.4 Code Development Platforms

Developing a framework with simulation environ-

ments in which to test intelligent algorithms can

be a time-consuming task and divert attention from

the learning outcomes that we want the students to

achieve. For the programming exercises for Part A, I

rely on two online websites that provide such frame-

works, namely HackerRank

and CodinGame.

Stu-

dents have to register a user (free of charge) on both

websites.

Using HackerRank and CodinGame removes the

need for writing a lot of boilerplate code and sim-

ulation logic, leaving the students to focus on the

design and implementation of the algorithms. Both

websites support more than 20 of the most common

programming languages, provide discussion forums,

blogs, and leaderboards (rankings), with CodinGame

also providing entertaining gamelike visualisations of

the code execution, which is quite useful when test-

ing an algorithm. Thus both websites provide fun and

playful environments that act as a catalyst for learning

https://www.hackerrank.com

https://www.codingame.com

The Teacher as a Facilitator for Learning - Flipped Classroom in a Master’s Course on Artiﬁcial Intelligence

187

where students can compete both against themselves

and others in the quest of obtaining better scores.

2.4.1 HackerRank

HackerRank provides numerous programming chal-

lenges in several domains and programming lan-

guages, including the domain most relevant for this

course, namely AI. All challenges consist of a prob-

lem description and a code window in a web browser

where students can enter their programmes, or they

can use a plugin to enable writing their code locally in

their own editor. Programmes are then tested on text

input and must produce the correct text output. Sam-

ple code to get students started is provided in many

cases.

For Part A of TAI, I cherrypick exercises from

relevant AI subdomains on HackerRank, including

Graph Theory, A* Search, Bot Building, Alpha Beta

Pruning, Combinatorial Search, and Games. An ex-

ample exercise is BotClean Large, in which students

must program an intelligent agent (bot) to clean all the

dirty cells in a 2D grid. After submitting their code, a

simulation tests the bot of each student on a number

of different scenarios and returns a score.

2.4.2 CodinGame

CodinGame works in a similar manner as Hacker-

Rank but also provides game-like 2D graphical feed-

back of the behaviour of the code. The stub code

to get students started on a particular programming

challenge is usually better than the ones on Hack-

erRank. Programming challenges are divided into

single-player challenges (ranked Easy, Medium, or

Hard) that can be found in the Training category,

and larger, multiplayer games called Bot Program-

ming, that can be found in the Multiplayer category

(past contents) or Contests (currently running con-

tests). One example of a multiplayer game is Game

of Drones, in which students must control a ﬂeet

of drones and compete against other players (class-

mates, online players, or the default AI provided by

CodinGame) with their own ﬂeet of drones in a battle

of controlling as many zones as possible in a large 2D

grid.

2.5 Video Lectures and E-learning

With the advent of MOOCs, numerous online e-

learning resources exist, often free of charge. Two

of the leaders in the ﬁeld of MOOCs are Udacity

and

http://www.udacity.com

edX.

Udacity was perhaps the very ﬁrst MOOC and

was born out of a Stanford University experiment

by famous AI researchers Sebastian Thrun and Peter

Norvig (incidentally, Norvig is also co-author of the

AIMA course textbook (Russell and Norvig, 2010)).

They offered an online course called Introduction to

Artiﬁcial Intelligence online for free and managed

to achieve a simultaneous enrolment of more than

160,000 students in more than 190 countries.

EdX was founded by the Massachusetts Institute

of Technology (MIT) and Harvard University in May

2012 and provides courses from more than 60 univer-

sities, corporations and institutions.

We encourage students to enroll for free in one

or more of the courses provided by Udacity, and de-

mand that they enroll, also for free, in the edX course

CS188.1x Artiﬁcial Intelligence prepared by staff at

the University of California Berkeley. The courses

that we suggest build heavily on the textbook by Rus-

sell and Norvig (2010) and other textbooks on AI and

contain numerous interactive video lessons, self-tests

such as quizzes and multiple-choice tests, program-

ming exercises, and other material that encourage ac-

tive learning and are useful for TAI.

2.6 Workshops

All in-class learning activities in TAI take place in an

ordinary ﬂat classroom on a single weekday that typ-

ically begins at 8:15 and ends at 16:00. However, the

teacher is neither required nor expected to be present

during the entire day.

The purpose of calling this teaching day a work-

shop is to emphasise an active, collaborative, student-

centred learning environment in which students will

construct their own learning, in contrast with more

teacher-centred learning environments. I will discuss

more about learning activities in the following sec-

tion.

3 TEACHING METHODS

3.1 Information and Structure

There are three important bits of information that

must be distributed to students before the very ﬁrst

workshop.

First, because the course is closely aligned with

the course textbooks, it is vital to let the students

know which books to buy as long as possible before

http://www.edx.org

CSEDU 2017 - 9th International Conference on Computer Supported Education

188

the start of the semester. Unfortunately, some students

enrol in courses quite late, and we therefore have to

re-send this information several times to make sure

latecomers get it.

Second, we post a detailed 10-page course

overview, which serves as a reference guide for the

entire semester. This document includes core infor-

mation, high-level ILOs, teaching methodology, read-

ing material and topics, a weekly schedule, and infor-

mation about assignments, online resources, and the

course evaluation.

Third, I post the description of my ﬁrst workshop,

with details on prerequisite homework (getting text-

books and registering on online resources) as well as

in-class learning activities. Unsurprisingly, many stu-

dents fail to do the homework and the workload of

the workshop is therefore intentionally smaller than

the remaining workshops to allow for this, with no

homework reading, exercises, or video lectures.

Early in the semester we also post a detailed list of

potential oral exam questions. As we elaborate on in

an accompanying paper submitted to this very confer-

ence (Osen and Bye, 2017), posting such a list serves

several purposes. It reduces uncertainty and stress re-

lated to the exam but just as important, it provides

students with mental hooks that aid in constructing

knowledge as they progress through the course, since

having read the questions will reduce the cognitive

load when new material is introduced, and they will

be able to more quickly putting new knowledge into

context and storing it in long-term memory.

All of the above information is posted on Fronter

in a highly structured way for easy and quick access,

even when using smartphones and tablets. The course

overview is rich in detail but is a document that we en-

courage students to refer back to regularly. It is useful

for the students to have a schedule of when all the dif-

ferent topics will be introduced as well as assignment

deadlines as early as possible so that students can plan

their studies and reserve time for assignments. Intro-

ducing the teaching methodology with the emphasis

on ﬂipped classroom and active learning, with over-

arching ILOs and a well-deﬁned main objective of

the course, also provides a useful top-down view that

guides the students during the course.

3.2 Workshops

Some time between a workshop and the next (typi-

cally 3–5 days), a new workshop description is re-

leased on Fronter. Each workshop description con-

tains details such as the date, classroom, start time,

teacher, and textbook chapters/topics to be studied;

the ILOs of that particular workshop; a rough sched-

ule of tasks to be completed; details on each task; and

homework for the next workshop. This homework

consists of reading selected chapters and solving se-

lected exercises in the textbook, as well as complet-

ing various tasks in the online edX course (see Sec-

tion 3.3) or on the code development platforms (see

Section 3.4).

Every workshop begins with a short evaluation of

the current status regarding problems students have

faced in their homework, questions they may have,

what they think about the teaching methods, and other

things that come to mind. Typically, there will be

some parts from the homework that students have

struggled with and have questions about. Minor ques-

tions are answered immediately, however, if it is

clear that a more thorough explanation is needed, the

teacher writes down the questions or topics on a list

and typically gives a micro-lecture on the various top-

ics afterwards.

Next, students are given a number of active learn-

ing tasks, varying in size. For example, a two-minute

task could be to discuss with another person in class

the possible answers of a question I give them, whilst

students can be working for up to several hours on an

larger exercises or assignments.

The teacher is not required to be available at all

times. Indeed, it may be beneﬁcial to leave the class-

room at times, as internal discussion and group work

seem to ﬂourish with the teacher absent.

3.2.1 Self-tests

For self-tests (quizzes or multiple-choice questions), I

usually ask everyone to do them individually, but en-

courage discussions with other students and myself.

This has the advantage that those who prefer some

time on their own to reﬂect on the answer get that,

whereas those who prefer to interact with others in

search of answers can do so. Afterwards, I ask stu-

dents in turn for answers, and most importantly, the

reasoning behind the answers. If the student’s reason-

ing is weak, I ask for clariﬁcations from the rest class,

and ﬁnally, I often provide my own explanation, in

different wording.

Digital self-tests has the advantage that immediate

feedback is returned to the students. However, they

also require questions to be unambiguously phrased.

During a semester, there will very often be students

who are dissatisﬁed with a question, claiming that an-

other answer is more correct, or that there is no right

answer. This should not be viewed as something un-

fortunate. Rather, it is a golden opportunity for dis-

cussion in class and a driver for deep learning. Stu-

dents must have a thorough understanding of the topic

of the question and be able to formulate to the teacher

The Teacher as a Facilitator for Learning - Flipped Classroom in a Master’s Course on Artiﬁcial Intelligence

189

why they disagree with the question, and other stu-

dents have to opportunity to delve into the discussion.

3.2.2 Oral Presentations

For oral presentations, students will generally be

working in groups, which tend to lower the fear of

presenting and generally improves the quality of both

quiz answers and presentations. Each group will ran-

domly be assigned a topic for the presentation, for ex-

ample, a particular search algorithm. Knowing that

they will have to make a presentation in class but not

knowing the topic beforehand likely acts as a cata-

lyst for completing the homework in a serious man-

ner. Also, making the presentation ad hoc in class and

explaining the topic to others force the students to or-

ganise their knowledge and insight on the topics.

Naturally, the students’ presentations will suffer

from the short preparation time in the classroom. I

therefore interrupt the presentations when needed and

ﬁrst ask the presenting students if they are able to clar-

ify the issue at hand, and if not, I ask the class as a

whole if someone can help out. Even if I get a good

answer, I usually rephrase the answer with my own

words and try to be as concise and precise as possi-

ble, and hopefully, both the presenters and the rest of

the class get a much clearer picture of the particular

issue.

3.2.3 Textbook Exercises

Another activity during workshops include discussing

answers to textbook exercises. These exercises differ

from typical online exercises and self-tests, as they of-

ten require more work and are more time-consuming;

one often have to make an analysis of the problem

ﬁrst, and then provide a long and elaborate answer. It

would therefore be a waste of time to do such exer-

cises in class, and instead, students are given a short

list of recommended textbook exercises to complete

at home before each workshop. In class, answers

to the exercises are walked through and discussed as

needed.

3.2.4 Coding Challenges

Finally, being a course on topics in AI, writing com-

puter code and implementing intelligent algorithms is

an inherent and important part of the course. Dur-

ing workshops, on average, a large portion of the time

is devoted to larger coding challenges contained in

the assignments, or minor coding challenges that are

solvable in a short period of time in class. More de-

tails on coding are provided in Section 3.4.

3.2.5 Competitions

To add some spice to the learning environment, I

sometimes arrange for small informal competitions in

the class, where the winner, or winning team, gets

nothing more than pride and glory. Sometimes I split

the class in groups and let each group work on the

same problem for 10-15 mins, say. The ﬁrst team

who submits a working solution gets a point. Points

are then accumulated for various learning tasks during

the day. The students tend to like this concept but it

should not be overdone and detract from the learning

process.

3.2.6 Discussions

Discussions are not a scheduled task in the workshop

but rather something that happens ad hoc during the

tasks mentioned above. Forcing students to discuss a

topic is rarely very awarding. Instead, having out-

sourced the traditional lectures from the classroom

provides more time to allow discussions to emerge as

needed. This is extremely useful for deep learning,

as it helps students learning by reducing the cognitive

load, delving deeper into topics, and allowing time to

organise and store new material in long-term mem-

ory. In addition, discussions will often sidetrack into

related topics, evident of students looking at the world

from a different perspective, discovering relationships

across topics, and even changing as a person.

3.3 Online AI Course

All students in TAI are required to enrol in an on-

line AI course offered by edX called CS188.1x Artiﬁ-

cial Intelligence. This course has been archived since

2014, which means that no teaching staff maintain

or are active in the course. Nevertheless, all the re-

sources provided are available, free of charge, to edX

users. In Part A of TAI, four of the ﬁve topics are

covered by the edX course, with excellent video lec-

tures, step-by-step tutorials, and quizzes. The video

lectures come both in long and unedited versions and

in short and condensed edited versions that are also

interspersed with self-tests with immediate feedback,

all easily accessible on portable devices such as mo-

biles and tablets. The voices in the videos are tran-

scribed to text, which makes it easy for students to

quickly browse through a video by scrolling through

the text until a given point of interest.

Whereas some students favour watching the long

videos in one go and then use the the short, edited

videos for repetition, others jump straight to the edited

videos, especially if they have studied the textbook

ﬁrst. The video format and interactive material also

CSEDU 2017 - 9th International Conference on Computer Supported Education

190

make it easy for students to squeeze in short micro-

sessions of homework, e.g., during a 15-min bus ride.

3.4 Coding Challenges

To construct knowledge, skills, and competence

within the AI topics that we study, students need to

practice on modelling problems with relevance to the

real-world and be able to select or modify existing in-

telligent algorithms from the AI literature or devise

new algorithms suitable to the level of abstraction in

their models. The next step is to implement the algo-

rithms and test them in a simulated environment, and

perform an analysis of their behaviour, often requiring

visualisations to understand what is going on.

Doing this from scratch requires both skills and a

lot of time, and henceforth we have outsourced this

to two code development platforms, HackerRank and

CodinGame, both of which provide excellent frame-

works within the students do not have to worry all the

necessary boilerplate code and instead focus on the

algorithms.

3.4.1 Intelligent Agents

Both platforms have been designed in a similar man-

ner where the user must input a computer programme,

commonly referred to as the AI, or bot, that reads data

from standard input, does some processing, and then

outputs a desired action to standard output. This loop

repeats for a number of time steps until the simula-

tion, or game, ends. Thus, the internals of the sim-

ulated environment behaves much like a black box

to the bot, however, certain parameters in the envi-

ronment are “sensed” and provided to the bot at each

time step, and in addition, each coding challenge pro-

vides details of the model the simulated environment

is based upon sufﬁcient that a useful bot can be con-

structed. Hence, the coding challenges adopts the

same paradigm as the course textbook, in which AI

is centred around intelligent agents that sense, plan,

and act (Russell and Norvig, 2010).

Coding challenges typically revolve around cer-

tain themes in computer science and AI such as

queues, lists, search algorithms, path planning, con-

trol, etc. Very often, each challenge comes in several

versions, Easy, Medium, and Hard, in which the chal-

lenge becomes gradually more complex and difﬁcult

to solve. An example is the Mars Lander challenge

on CodinGame, where students doing the Easy level

must design an intelligent agent, ﬁrst for controlling

the landing of a spacecraft in the vertical dimension

only, then, for the Medium and Hard levels, in two

dimensions, which is exceedingly harder.

3.4.2 Design Process

When working on coding challenges, I emphasise

the importance of thinking about the problem before

jumping straight into coding. Together with the stu-

dents, we brainstorm various potential models of the

problem to be solved, and discuss aspects such as

levels of abstraction, and their advantages and lim-

itations. The solution method to be used is depen-

dent on the model, as an intelligent algorithm requires

the problem to be represented by exactly those com-

ponents that the algorithm was designed for. The

students then run a number of tests on the coding

platform to verify their design and receive automatic

feedback on which tests were passed or failed.

In contrast with HackerRank, the CodinGame

platform also provides visualisations of the simulated

environment. This is very valuable to students in the

iterative process of testing, debugging, reﬁning, and

analysing their algorithms, because visual feedback

quickly can reveal problems in the execution of the

algorithm.

4 COURSE EVALUATION

To conform with the requirement in NTNU’s Quality

System for Education that a course evaluation must

take place every time a course is run, we adopt a

scheme where all students present during workshops

(typically 7–8 students) belong to a so-called refer-

ence group. At the beginning of a workshop, we begin

with a class discussion about course-speciﬁc issues

such as status, progression, problems, etc., as well

as general issues you about the master programme in

general. The teacher takes notes, which form the basis

of an end-of-semester course evaluation report. These

short meetings provide an important arena and oppor-

tunity for students to help the teacher adjust course

early before issues become too big to ﬁx.

In the sections below, I highlight some of the eval-

uation ﬁndings from the TAI course that was run au-

tumn 2016.

4.1 Workload

Students thought the overall workload was appropri-

ate, although perhaps slightly bigger than in some

other courses in the master programme. Some stu-

dents thought the workload for the assignments was

For this reason, and other minor reasons, we have chosen

to use the CodinGame platform exclusively starting from

2017.

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191

high but thought this was justiﬁed by the assignments

being highly valuable. For example, one student said

that “(. . . ) the assignments were big but good. Even

though this course had a high workload, in retrospect

I would say that this was necessary, and the main rea-

son I learnt so much.”

4.2 Teaching Methods and Learning

Environment

Students expressed enthusiasm for the workshop for-

mat and the elements of ﬂipped classroom and had

little to add apart from comments that when revis-

ing theory from homework, e.g., in a micro-lecture, it

would be slightly better for the teacher to have slides

than having to move back and forth in the online

video lecture. Students also appreciated the fact that

videos were available in both short and long versions,

and different students sometimes favoured one over

the other. Students also expressed that the self-tests

were appropriate and useful, however, some multiple-

choice questions could contain ambiguously phrased

questions and therefore one could argue which answer

was really the correct one.

Such input is very useful for both the teacher and

for fellow students, as it triggers class discussion and

deeper insight into the problem that is being studied.

This is also an example of deep learning, commonly

observed with skilled students, as more shallow sur-

face learners will fail to note such ambiguities in the

questions.

Regarding the code development platforms, there

was consensus that HackerRank should not be used,

mainly because it was more difﬁcult and lacks vi-

sualisations, in contrast with CodinGame. Students

also thoroughly enjoyed the gamiﬁcation part of

CodinGame, where one achieves rating points and

badges and therefore are continuously eager to im-

prove.

4.3 Assignments

The students thought the topics covered in assign-

ments and the design of the problems to be solved

were appropriate, although the workload was quite

high. They also appreciated that assignments are ﬁrst

released as draft versions and then modiﬁed by the

teacher and the students together through constructive

class discussions.

4.4 Structure and Information

The students expressed satisfaction with the course

structure and the continuous ﬂow of information by

means of Fronter. The teachers have tried to encour-

age the Fronter forum functionality by posting an-

swers to several questions that students ask in class

or by email but there has been zero activity from stu-

dents in the forum. Students emphasised that being

on-campus and seeing each other regularly face-to-

face, there was not much need for an online forum.

Still, the teachers think the forum functionality should

be used but then acting more as a knowledge bank of

frequently asked questions.

4.5 Summary of Feedback

Throughout the course and in the weekly evaluation

sessions involving the entire class, students had very

little negative concern about the course itself, be it

teaching methods, workload, or curriculum. When

they had concerns, this was mainly related to clashes

of assignment deadlines across courses and sugges-

tions to reduce workload or modify content of assign-

ments.

Interestingly, students were very eager to discuss

issues of other courses and the master programme as

a whole. We see this as a sign that this course pro-

vides a safe and relaxing learning environment, since

students felt comfortable and very keen to share these

details with the teachers.

Finally, almost all students told us that they were

highly satisﬁed with the course, with two students ex-

pressing in writing that “I also wish to express that

this has been one of the best courses I have taken in

higher education” and “[t]hank you for teaching one

of the better courses if not the best course I’ve had in

this master[’]s program[me].”

5 DISCUSSION

In this paper I have presented a ﬂipped classroom ap-

proach for teaching a master’s course on AI. Whilst

it should be clear that the course has been a great

success, the reader should by no means believe that

we have found the holy grail for teaching higher ed-

ucation courses. Indeed, there are several limiting

frame factors and challenges that must be overcome

for ﬂipped classroom to work as intended. Below I

discuss some of these.

5.1 Frame Factors

The number of students in a class can be a limiting

frame factor for achieving ILOs. In the course pre-

sented here, run in 2015 and 2016, both cohorts con-

sisted of about 10 students, of which 2–3 quit early

CSEDU 2017 - 9th International Conference on Computer Supported Education

192

or never turned up.

If the class had exceeded about

25 students (which is also the number of seats funded

by the Ministry of Education), it may have been more

difﬁcult to achieve the same fruitful learning environ-

ment in the classroom. For larger classes, one would

likely have to abandon ﬂat classrooms and use large

auditoriums. University management would typically

see no problem in this and tell teachers to go ahead

and do their traditional one-way lectures as usual,

which is not desirable. Optionally, one could split

such large cohorts into smaller groups, which would

multiply the number of teachers required and asso-

ciated costs. This begs the question why there is a

transition in teaching methodology from classroom-

style teaching (one teacher per class of 30 students,

say) in lower education to auditorium-style lecture-

based teaching in higher education, thus turning stu-

dents into passive learners. Flipped classroom can

still be useful for large classes, however, but may

require much more effort in facilitating the in-class

learning activities.

Another important frame factor is the available

online resources. In TAI, we have been lucky to ﬁnd

both an edX course that is closely aligned with the

topics in the course textbook for Part A, as well as

the CodinGame platform that provides a high quality

framework with a vast variety of AI coding challenges

suitable for achieving ILOs. Producing these online

resources oneself with the same quality, as well as

the material contained in textbooks, is an impossible

task for the common teacher. Still, with e-learning

as a buzz word nowadays, university management are

very eager for teaching staff to make videos to put on-

line. I think this approach is ﬂawed, as it is extremely

time-consuming to produce video lectures oneself of

the desired quality, not to mention developing a suit-

able simulation environment for testing algorithms.

Instead, teachers should act as facilitators and devote

their time to ﬁnding such resources and tying them to-

gether in a didactically sound manner. For example,

the teacher must take care in selecting the right coding

challenges and guiding the students during problem-

solving (see the next section).

5.2 Problem-based Versus

Problem-solving Learning

Problem-based learning is a hot topic of higher educa-

tion and the interested reader may refer to our accom-

panying paper for reﬂections on many of its aspects

(Osen and Bye, 2017). Here, I refrain myself to a very

important ﬁnding by Hattie and Goveia (2013), who

15 students are enrolled in 2017.

upon examining 800 meta-analyses notes that

problem-based learning cannot be shown to have a

positive effect on achieving ILOs! The reason for

this, according to Sotto (2007), is the dinstinction be-

tween problem-based and problem-solving learning.

He argues that one should use a teaching approach

that employs well-designed case studies and avoid

problem-based and student-centred learning, espe-

cially for larger problems and when there is no clear

guidance towards how to solve them. Otherwise, stu-

dents will get stuck or spend too much time on ﬁnding

the necessary pieces to solve the puzzle by searching

the Internet or studying textbooks.

Adopting this distinction of Sotto (2007), I believe

the focus on problem-solving learning is a major key

to the success we have experienced in our course. For

example, the coding challenges on CodinGame are

carefully selected and much attention is given during

workshops on how to model the problems and how to

select appropriate solution methods.

On the other hand, if problems are too rigidly de-

ﬁned, with perhaps only one solution approach pos-

sible, as when instructing students to follow detailed

step-by-step instructions, there is a danger that cre-

ativity is neglected and students only achieve surface

learning (Andersen, 2010). Comparing with bache-

lor’s courses, where such rigid schemes to some ex-

tent can even be desirable, master’s courses should

have a higher emphasis on being research-based and

investigative in nature.

In TAI, we seem to have found the right balance

between coding challenges being too rigid versus too

open-ended. There are nearly always several paths for

solving a given problem, and the teacher often pro-

vides a simple outline of a solution as a starting point

and then guides students towards improving the solu-

tion.

5.3 C-4 Dynamite for Learning

Central to my teaching approach is an emphasis on

four axes of learning, C-4, that together are dynamite

for learning: creativity, cooperation, competetition,

and challenge.

First, the learning activities we offer in the course

should facilitate creativity. In a creative process, stu-

dents model and design solutions to a wide variety of

problems but are usually not restricted (much) on how

to model the problem and which method to use.

Second, we employ many cooperative learning

activities in class and we encourage students to co-

operate with each other also for individual work.

C-4 is also a common plastic explosive.

The Teacher as a Facilitator for Learning - Flipped Classroom in a Master’s Course on Artiﬁcial Intelligence

193

Third, as mentioned previously in Section 3.2.5,

I sometimes run small, informal competitions in

class as a driver for motivation. In addition, the

CodinGame platform is inherently competitive, as

users get rating points as they improve their solutions

or solve more problems. Students therefore compete

not only against others but also against themselves in

a quest for better ratings.

Fourth, we strive for students to critically chal-

lenge all the concepts they are introduced to in the

course and have indeed found that students tend to do

this more as the course progresses. Triggers for the

students can be as minor as mistakes in the written

formulations in quizzes, as discussed in Section 3.2.1,

or discovering that a solution method has certain lim-

itations that they ﬁrst found out about when imple-

menting it on CodinGame. For example, a given intel-

ligent algorithm may not be able to provide a good an-

swer in reasonable time and a particular CodinGame

test therefore fails.

5.4 Future Work

In future versions of TAI, my colleague and I will look

both at the curriculum and on the learning activities

that we offer to bridge the gap between Part A and B

of the course. Most of what I have presented in this

paper relates to my own ﬂipped classroom approach

for teaching of Part A, whereas my colleague has not

been able to ﬁnd the same highly aligned and rele-

vant online resources to be able to facilitate ﬂipped

classroom to the same degree for Part B. We will also

address the issue of colliding assignment deadlines

across parallel courses. Finally, we will be showcas-

ing this course to our colleagues as a motivator for

implementing ﬂipped classroom themselves.

5.5 Conclusions

I have shown one way out of many that ﬂipped class-

room can be implemented in a master’s course on AI.

The key to success is to be able to facilitate learn-

ing by cherrypicking textbook chapters and relevant

literature as well as suitable online resources such

as video lectures, self-tests, and coding challenges.

When such resources do not exist or lack in quality

or theory, one can try to make one’s own material,

e.g., a compendium supplementary to the textbook,

or one’s own video lectures. However, this is a very

time-consuming process if one wants to achieve the

desired quality, and should be a last resort.

I favour a teaching approach that has much in

common with constructive alignment, but emphasise

that the right balance between too rigidly deﬁned lear-

ning activities where students are being “spoonfed”

and too vaguely deﬁned problem-based learning ac-

tivities must be found, so that the problems are in-

vestigative in nature with different possible paths to-

wards solutions, yet at least one path must be reason-

ably easy for the students to ﬁnd and follow.

ACKNOWLEDGEMENTS

The Software and Intelligent Control (SoftICE) Labo-

ratory is grateful for the ﬁnancial support given by the

Study Committee at NTNU in Ålesund through the

project Research-based and Innovation-driven Learn-

ing (FILA), grant no. 70440500.

REFERENCES

Abeysekera, L. and Dawson, P. (2015). Motivation and cog-

nitive load in the ﬂipped classroom: deﬁnition, ratio-

nale and a call for research. Higher Education Re-

search & Development, 34(1):1–14.

Abu-Mostafa, Y. S., Magdon-Ismail, M., and Lin, H.-T.

(2012). Learning From Data: A Short Course. AML-

Book.

Andersen, H. L. (2010). “Constructive alignment” og

risikoen for en forsimplende universitetspædagogik.

Dansk Universitetspædagogisk Tidsskrift, 5(9).

Anderson, J. R. (2015). Cognitive Psychology and Its Im-

plications. Worth Publishers, 8th edition.

Biggs, J. (1996). Enhancing teaching through constructive

alignment. Higher Education, 32:347–364.

Biggs, J. (2011). Aligning teaching for constructing learn-

ing. The Higher Education Academy, York, United

Kingdom. Accessed online on 27.09.2011 at http://

www.heacademy.ac.uk/assets/documents/resources/

resourcedatabase/id477_aligning_teaching_for_

constructing_learning.pdf.

Biggs, J. and Tang, C. (2011). Teaching for Quality Learn-

ing at University. McGraw Hill/Open University

Press, 4th edition.

Bishop, J. L. and Verleger, M. A. (2013). The ﬂipped class-

room: A survey of the research. In ASEE National

Conference Proceedings, Atlanta, GA, volume 30.

Bligh, D. A. (1998). What’s the Use of Lectures? Intellect

Books.

Bonwell, C. C. and Eison, J. A. (1991). Active Learning:

Creating Excitement in the Classroom. 1991 ASHE-

ERIC Higher Education Reports. ERIC.

Borkowski, J. and Thorpe, P. (1994). Self-regulation and

motivation: A life-span perspective on under- achieve-

ment. In Schunk, D. and Zimmermann, B., editors,

Self-regulation of learning and performance: Issues

of educational applications, pages 44–73. Hillsdale,

NJ: Erlbaum.

CSEDU 2017 - 9th International Conference on Computer Supported Education

194

Bowen, C. W. (2000). A Quantitative Literature Review

of Cooperative Learning Effects on High School and

College Chemistry Achievement. Journal of Chemi-

cal Education, 77(1):116.

Clark, R. C., Nguyen, F., and Sweller, J. (2011). Efﬁciency

in learning: Evidence-based guidelines to manage

cognitive load. John Wiley & Sons.

Entwistle, N. and Ramsden, P. (1983). Understanding stu-

dent learning. Beckenham: Croom Helm.

Felder, R. M. and Brent, R. (2005). Understanding stu-

dent differences. Journal of Engineering Education,

94(1):57–72.

Foldnes, N. (2016). The ﬂipped classroom and cooperative

learning: Evidence from a randomised experiment.

Active Learning in Higher Education, 17(1):39–49.

Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K.,

Okoroafor, N., Jordt, H., and Wenderoth, M. P. (2014).

Active learning increases student performance in sci-

ence, engineering, and mathematics. Proceedings

of the National Academy of Sciences, 111(23):8410–

8415.

Gynnild, V. (2001). Læringsorientert eller ek-

samensfokusert? Nærstudier av pedagogisk

utviklingsarbeid i sivilingeniørstudiet. PhD the-

sis, NTNU.

Gynnild, V., Holstad, A., and Myrhaug, D. (2007). Teach-

ing as coaching: A case study of awareness and learn-

ing in engineering education. International Journal of

Science Education, 29(1):1–17.

Gynnild, V., Holstad, A., and Myrhaug, D. (2008). Identi-

fying and promoting self-regulated learning in higher

education: roles and responsibilities of student tu-

tors. Mentoring & Tutoring: Partnership in Learning,

16(2):147–161.

Hattie, J. and Goveia, I. C. (2013). Synlig læring:

et sammendrag av mer enn 800 metaanalyser av

skoleprestasjoner. Cappelen Damm akademisk.

Johnson, D., R., J., and Smith, K. (1998). Active Learn-

ing: Cooperation in the College Classroom. Interac-

tion Book Co., Edina, MN, 2nd ed. edition.

Lage, M. J., Platt, G. J., and Treglia, M. (2000). Invert-

ing the classroom: A gateway to creating an inclusive

learning environment. The Journal of Economic Edu-

cation, 31(1):30–43.

Lan, W. (1996). The effects of self-monitoring on stu-

dents’ course performance, use of learning strate-

gies, attitude, self-judgment ability, and knowledge

representation. Journal of Experimental Education,

64(2):101–116.

Marshall, D., Summers, M., and Woolnough, B. (1999).

Students’ conceptions of learning in an engineering

context. Higher Education, 38(3):291–309.

Marton, F. (1981). Phenomenography — Describing con-

ceptions of the world around us. Instructional Science,

10:177–200.

Marton, F. and Booth, S. (1997). Learning and Awareness.

Mahwaw, NJ: Lawrence Erlbaum.

Negnevitsky, M. (2005). Artiﬁcial Intelligence: A Guide to

Intelligent Systems. Addison Wesley, 2nd edition.

Osen, O. L. and Bye, R. T. (2017). Reﬂections on Teach-

ing Electrical and Computer Engineering Courses at

the Bachelor Level. In Proceedings of the 9th Inter-

national Conference on Computer Supported Educa-

tion (CSEDU ’17). INSTICC, SCITEPRESS. Paper

accepted for publication.

Piaget, J. (1968). Six psychological studies. Trans. A. Ten-

zer.

Prince, M. J. (2004). Does active learning work? A review

of the research. Journal of Engineering Education,

93(3):223–231.

Prosser, M. and Trigwell, K. (1999). Understanding learn-

ing and teaching: The experience in higher education.

Buckingham: Society for Research in Higher Educa-

tion and/Open University Press.

Russell, S. and Norvig, P. (2010). Artiﬁcial Intelligence:

A Modern Approach. Pearson: Upper Saddle River,

New Jersey, 3rd (international) edition.

Russell, S. and Norvig, P. (2013). Artiﬁcial Intelligence:

A Modern Approach: Pearson New International Edi-

tion. Pearson: Upper Saddle River, New Jersey, 3rd

edition.

Schaathun, H. G. and Schaathun, W. A. (2016). Learning

mathematics through classroom interaction. In The

18th SEFI Mathematics Working Group seminar on

Mathematics in Engineering Education, pages 155–

161.

Schroeder, C., Scott, T. P., Tolson, H., Huang, T.-Y., and

Lee, Y.-H. (2007). A Meta-Analysis of National

Research: Effects of Teaching Strategies on Student

Achievement in Science in the United States. Journal

of Research in Science Teaching, 44(10):1436–1460.

Sotto, E. (2007). When teaching becomes learning: A the-

ory and practice of teaching. Bloomsbury Publishing.

Springer, L., Stanne, M., and Donovan, S. (1999). Effects of

Small- Group Learning on Undergraduates in Science,

Mathematics, Engineer- ing and Technology: A Meta-

Analysis. Review of Educational Research, 69(1):21–

52.

Topping, K. and Ehly, S. (1998). Peer-assisted learning.

Routledge.

Topping, K. J. (1996). The effectiveness of peer tutoring in

further and higher education: A typology and review

of the literature. Higher education, 32(3):321–345.

Vygotsky, L. (1978). Mind in society. London: Harvard

University Press.

The Teacher as a Facilitator for Learning - Flipped Classroom in a Master’s Course on Artiﬁcial Intelligence

195