Dropout Rates of Regular Courses and MOOCs

Léon Rothkrantz

Delft University of Technology, Mekelweg 4, Delft, The Netherlands

Technical University in Prague, Konviktskastreet20, Prague, Czech Republic

Keywords: Dropout Rates, MOOCs, Flip the Classroom, Didactical Models, Blended Learning.

Abstract: Recently we observe an enormous grow of Massive Open Online Courses (MOOCs). But it proves that the

dropout rates of MOOCs are very high. One of the main causes are missing of necessary capabilities of

students, inability of students to manage their study and a missing appropriate didactic model. In this paper

we compare the dropout rates of MOOCs, regular courses and courses using new didactical approaches as

blended learning and flip the classroom. Finally we discuss possible ways how to teach 21st century skills

as cooperative working, learning, creativity, networking and how to solve real life problems in a context

sensitive approach. Our research findings are based on educational experiments at Delft University of

Technology (DUT).

1 INTRODUCTION

In a complex, globalising, unpredictable world full

of networks, worldwide communications and social

media we have to train students in acquisition of

typical 21

century skills such as critical reflection,

cooperating, networking, creativity, ability to handle

big data, ability to solve real life problems, ability

for life-long learning. We have to educate students

which are able to face and contribute to the future

world (Bussemaker, 2015). The question is whether

and how educational innovations as “blended

learning” and MOOCs enable students to acquire

such skills. It is not enough to offer courses as

MOOCs but it is also necessary to develop didactic

models to challenge students to take this courses and

complete them successfully (Rothkrantz, 2015).

The process of teaching and learning has an

impact in three domains (Bussemaker, 2015):

 Qualification, this is about the role of teaching in

acquisition of knowledge, abilities,

competencies, and attitudes

 such that after

graduation students are qualified to perform their

job or play their role in society.





Socialisation, this is the way how we teach

students the social processes in a job

environments or culture and democracy in

general.





Personal development, the impact of teaching on

the process of individualisation and subjectivity

.

The question is how to realise these goals with a

student population of increasing diversity,

differences in cultural background, interest, abilities,

learning style, speed/pacing. Most regular courses

are focussed on realisation of the qualification goal.

By introducing MOOCs and blended learning

courses the hope was that they support socialisation

and personal development. But as we will show in

common used didactic models it is assumed that

students master already 21

century skills. But in

most secondary schools these skills have not been

trained, so they have to be learned at University.

From a recent study in the Netherlands

(Bussemaker, 2015) it proves that the teaching

process at Universities is still focussed on

knowledge transfer and qualifications of students

and not on socialisation and personal development.

The concept Bildung introduced by Humboldt in the

century focussed on the development of all

human abilities and not only some cognitive abilities

and knowledge acquisition is a relevant item.

To realise such educational goals there is a

request of smaller learning communities with intense

interaction students and teacher. But in MOOCs the

direct interaction with a teacher is minimised and is

embedded in the teaching material in a non-direct

way. We observe also a trend of creating honours

programmes challenging and enabling students to

compose their individual self-paced programmes.

Rothkrantz L.

Dropout Rates of Regular Courses and MOOCs.

DOI: 10.5220/0006811600010001

In Proceedings of the 8th International Conference on Computer Supported Education (CSEDU 2016), pages 9-18

ISBN: 978-989-758-179-3

Concepts as freedom, responsibility, societal

relevance and innovation, are very appealing to

students. But we can observe that only a minority is

able to implement such programmes successfully.

Many students don’t know what they really want,

are not aware of requirements of individual studies

and of the required capabilities. MOOCs have a high

dropout rate because students don’t have the

required capabilities, attitudes and ability to control

their own study behaviour. Student are used to take

precooked programmes and a teacher taking the

supervision on their study. Self-paced studies require

a new study behaviour which has to be learned.

In section 4 we discuss the results of a

psychological assessment at TUDelft. First years

students were tested using the Big Five personality

test to assess if students have the abilities to learn

century skills (Rothkrantz, 2015). Then we will

discuss the results of a huge experiment performed

at TUDelft from 1953-1957 (Bottema 1959).

Students were tested using personality tests,

(cognitive) ability tests, completed with interviews

of student counsellors and surveys on assessment of

features in the teaching-learning environment. The

goal of this experiment was to research if

psychological assessment could provide additional

information next to the results of final examination

at secondary schools to predict study-success or –

failure. In 2014 an experiment has been started using

new teaching learning models in mathematical

courses for first years students at TUDelft. Again we

will report the study performance of students

compared to traditional ways of teaching

mathematics. To summarize the goals of this paper

are:

 Identification of factors/reasons explaining the

high dropout rate of MOOCs.



Comparison of factors underlying different

teaching-learning models varying from

classroom teaching up to MOOCs, with respect

to study -success or –failure.



Our research findings are based on results of

experiments performed at Delft University of

Technology.

2 LITERATURE SURVEY

In (Onah et al., 2014) the authors researched MOOC

drop rates from different perspectives. They listed a

number of reasons for dropout based on literature

search. Next they researched data from a specific

MOOC provided by the University of Warwick.

Their results indicate that many participants who

may be classified as dropouts are still participating

in the course in their own preferred way, either with

a slower pace or with selective engagement .

In (Yang, 2013) the author explores students

dropout behaviour in MOOCs. As a case study they

took a special class. He developed a survival model

that allows to measure the influence of factors

extracted from that data on student dropout rate. His

study shows that specific social factors as interaction

between students, emergent sub community

structure affect dropout behaviour

In (Sinha, 2014) the author researched the

underlying interaction mechanisms which govern

students’ influence on each other in Massive Open

Online Courses (MOOCs. Specifically, they outlined

different ways in which students can be negatively

exposed to their peers on MOOC forums and discuss

a simple formulation of learning network diffusion,

which formalizes the essence of how such an

influence spreads and can potentially lead to student

attrition over time.

In (Ye and Biswas, 2014) the researchers

extended traditional features for MOOC analysis

with richer and higher granularity information to

make more accurate predictions of dropout and

performance. The results show that finer-grained

temporal information increases the predictive power

in the early phases of the Pattern-Oriented Software

Architectures (POSA) tested on a MOOC offered in

summer 2013 by Vanderbilt University.

Prediction of study success is a popular research

topic over the years. (Wilbrink, 1997) shows a

literature survey of assessment in historical

perspective with more than 200 reviewed papers.

In (Tinto, 1975) ia theoretical longitudinal model

has been introduced, demonstrating how different

personality characteristics and characteristics of the

University have their impact on the interaction

process student-University. Such a process resulted

in delay, dropout or study success. In fact Tinto

describes the interaction process between three

systems, the individual student, the academic system

and the social system. According to Tinto the

individual student will enter the University with a

specific social background, personality education

and training. He will have a special binding with the

University and goal of the study. This binding will

be expressed in motivation and expectations. The

binding will change over time, caused by the

influence of the academic and social systems and the

interaction of both and will eventually result in

dropout.

A similar study has been performed in Belgium

at the Universities of Ghent and Leuven. Researcher

Lacante and Janssen were involved in research on

prediction of study-success for a long time (Lacante,

1981). They developed special surveys to predict the

academic performance at Universities. They found

similar results as the researchers at Delft University

with some refinements.

The didactic Adagio of the famous Dutch

mathematician and didactic specialist in

mathematics Freudenthal (Freudenthal, 1973) was

“You can learn mathematics only by doing and

discover mathematics in the real world”. For him

was teaching mathematics an educational task and it

should be context sensitive and application oriented.

Students should be able to design mathematical

models and translate real world problems formulated

in natural language in a mathematical language. It is

very important to give students opportunities to

reflect on and clarify their thinking about

mathematical ideas. Most of current didactic models

fits in the discovery learning tradition developed

around 1960. Piaget, Dewey, Vygotsky and Freire

and many others support constructivist learning. Up

to then drill and practice was one of the favourite

pedagogical principles in mathematics. Now the

focus is on learning based on personal and societal

experience. Our developed didactic model FETCH

2.0 is based on similar ideas (Rothkrantz, 2015). The

question is of course how to implement this didactic

model in the developed MOOC. The oldest, and still

the most powerful, teaching tactic for fostering

critical thinking is Socratic teaching. In Socratic

teaching we focus on giving students questions, not

answers. The next step is that students themselves

learn to generate questions around a learning text

(Rothkrantz, 2015).

Mathematicians are trained to ask (critical)

questions reading a scientific journal. These

questions are stimulated by the learning material but

also by the surrounding environment and context.

Developing a critical attitude by students is not

limited to mathematics. Freudenthal writes about

“Mathematics as an educational task”. Many

mathematicians use the inquiry based methods also

during reading or reviewing a paper, or documents

or listening to a presentation. It proves that inquiry

based method is an efficient way to keep the

reader/listener alert and is a first step to processing

the presented information.

3 REASONS FOR DROPOUT

In this section we discuss possible causalities of the

high drop off rate of MOOCs. TUD launched 20

MOOCs, accessible via edX platform. In this section

we focus on the MOOC Pre-Un Calculus, visited by

more than 26.000 students. Via interviews and

questionnaires experiences of students and teachers

are assessed and reported in (Vos, 2015). Most

(early) dropouts from the course didn’t take part in

the assessment procedure, so we realise that the

results are biased.

MOOCs are in principle open for everybody. No

entrance exam has been required. But some

knowledge and capabilities are required to finish a

MOOC successfully. This is stressed in information

about the course but some students still believe they

can do it. Similar experiences we have with regular

students at DUT. No entrance exam is required to

enter TUD. The first year of study should be used

for selection, orientation and adaptation. It proves

that low grades for mathematics and physics at

secondary school are good predictors for dropout.

Over the years the failure rate of regular academic

courses has been studied. We will report about a

study at Delft University of Technology with a

psychological assessment of all freshmen. It proves

that results of students at their secondary school

predict about 40% of the study success or failure in

the first year. Psychological assessments ads about

10%.

To complete a MOOC successfully a student

should have a strong motivation, based on the

expected outcomes, interest in science and increase

of knowledge and competences. Next a student

should be able to manage his time and plan a study.

In the current MOOC a global time schedule of

video lectures, simulations, exercises and exams is

presented. To enable communication and

cooperation between students all students are part of

one group taking the course together. In more

individual based schedule students will lack support

of fellow students. Many students reported that they

prefer to manage their own course without

cooperation. This violates of course the goal to

realise 21

century skills. Individually based studies

are offered for many years by the open University.

But they also offer meetings for participants to

create a community. Given a strong motivation and

ability to plan their study, students should be able to

come to activities. Viewing video lectures and

simulations don’t require active participation of

students. It may give them the feeling that they have

everything under control. They are confronted with

reality if they have to make assignments or exams.

Inadequate time management, un-ability to set

themselves into action and missing of other study

skills, results in many cases to dropout.

An adapted didactic model for MOOCs is

needed. Recorded web lectures of gifted teachers or

successful lectures are no guarantee of high study

success of MOOCs. In many cases the didactic

model of regular courses in lecture halls is copied. A

teacher explains the theory, gives some examples

during the course and students are supposed to study

the learning material and to make exercises. But

making homework is a great problem. The gap

between lectures and making exercises is (too) big

and most students are not able to manage their time

and set themselves into action.

It can be observed at TUDelft that a week before

the exams students start to visit libraries, study-halls

etc. to start their preparation for exams. Observing

this study behaviour of fellow students triggers other

students to start similar behaviour. It is difficult to

implement similar triggers in the MOOC

community. But we have to realise that used didactic

models assuming regular study behaviour of

students in general don’t work for MOOCs. In

regular students are creative in finding alternatives

observing peer models what is still missing in the

MOOC community.

Personal, intensive study guidance results in

better study results. A good binding with the study

and study community is an essential prerequisite for

successful study. Unfortunately the massive

character of MOOCs makes individual tutoring by

teachers impossible. One of the assumptions of

creating a study community via social media is that

students will support each other. But it proves that

creating study communities is far from trivial. One

of the outcomes of the surveys is that many students

don’t like networking and cooperation and prefer to

study in their own individual way. Students get

stronger involvement with the study and study

community.

To be a member of a learning community has a

great impact on the motivation, emotions and study

results/achievement. (Furrer and Skinner, 2003). It is

important to give students the feeling that it is

exceptional to take these courses and that they

should be involved in learning communities. In case

of MOOCs it cannot be expected that there is much

individual support or supervision of teachers. Fellow

students can take the role of a teacher and the

community of fellow students should support and

stimulate the members of the community.

Many students following MOOCs don’t live in a

student environment. Via social media they can be

involved in learning environments. Participation in

such an environment is important to develop 21

century abilities. MOOCs offer the opportunity for

internationalisation, integration of research in the

learning environment. In research based education

students are involved in common research activities

and in research tutored education students perform

research activities themselves.

Students following MOOCs have to take the

responsibility for their own learning process. It is

definitely true that part of the students are able to

manage their study. But most students need group

pressure, supervision of teachers, counsellors, fellow

students to complete a MOOC successfully.

Nevertheless many educators prefer self-paced

instruction so that every student is able to define his

individual study path. This enables students to

accomplish their study at their own speed. But it

creates problems to give support from the

community. In the Netherlands some PhD students

have to follow MOOCs instead of common courses

at some University. This is highly appreciated by

PhD students and the success rate is high. After

completion of a course PhD students are supposed to

do an exam at the home University.

4 EXPERIMENTS

4.1 Big Five Personality Test

One of the research goals of this paper was to

investigate how to teach students 21

century skills

as networking, cooperation, creativity etc.

Traditionally teaching mathematics is rather

individually oriented. A lecturer introduces students

in lecture halls into new topics. Students listen and

make notes. Parallel to the lectures are usually lab

hours during which time students are supposed to

make exercises alone or together. Especially during

the lectures students are passive consuming

knowledge, instead of exploring new topics and

discover new knowledge and even more important it

is an individual process. It takes students less energy

to listen to lecturers and see how he discovers new

knowledge. But the challenge is to take students out

of their comfort zone and challenge them to actively

search for knowledge and problem solving.

Changing the didactic approach may change the

learning attitude of students. It can be expected that

there will be resistance leaving the comfort zone. A

second question is if students have the right

personality characteristics to learn the 21

abilities.

If students are not able to cooperate, socialise and

network there is a problem. To research that problem

a group of first years students in mathematics and

computer science were tested using the Big Five

Personality test in September at start of the academic

year. Students were supposed to fill in a

questionnaire of 4x5 questions. Then the score on 5

factors E, A, C, N, O listed below were computed.

Students with high scores on the factors A and O are

supposed to be open for learning the 21

century

skills. But it proves that students score significant

lower on A and higher on O. This supports the

hypothesis that students in technology or exact

courses have the tendency to work individually and

are open for new knowledge. The factors underlying

the Big Five Personality test can be described as

follows:

Extroversion (E) is the personality trait of seeking

fulfilment from sources outside the self or

in community. High scorers tend to be very social

while low scorers prefer to work on their projects

alone.

Agreeableness (A) reflects much individuals adjust

their behaviour to suit others. High scorers

are typically polite and like people. Low scorers tend

to 'tell it like it is'.

Conscientiousness (C) is the personality trait of

being honest and hardworking. High scorers

tend to follow rules and prefer clean homes. Low

scorers may be messy and cheat others.

Neuroticism (N) is the personality trait of being

emotional.

Openness to Experience (O) is the personality trait

of seeking new experience and intellectual

pursuits. High scores may day dream a lot. Low

scorers may be very down to earth.

Table 1: Average values of the five factors of the Big Five

test E, A, C, N, O for a cohort of TUD students computer

science and mathematics and a general cohort.

Number of respondents 10189 179

Extroversion 3.05 3.05

Agreeableness 3.84 3.69

Conscientiousness 3.38 3.06

Neuroticism 2.98 3.39

Openness 4.05 3.71

From Table 1 it can be observed students

mathematics and computerscience have the same

average score on the factor Extroversions indicating

that in principle they are open/not open for social

contacts and open/not open for networking.

The students in the TUD cohort score lower on

the factor Openness and less open for new

experience and intellectual pursuits. But we may

conclude that students in exact/technical sciences are

in principle open for learning 21

century skills.

4.2 Psychological Assessment

Procedure at TUDelft 1953-1957

For most academic studies at Universities in the

Netherlands there is no entrance exam. Only a

limited studies with capacity problems have a

special admission procedure. Students with low

grades at the final exam at their secondary school

have a lesser chance to pass this special entrance

procedure than students with higher grades. As a

consequence many students start a study with

insufficient intellectual capabilities. The assumption

is that Universities have the opportunities to select

the students for the higher years based on their

academic performance in the first year. It is a well-

known saying that students with limited intellectual

capabilities can compensate this shortage by hard

working. To research the impact of personality

characteristics, motivation, social environment on

academic performance and study-success or –failure

a huge assessment procedure was performed in 1953

at Delft University of Technology. All students in

the first, second and third year got a psychological

assessment. They were supposed to fill in

questionnaires corresponding with test assessing

cognitive abilities, and personality. Next all students

were interviewed by student counsellors. The

outcomes of all these assessments were correlated

with the results of exams of students and results of

students at their secondary school. It proves that

there was a significant correlation between academic

performance at the University and final results at

secondary school for the subjects mathematics and

physics. Not a big surprise taking into account that

the subjects mathematics and physics play an

important role in technical studies at Delft

University of Technology. From table 2 we can see

that students with lower grades for the subjects

mathematics and physics have a low study progress

in the first year and students with high grades have a

high chance of study success. For the middle group

of about 50 % it was difficult to predict study

success or –failure. A disappointing result was the

fact that the outcome of psychological assessment

has a very limited added value to the prediction of

study-success or –failure. Apparently the impact of

personality, social environment were already

included in the performance at secondary school.

Table 2: Study-progress/delay/dropouts in percentages

crossed with average score mathematics/physics grades at

school, after two years of study, cohort 1953.

Studyprogress

≥ 150%

0% 4% 11% 15% 7%

Studyprogress

≤ 150%

0% 8% 13% 4% 0%

Delayed students

with Incomplete

first year

2% 2% 1% 1% 0%

Dropouts during

second year

2% 6% 2% 0% 0%

Dropouts during

first year

2% 9% 9% 1% 0%

Average math/

physics grades at

school exam

5-6 6-7 7-8 8-9 9-10

Table 3: Study-progress/delay/dropouts crossed with

number of passed exams in the first exam-session.

Studyprogress

≥ 150%

0% 1% 7% 14% 16%

Studyprogress

≤ 150%

3% 8% 8% 4% 3%

Delayed students

with Incomplete

first year

2% 1% 1% 0% 0%

Dropouts during

second year

2% 6% 1% 1% 0%

Dropouts during

first year

6% 9% 5% 0% 0%

Numbers of exams

passed successfully

in first period

0 1 2 3 4

From table 2 can be observed that the dropout

rate in the first two years is 31%. After two years of

study the dropout rate was 31%. In total the dropout

rate was 43%. About half of the dropouts started

another study at TUD or at a Polytechnique School.

The function of the first year was orientation and

selection. But still 10% of the students started a

second year and dropout during that year and

additionally 12% of the students drop out later in the

study. At this moment the University spend a lot of

time and effort to stimulate students to take their

decision of dropout as soon as possible. At this

moment it is in general not possible to start the

second year before completing the first year. There

are special programs and even a special MOOC

enabling student to get a better orientation of the

study.

A special problem with presented tables is that

some students enrolled in the study but never show

up. In the early days even no-show students could

get a student loan for the whole year. So no-show

behaviour has financial consequences. A similar

problem can be observed in the cohort of MOOCs

students. In the past the group of slow starters made

a successful start in the second year. The phenomena

of restarting students is not observed in the cohort of

MOOCs students. One of our recommendations for

MOOCs designers is to take care of slow starters,

students who need a longer adaptation time or to

apply the spiral principle of in increasing

difficulties.

Students with a low respectively high average

score on the subjects mathematics/physics have a

low chance/high chance to complete the study

successfully. But students with modal scores are

rather unpredictable. The hope was that

psychological personal improved prediction of

study-success or failure for the middle group.

Unfortunately this was not the case.

From Table 3 can be observed that dropouts miss

more than half of the exams already in the first

session. Some students with bad results in the first

year give themselves second start but have again bad

results in the first exam session. The phenomena of

restarting students is not observed at MOOCs.

4.3 Pre-University Calculus

July-September 2015

TUDelft designed a special MOOC called Pre

University calculus. The goal of the MOOC was to

refresh mathematical knowledge of students before

they start their academic study at the University and

to teach students missing topics of the mathematics

at secondary schools. The teaching material was

composed of small, 10 minutes video fragment with

a lot of simulations, video lectures, gaming etc. The

focus was on applications of mathematics.

Figure 1: Screenshot of the Pre-Un. Calculus MOOC.

Students were stimulated to cooperate by

interacting via special blogs. Students are stimulated

to define questions stimulated by the lectures and the

learning material. Fellow students in the network are

invited to answer the questions and commenting

solutions in the course forum. This is common

practice in mathematics learning.

Figure 2: Topics of the Pre-Un. Calculus MOOC.

In 2015 more than 26.000 worldwide followed

the course and via data analytics the performance of

students was analysed. We will report some main

findings from participating students, first students

from TUD and after that students from all over the

world. Aspirant students from TUD were stimulated

to take part in the special MOOC. Usually lectures

were offered during the summer holidays after the

school exam and just before the start of the academic

year. The MOOC was supposed to attract more

students because there was no need to come to the

University and also no need for teachers to lecture to

lecture during the summer holidays.

In total 794 TUD students enrolled in the course,

420 (53%) of them attempted an exercise and 40

(5%) attempted all exercises. Only 46 students

passed the final exam successfully. From the

interviews it proves that most students had the

feeling that they master the topics already. Just

before the start of the academic year, other activities

got a high priority as finding housing in Delft,

increasing the income by performing a job or just

taking holidays after passing the school exam

successfully. Students didn’t feel the need to

participate in the MOOC.

From table 4 it can be observed or computed that

there is no significant difference in mathematics

grades between students that enrolled and students

that did not enrol.

In total 27.186 students enrolled in the course,

4.150 students attempted to make an exercise and

only 273 attempted all exercises.

Table 4: Distribution of students who enrolled and didn’t

enrol in the MOOC.

Grade math at school exam

5 6 7 8 9 10

# of students that

did not enrol

101 764 1017 703 332 52

Number of

students that

enrolled

32 173 200 166 63 10

All students were requested to fill out some

questionnaires. We report the results from students

who showed some activity in the course. About 59

% of the students took part in the course from begin

to the end, 18 % browsed through the topics and

videos and 4% mainly watched the videos, 7%

mainly did the exercises.

Students on average stated they rarely

participated in study groups, connected to other

students, posted a comment or a question, or looked

at the forum. They also rated their perceived support

from either other students or staff rather low.

However, they did not feel that feelings of

loneliness or missing interaction negatively affected

them in the course. Neither did they feel that lack of

feedback negatively affected them. Rather, they

rated the feedback as good. It seems, students in

general neither expect nor need a lot of interaction

with either students or staff. We stress that this only

holds for students taking part in the course. From

students who didn’t take part or dropout in an early

stage we have no information.

4.4 Experiment Mathematics at TUD

during 2015

It was decided that teaching mathematics at TUDelft

will change dramatically from 2015 on. The dropout

rate had to be reduced. There should be more focus

on applications of mathematics, self-discovery,

teaching 21

century skills such as networking,

cooperation via social media etc. In a first

experiment 370 students studying Civil Engineering

got mathematical courses new style. First the

didactical principle flip the class room was applied.

For many years students got their lectures

mathematics in big lecture halls followed by making

exercises in small classrooms. Now the order has

been changed. Students are supposed to study video

lectures and make exercises before they meet the

teachers and fellow students in small classrooms to

discuss problems and outcomes of the exercises. In

the video lectures there was a focus on applications

of mathematics, self-discovery activities of students.

They are stimulated to cooperate in study groups.

The MOOC Pre-Un Calculus was integrated in video

lectures. Many new videos were added especially on

simulation and applications of mathematics.

We will now report some of the results from

surveys and interviews. It proves that on average

only 20% of the students viewed the video lectures

before the classroom meeting. So the assumption

that students activate the right pre-knowledge was

not correct. That poses a didactical problem for the

teacher. Repeating or summarising the homework is

boring for students making their homework and will

not stimulate viewing video lectures in the future.

One of the critical comments of students was that the

video lectures are interesting but are not direct

integrated with the learning material/book. Viewing

the video lectures maybe will result in a better

understanding of mathematics in general but it is not

necessary to pass the exam successfully. One of the

goals of the new mathematics teaching was to focus

on real applications. Then the applications should be

part of the exam and not additional. In the

compulsory homework assignments it is possible to

make links with some topics discussed in the video

lectures.

At start about 80% of the students visited the

classroom meeting and at the end it was about 65%.

The classroom meetings were highly appreciated.

Especially the moments the teacher was lecturing or

explaining difficult topics. It proves that teachers

were better in raising questions or pointing to typical

problems. Individual or group wise making

assignments during the classroom lectures was

highly appreciated. Because there was a meeting

place for the students in the classroom there was no

need to cooperate via social media. Teachers were

asked if and how many times during a lecture they

provide opportunities for students to discuss topics

or cooperate. In the next table we summarise the

results.

Table 5: Overview of opportunities teachers offered for

discussion or cooperation.

Opportunities

for discussion or

cooperation

Never 1 time

2-3

times

4 or

times

11% 22% 34% 33%

Teachers like to teach and play a central role

during the lessons. Many students come to the

lessons and expect that the teacher lectures. Parallel

to the classroom meetings there was a digital Lab

room (My.MathLab). Students were supposed to

make assignments and got feedback in an automated

way. It proves that many students joined their effort

and meet each other to make assignments.

An interesting option for students was to provide

feedback during the lectures using their smart

phones or laptop. One of the start up companies

from DUT, FeedBackFruits developed a tool for

mobile devices which enables students to ask

questions during a lecture. The questions are

visualised on a display in front of the teacher. It

proves that students consider the tool as an

interesting option. But defining questions takes some

time and usual the lecture is going on. It is up to the

teacher if he introduces breaks to allow and discuss

questions. Some questions can be used by the

teacher to summarize a topic. If there are many

questions about the same topic the teacher has the

option to explain the topic in an different way or to

come up with some examples. To support the

didactical approach “flip the classroom”, the start up

company FeedBackFruits was requested to generate

a plugin to make the “questioning tool” available via

edX, one of the MOOCs consortia. A layer of new

functionalities was developed over the edX platform.

This enables students to make specific notes inline

and make digital notes out of it. The plug-in also

allows users to add new content to the course and

share a message information board way questions of

the students can be considered as an online feedback

system for the teacher.

5 CONCLUSIONS

The main goals of this paper was to study the high

drop off of MOOCs, to find possible causes and to

develop a new didactical model. Secondly we want

to invest in how far if it is possible to teach students

21st century skills. We used the data from two

experiments at TUDelft.

In the first experiment, TUDelft designed a

special MOOC called Pre University Calculus. The

goal of the MOOC was to refresh mathematical

knowledge of students before they start their

academic study and to teach students missing topics

of the mathematics teached at secondary schools.

The teaching material was composed of small, 10

minutes video fragment with a lot of simulations,

video lectures, gaming etc. The focus was on

applications of mathematics. Students were

stimulated to cooperate by interacting via special

blogs. In 2015 more than 26.000 worldwide

followed the course and via data analytics the

performance of students was analysed. Only a

minority of 5% of the students completed the course

successfully. These students stated in surveys and

interviews that they prefer to work individually and

not in groups or digital community. Unfortunately

the early dropouts were not surveyed. From the

questionnaires and interviews we found many

reasons for dropout behaviour. From the available

data can be concluded that there were only a few

network activities and cooperation between students

via social media. Teaching students 21

century

skills will not take place automatically by using

MOOCs but special didactic models are needed.

Secondly it was decided that teaching

mathematics at TUDelft will change dramatically

from 2015 on. There should be more focus on

applications of mathematics, self-discovery,

teaching 21st century skills such as networking,

cooperation via social media etc. In a first

experiment students studying Civil Engineering got

mathematical courses new style. First the didactical

principle flip the class room was applied. For many

years students got their math lectures in big lecture

rooms followed by making exercises in small

classrooms. Now the order has been changed.

Students are supposed to study video lectures and

make exercises before they meet the teachers and

fellow students in small classrooms to discuss

problems and outcomes of the exercises. In the video

lectures there was a focus on applications of

mathematics, self-discovery activities of students.

They are stimulated to cooperate in study groups. It

proves that less than 20% prepared the lessons by

viewing the video lectures in advance. Most students

reported that lack of time and motivation was the

main reason. And they expect that the teacher will

summarise the main topics in the lessons so that they

are able to follow the lessons. Following the lessons

is important for the students because they expect the

teacher will provide essential information needed to

pass the exam successfully. Students cooperated via

the digital Lab making homework together. In the

lessons there was less cooperation also because the

teachers didn’t provide the opportunity. During the

lessons and video lectures students were able to give

comment or asking questions. This provides

essential feedback for the teacher and information

for the evaluation of the course. Again we to

conclude that blended courses don’t guarantee that

students learn 21

century skills.

Students mathematics and computer science got

a psychological assessment using the Big Five

personality test. From the results it proves that

students have the abilities to learn the 21

century

skills but this will not happen automatically. A

special didactic model is needed.

A comparison was made between a huge

assessment study at TUDelft during 1953-1957 and

recent experiments at TUDelft. It proves that over

the years 40% of the students dropout. We reported

many causes based on surveying and interviewing

students.

As a final conclusion we state that also regular

courses have high dropout rates varying around

40%. Many attempts to reduce the high dropout rate

were not successful over the years. MOOCs show

even higher dropout rates and we conclude from the

outcomes of surveys and interviews that lack of

cooperation in networking, lack of social control of

peer students and inability to manage the study were

the main causes of high dropout rates.

ACKNOWLEDGEMENTS

We thank Ingrid Vos providing me with the results

of evaluation of the experiments at TUDelft. We

thanks the colleagues of the FETCH project for their

valuable help.

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