Motivation in a MOOC: a probabilistic analysisof online learners’ basic psychological needs

Tracy L. Durksen1 • Man-Wai Chu2 •

Zaheen F. Ahmad3 • Amanda I. Radil4 •

Lia M. Daniels4

Received: 31 May 2015 / Accepted: 7 December 2015

� Springer Science+Business Media Dordrecht 2016

Abstract Self-determination theory (SDT) is one of the most well-known

approaches to achievement motivation. However, the three basic psychological

needs of SDT have not received equivalent attention in the literature: priority has

been given to autonomy, followed by the need for competence, with research into

relatedness lacking (Bachman and Stewart in Teach Psychol 38: 180–187, 2011.

doi:10.1177/0098628311411798). One new educational setting where relatedness

may be particularly important is massive open online courses (MOOCs), which

provide unprecedented opportunities for either relatedness or isolation. The purpose

of the research was to use Bayesian networks (BN) to establish probabilistic

relationships between learners’ basic psychological needs in the context of one

& Tracy L. Durksen

tracy.durksen@unsw.edu.au

Man-Wai Chu

manwai.chu@ucalgary.ca

Zaheen F. Ahmad

zfahmad@ualberta.ca

Amanda I. Radil

radil@ualberta.ca

Lia M. Daniels

lia.daniels@ualberta.ca

1 School of Education, University of New South Wales, John Goodsell Building, Sydney,

NSW 2052, Australia

2 Werklund School of Education, University of Calgary, 2500 University Dr. NW, Calgary,

AB T2N 1N4, Canada

3 Department of Computing Science, University of Alberta, 2-21 Athabasca Hall, Edmonton,

AB T6G 2E8, Canada

4 Department of Educational Psychology, University of Alberta, 6-102 Education North,

Edmonton, AB T6G 2G5, Canada

123

Soc Psychol Educ

DOI 10.1007/s11218-015-9331-9

MOOC. The majority (59 %) of participants (N = 1037; 50 % female and 50 %

male) were under 45 (age range was 18–74 years). This sample represented

approximately 88 regions and countries. Participants completed a revised Basic

Student Needs Scale (Betoret and Artiga in Electron J Res Educ Psychol 9(2):

463–496, 2011). In order to reveal the best structural understanding of SDT within a

MOOC learning environment, analysis of the data involved the development of a

BN probabilistic model. The best fitting BN model included autonomy, competence,

and relatedness—resulting in a probabilistic accuracy of 77.41 %. Analyses

revealed participants with high autonomy had an 80.01 % probability of having a

moderate level of competence. Relatedness was distinct from the autonomy and

competence relationship. The strong inter-connections between autonomy and

competence support existing research. The notion that relatedness may be a distinct

need, at least in this context, was supported and warrants future research.

Keywords Student motivation � Self-determination theory � Online learning �Higher education � Bayesian network analysis � Massive open online course

1 Introduction

‘‘[There are] no more lecture theatres in our buildings because we think they

can be done just as well online’’—University Professor (Griffits and Raschella

2014).

Massive open online courses (MOOCs) are a new type of online learning

environment that allow an unlimited number of participants from anywhere in the

world free access to course content often delivered by world renowned experts.

Whether MOOCs are the ‘game-changer’ for developing countries (Warusavitarana

et al. 2014) or perceived as a digital threat to education (Noble 2001), something

motivates hundreds of thousands of learners to participate. This study provides

empirical evidence to aide the considerable commentary and informal debates that

surround the issue of MOOCs—an area of limited, but growing, peer-reviewed

research.

In the current study, we applied Ryan and Deci’s (2000) self-determination

theory (SDT) as a framework for analyzing the motivation of MOOC learners.

According to SDT there are three basic needs that, when met, give rise to optimally

motivated behavior: autonomy, competence, and relatedness. In education, research

has focused on autonomy and competence, largely ignoring relatedness—a need that

may be at a greater risk of being ignored within an online learning environment.

Even though empirical research on MOOCs is only beginning to emerge, SDT has

already established itself as a promising theory guiding both case study (e.g.,

Hartnett et al. 2014) and pre/post research designs (e.g., Beaven et al. 2014). Adding

to this body of research, our study asked: What relationships exist between the basic

psychological needs of MOOC learners via a Bayesian network (BN) analysis?

Typically, motivation or engagement in MOOCs has focused on very basic

indicators such as number of logins, downloads, and unstable Internet connections.

T. L. Durksen et al.

123

By using SDT as a conceptual framework and performing probabilistic analysis, we

went beyond these basic indicators of participation to develop a probabilistic

description of learners’ satisfaction of basic psychological needs within the context

of a MOOC.

1.1 Massive open online courses: a brief history

MOOCs are relatively new, yet have already impacted our ideology of learning

within an online environment (Liyanagunawardena et al. 2013). While some have

argued that MOOCs have revolutionized education by providing learners with

classes from the most prominent professors at no cost (Cusumano 2013), others

focus on MOOCs as a new application of old learning theories (Siemens 2004).

Even in their short history, MOOCs have morphed from their original design and

intent, have attracted learners from around the world, and have gained notoriety at

world-class educational institutions (Clarke 2013).

In 2012 Siemens distinguished between the original connectivist massive open

online courses (cMOOCs) and newer edX and massive open online courses such as

those offered by Coursera� (xMOOCs). cMOOCs were originally developed based

on connectivist pedagogies that take advantage of the online environment for

connecting students (Siemens 2012). According to the tenets of connectivism,

students learn best by socially engaging with the material and sharing their ideas

with others. Connectivism proposed that knowledge is broadened when an idea is

critically presented, re-considered, and used as the basis of connections and

networks between learners. cMOOCs were built on this shared belief that

knowledge can be created through spontaneous and practical sharing of ideas.

That cMOOCs expected knowledge to be created, gathered, and re-distributed

was both their biggest advantage and downfall. Universities could see the advantage

of reaching thousands of learners but needed to do so with more conventional and

structured methods like traditional university courses, thus the xMOOC was born.

xMOOCs are just as massive, open, and online as cMOOCs but they are

philosophically different. xMOOCs tend to provide materials for students to learn

such as notes or pre-recorded video segments, use discussion boards or forums, and

assess learning through quizzes or tests (Clarke 2013). Even within this more

traditional format, there is evidence of spontaneous connections, groups, artifacts,

and learning, but these are not the driving force of an xMOOC. Thus, for xMOOCs

it may be true that ‘‘[i]n spite of all the hype about interactivity, ‘lecturing’ a la

MOOCs merely extends the cliche of the static, one-sided lecture hall, where

distance learning begins after the first row’’ (Newstok 2013 p. 18).

We believe that inquiries into motivation, however, can reveal the influential

factors that underlie connectivity in learning—regardless of the context. Because

now tens of thousands of learners may simply be exposed to ‘‘good’’ or ‘‘bad’’

xMOOCs the same way traditional students have endured ‘‘good’’ and ‘‘bad’’

lectures, seminars, and labs, it has become all the more important to understand how

the basic psychological needs are related to each other in an xMOOC.

Motivation in a MOOC: a probabilistic analysis of online…

123

1.2 Self-determined learning

We used SDT as the guiding framework for our research because the theory

positions motivation for learning on a continuum (from amotivation to extrinsic

motivation and towards intrinsic motivation) within an immediate social context—

ideally one with opportunities for need satisfaction. Given that three basic

psychological needs—autonomy, competence, and relatedness—have been univer-

sally recognized as intrinsic motivators (Deci et al. 2001; Ryan and Deci 2000), the

current study aimed to contribute empirical evidence of a good model of SDT

within a MOOC learning environment. We also explored a fourth need—

belonging—since a sense of belonging may be achieved through an online

community with a shared interest in course content in a way that is different from

relatedness.

Researchers have explored need satisfaction across life domains (Deci and Ryan

2008; Milyavskaya and Koestner 2011) and learning environments (e.g., online;

Yang 2014). For example, Chen and Jang (2010) found SDT particularly suitable as

a framework for studies of motivation in online settings, given the apparent

connections to autonomy (e.g., flexible learning context), competence (e.g., content

and technological skill development), and relatedness (e.g., computer-mediated

social interactions). Autonomy has been defined as an individual’s experience of

volitional and freely chosen behaviors (Niemiec and Ryan 2009; Oliver et al. 2008;

Deci and Ryan 2008; Faye and Sharpe 2008). The need for competence, however,

can be satisfied by experiencing ‘‘behavior as effectively enacted’’ (Niemiec and

Ryan 2009, p. 135). For example, a learner may gain competence through a

challenging activity (i.e., neither too difficult nor too easy) that allows an individual

to feel effective in his or her environment (Faye and Sharpe 2008). The

psychological need for relatedness is experienced through feeling connected to

others within supportive or close relationships (Oliver et al. 2008; Reis et al. 2000),

while belonging may be more likely satisfied through collective and communal

shared interests (Baumeister and Leary 1995).

Autonomy-supportive teachers can maximize ‘‘students’ perceptions of having a

voice and choice’’ (Niemiec and Ryan 2009, p. 139). While MOOCs offer students a

platform to work independently, this does not ensure that students feel autonomous.

Research from traditional classrooms found students with autonomy-supportive (as

opposed to controlling) teachers are more intrinsically motivated, more creative,

show more interest, and are more likely to persevere (Niemiec and Ryan 2009).

Teachers in a MOOC environment can also instruct in an autonomy-supportive

manner. For example, Yang (2014) asserts that the format and flexibility of online

courses can easily be used to encourage a students’ sense of autonomy. Deci and

Ryan (2008) found that an autonomy-supportive learning environment leads to

better outcomes (when compared with a controlling climate) and that an absence of

autonomy support can substantially decrease students’ satisfaction of basic

psychological needs.

Niemiec and Ryan (2009) emphasize the pairing of autonomy and competence as

essential for intrinsic motivation. Teachers and the learning climate that they create

not only influence a student’s sense of autonomy, but also student’s competence

T. L. Durksen et al.

123

(e.g., through task difficulty and feedback). Like self-efficacy (Bandura 1997), a

student’s need for competence can affect effort and achievement outcomes

(Bachman and Stewart 2011). Unlike self-efficacy, which defines confidence in

the ability to carry out a task in the future, student competence focuses on present

capability in an immediate social context. While self-efficacy can be measured

through questions that begin with ‘‘How confident are you in your ability to,’’ SDT

researchers interested in measuring competence have used items such as ‘‘I am

capable of learning the material in this course’’ (Perceived Competence for Learning

Scale; Williams and Deci 1996).

Students’ learning benefits from student–student and teacher–student connec-

tions, however relatedness has been an understudied component (Bachman and

Stewart 2011). Moller et al. (2010) state that positive trusting relationships define

the psychological need for relatedness—one that requires interactions with ‘‘real

human[s]’’ (p. 760). An environment that promotes teacher–student relatedness can

increase both teacher and student engagement, and consequently learning (Klassen

et al. 2012, 2013). In traditional classrooms, teachers who intentionally and

regularly provide one-on-one or small group support can create positive experiences

that students’ may drawn upon when faced with learning or interpersonal challenges

in the future (Klassen et al. 2012). While there is evidence showing how online

learning can meet students’ needs of autonomy and competence, the challenge often

remains with satisfying the need for relatedness through computer-mediated

interactions.

While relatedness tends to address the teacher–student relationship, belonging

may best be satisfied through a school, classroom, or online community of learners.

Well-designed learning communities, off- and online, have the potential for

encouraging relatedness and a sense of belonging through the development and

maintenance of genuine and caring relationships. A virtual community can provide

the motivational context for belonging with membership in a social space,

irrespective of actual physical locality (Ainley and Armatas 2006). Although

relatedness and belonging have been used interchangeably in the literature—with

the need for relatedness defined as the need for experiencing belonging (Osterman

2000), Betoret and Artiga (2011) considered belonging to be a separate need for

students.

1.3 Bayesian network analysis

Bayesian network (BN) analyses are applied in many disciplines to represent and

model different theories (Darwiche 2010). For example, BN analysis has been

successfully applied to SDT by researchers looking at reasoning patterns in

competitive sports (Fuster-Parra et al. 2014). The strength of this model is that it

allows for a representation of the relationships in a domain while using probabilities

to characterize the strength of those relationships (West et al. 2010). In the current

study, we chose BN analysis as one way to find a model that represents the extent to

which learners’ basic psychological needs were met in the context of learning in a

MOOC.

Motivation in a MOOC: a probabilistic analysis of online…

123

BNs are probabilistic graphical models that represent conditional dependencies in

a framework that will yield the highest expected information on the flow of

influence between variables (Koller and Friedman 2009; Russell and Norvig 2003).

They are typically represented as directed acyclic graphs (DAG), which represent

data using objects called nodes and edges. A node is a variable (e.g., observable or

latent) and an edge is a connection (e.g., one directional dependency) between two

nodes. Data can flow from one node to another along an edge or set of edges. For

instance, node A has an edge with node B, and node B also has an edge with a node

C. Since information can flow from nodes A to C through node B, a path exists

between nodes A and C.

DAGs for BNs are a compact representation of a complete joint probability

table given a set of random variables. DAGs, as the name suggests, are composed of

directed and acyclic graphs. A directed graph is one where the edges have an

explicitly defined direction for the passage of information between two nodes. For

example, a directed edge from node A to node B shows that information can be

passed from A to B but not from B to A. An acyclic graph is one where, given a set

of nodes, the edges between nodes exist in a manner such that no cyclic path exists

between the nodes, ensuring a one-way flow of information. The nodes and edges

represent the random variables and the dependencies between them. If there is a

directed edge from node A to node B, it shows that the variable of node A has a

causal effect on the variable of node B. On the other hand, when we look at it in the

other direction, the directed edge shows the variable of node B only has an indirect

effect on the variable of node A; it implies an effect only if evidence of node B is

given. A Bayesian Network represents these relationships over a set of many

variables so that calculating these joint probabilities become computationally easier.

The heart of BN is the inversion formula, which follows Bayes’ (1763) rule:

P Hjeð Þ ¼ P ejHð ÞP Hð ÞP eð Þ

In the formula, P(H) represents the probability that the hypothesis is true and is also

known as the prior; P(e) represents the probability that the evidence observed is

true; P(e|H) indicates the probability that e will materialise if H is true; and P(H|e) is

the probability that the hypothesis is true if the evidence is also true. Unlike null

hypothesis significance testing, Bayesian analysis allows for direct statements about

the probability of researchers’ parameters of interest based on a prior probability

distribution chosen before analysis (Zyphur and Oswald 2015). In this study, we

chose to estimate prior distributions from the data with maximum likelihood as

empirical priors (i.e., empirical Bayes estimation).

Our goal was to construct a BN model that reflected a representation of joint

probability distributions and included a collection of conditional independent

statements to help guide inferences of the networks (Russell and Norvig 2003). In

the current study, we used structure learning, an exploratory analysis method that

can represent joint probability distributions. Exploratory data analyses often require

the testing of many models with modifications so that the best fitting model is found.

Structure learning borrows principles from machine learning, which uses an

T. L. Durksen et al.

123

exhaustive and automated search technique that examines all possible connections

between the nodes in the model (Koller and Friedman 2009; Pearl 2000). Since

employing an exhaustive search would be computationally infeasible, we used a

local hill search method to find the locally best model. Machine learning is a field

that deals with learning from data and making predictions on new data by producing

models using observed data. BNs are one such machine learning technique that uses

data to model beliefs.

The goal of this paper is to represent the relationships between the different

aspects of SDT. This technique is suitable because it allows for accurate modelling

of the basic psychological needs forwarded by SDT and inferences on how

observing evidence on one psychological need may affect other psychological

need(s). An advantage of BN is its ability to automate the modelling process and

reduce the complexity of the final model. Unlike most other techniques (e.g., neural

networks) used to make predictions based on patterns in data, a BN is more

transparent. It provides an easy-to-understand model of correlations in the

observation by presenting relationships between variables as simple conditional

probabilities, thus allowing experts to apply domain knowledge to the problem.

Therefore, we can use BNs to verify whether a hypothesized model is able to

explain real observations.

2 Method

2.1 Data collection

Data were collected during a Canadian university’s first offering of an xMOOC. The

introductory course in dinosaur paleobiology (Dino 101) consisted of twelve lessons

on non-avian dinosaurs. Three versions of the course were available for learners: (1)

universally for free (no exams); (2) universally for course accreditation with a

modest fee; and (3) enrolled at the university offering the course. Course content

and lessons were offered through interactive digital activities as well as videos. The

course included formative assessments such as quizzes embedded in the videos and

summative assessments such as end-of-chapter multiple choice quizzes and

university-built online exams (Chesney 2013a). In total, 23,252 people enrolled in

Dino 101 during its inaugural offering in the last semester of 2013. We were

interested in studying motivation and emotions of students in Dino 101 from a

psychological perspective that is not captured by the usage and demographic

surveys administered by the MOOC.1 Instead, we invited learners to complete one

questionnaire at the conclusion of Dino 101 that included a number of scales such as

Task Value Questionnaire (Wigfield 1994), Achievement Emotions Questionnaire

(Pekrun et al. 2005) and the Academic Motivation Scale (Vallerand et al. 1992).

1 The authors were not Dino 101 course instructors or students, but were provided access as researchers.

Motivation in a MOOC: a probabilistic analysis of online…

123

2.1.1 Participants

Participants were recruited through a voluntary link posted on the main page of the

Dino 101 course website. The link was available for approximately one month,

from the last week of the course until the official end of the course. Any student

registered in Dino 101 who logged in during that time would have access to the link

regardless of how frequently they had participated in Dino 101. Thus, although it is

more likely that respondents were learners still actively participating in Dino 101,

all registrants had the ability to participate. From this link, learners were connected

to the online questionnaire (via Survey Monkey�). Study participants (511 female,

515 male, 11 unspecified) were Dino 101 learners (N = 1037) who ranged from 18

to 74 years of age (59 % under 45) from approximately 88 regions and countries.

Prior to enrolling in the MOOC, 34.1 % of participants already achieved a

university degree. Similar demographic details were considered representative of a

respondent group of 3500 Dino 101 learners (Chesney 2013b).

2.1.2 Measures

With SDT as our framework, we chose to use learners’ responses to a revised

version of the Basic Student Needs Scale (BSNS; Betoret and Artiga 2011). This

scale was initially based on the Basic Psychological Needs Scale (BPNS; Ilardi et al.

1993), which consisted of three components (autonomy, competence, and related-

ness). Betoret and Artiga (2011) later added a fourth component (belonging) to

reflect Goodenow’s (1992) work on student’s feelings toward belonging to a class or

peer group. The relatedness items from the BSNS scale were replaced with the

relatedness items from the Basic Need Satisfaction at Work scale (BNSW; Deci

et al. 2001). The original BSNS relatedness items focused mostly on the relationship

between students and the teacher, while the BNSW relatedness items were more

relevant to relationships within the entire online community.

The four original subscales of the BSNS were reliable, as indicated by

Cronbach’s alphas (.65 to .86; Betoret and Artiga 2011). The relatedness subscale

(from the BNSW scale; Deci et al. 2001) that replaced the BSNS relatedness scale

was also reliable (a = 0.84). Table 1 presents the revised scale items that were used

in the current study, with subscale reliabilities ranging from .61 to .80. The

reliability for the revised relatedness scale used in the current study was less than

optimal (.61); likely due to the revised content and limited number of items included

(3 instead of 8 items). The low Cronbach’s alpha for the relatedness scale, however,

did not have an impact on the BN analysis. Alphas (and correlations) are not directly

related to BN analysis because the alpha and correlation show a relationship

between a group of items (alpha) and all of the items (correlation), but BN analysis

looks at each item individually.

Original subscale items were re-worded to prompt learners to reflect upon their

contextual experiences in the Dino 101 course and rated on a 7-point Likert scale

(1 = Strongly Disagree and 7 = Strongly Agree). Autonomy items measured a

learner’s internal acceptance of, and engagement with, his or her motivated behavior

(e.g., ‘‘I have been able to freely decide my own pace of learning during Dino

T. L. Durksen et al.

123

101’’); competence items measured a learner’s ability to take on and master

challenging tasks (e.g., ‘‘I felt I was capable while learning in Dino 101’’);

relatedness items measured a learner’s feeling of being connected to or understood

by others (e.g., ‘‘I really liked the people I learned with in Dino 101’’); and

belonging items measured a learner’s feeling of being part of a group (e.g., ‘‘There

has been a strong feeling of friendship in Dino 101’’).

2.2 Data analysis

In order to reveal the best structural understanding of SDT using data based on

learning within one MOOC, we developed a probabilistic model using BN analysis.

Given the increasing MOOC offerings through universities, this approach allowed

for a probabilistic analysis of learners’ psychological needs satisfaction that can be

used when highlighting practical implications for online learning. In the current

study, we used Murphy’s (1997–2002) Bayes Net Toolbox to write the program in

Matlab.2 Each node in the model represented an item from the revised BSNS. After

the items were selected, we deleted 8.3 % of the cases (listwise deletion; Acock

Table 1 Items and subscale reliabilities for the Basic Student Needs Scale (revised)

Subscale items (with Cronbach’s alphas)

Autonomy (a = 0.80)

1. I have been able to freely decide my own pace of learning during Dino 101 (A1)

2. I was able to freely choose the tasks I will do during Dino 101 (A2)

3. I felt I was capable of deciding about how to learn and work in Dino 101 (A3)

4. Dino 101 allowed me to work as independently, collaboratively or cooperatively as I wanted (A4)

Competence (a = 0.71)

5. I felt capable while learning in Dino 101 (C1)

6. I had the chance to show my capacities during Dino 101 (C2)

7. I was able to learn new and interesting material in Dino 101 (C3)

8. I felt competent enough to meet the challenges and tasks posed by Dino 101 (C4)

Relatedness (a = 0.61)

9. I really liked the people I learned with in Dino 101 (R1)

10. I pretty much keep to myself in Dino 101a (R2)

11. People in Dino 101 were pretty friendly towards me (R3)

Belonging (a = 0.80)

12. There has been a strong feeling of friendship in Dino 101 (B1)

13. I felt at ease in Dino 101 (B2)

14. Being in Dino 101 felt like belonging to a large family (B3)

15. I got the feeling that we formed a large team in Dino 101 (B4)

16. I will remember my classmates from Dino 101 affectionately in the future (B5)

a Reverse scored

2 Contact the authors if you are interested in the code that generated the results in this paper.

Motivation in a MOOC: a probabilistic analysis of online…

123

2005; Gravetter and Wallnau 2009) because the participants did not respond to any

of the items on basic needs. The analysis continued with the data collected from the

remaining 950 participants.

The structure learning algorithm we used to construct the model systematically

tested each possible link and calculated fit indices for each model using a local hill

search method (Koller and Friedman 2009). The indices used in the study were the

Bayesian Information Criterion (BIC; Schwarz 1978) and accuracy3 scores. BIC

scores are a criterion for model selection among a finite number of models (see

Schwarz 1978 for formulas). When developing models, it is possible to increase the

likelihood function by adding parameters, but this may result in over fitting the

model to the data. BIC scores offer a solution by introducing a penalty for each

parameter of the model. Often the largest BIC score among a set of models is

considered to be the best fitting model for the data.

In calculating the BIC, our goal was to achieve a score that represented the best

fit of a model. Each time we represented a portion of the model correctly, we

increased the score. At the same time, we were looking for a structure that provided

good accuracy without requiring too many edges between nodes. We also lowered

the score by penalizing structures that had an increased number of edges. We

penalized structures with more edges since the increased number of edges may lead

to over fitting (i.e., the model being so complex that is describes the random error

instead of underlying relationships).

In the current study, the penalty was reflected through the lowering of the BIC

score. Specifically, we took the Maximum Likelihood Estimate (MLE) and

subtracted it by the penalty (i.e., a larger BIC score indicated a better fitting model).

It is important to note that some programs will add a penalty score (i.e., a lower BIC

score would represent a better fitting model) to -MLE. This often leads to

confusion, so it is essential for researchers to include which equation is being used

when comparing models. For our equation, we maximized over the log likelihood

(MLE - penalty), while others may choose to minimize over the negative log

likelihood (-MLE ? penalty). Our procedure produced a larger BIC score

(indicating a better fit) while the latter would produce a smaller BIC score (also

indicating a better fit). In sum, we used MLE when calculating BIC scores (Murphy

2012) where a larger BIC score was indicative of a better fit.

3 Results

Descriptive statistics and correlations between the 16 items of the revised BSNS are

displayed in Table 2. Since the BN model is robust against low or weak coefficients,

the non-significant correlations that existed for some items did not have an impact

on the analyses. Items that were skewed in the current study, however, were

problematic since the low sample size on some of the scales made the model

difficult to converge. Therefore, we reduced the 7-point scale to a 3-point scale (1,

3 To calculate the accuracy score, we used * 80 % of the data (n = 750) to develop the model and then

used the remaining 20 % (n = 200) to calculate the accuracy of the model.

T. L. Durksen et al.

123

Table

2D

escr

ipti

ve

stat

isti

csan

dco

rrel

atio

ns

for

auto

nom

y(A

1–A

4),

com

pet

ence

(C1–C

4),

rela

tednes

s(R

1–R

3),

and

bel

ongin

g(B

1–B

5)

A1

A2

A3

A4

C1

C2

C3

C4

A1

6.6

3(.

87

)

A2

.65

**

6.3

1(1

.13

)

A3

.53

**

.47

**

6.2

4(1

.06

)

A4

.50

**

.48

**

.44

**

6.2

6(1

.11

)

C1

.61

**

.50

**

.56

**

.48

**

6.3

5(.

98

)

C2

.19

**

.27

**

.41

**

.29

**

.30

**

4.9

4(1

.36

)

C3

.50

**

.39

**

.45

**

.44

**

.44

**

.32

**

6.4

7(.

96

)

C4

.57

**

.41

**

.56

**

.45

**

.70

**

.31

**

.36

**

6.2

8(1

.05

)

R1

.16

**

.23

**

.24

**

.23

**

.20

**

.32

**

.23

**

.17

**

R2

-.3

5*

*-

.28

**

-.2

5**

-.2

7*

*-

.40

**

-.0

4-

.25

**

-.3

2**

R3

.12

**

.18

**

.22

**

.19

**

.20

**

.30

**

.18

**

.16

**

B1

.07

*.1

5**

.24

**

.18

**

.12

**

.36

**

.21

**

.09

**

B2

.43

**

.38

**

.48

**

.50

**

.48

**

.31

**

.45

**

.44

**

B3

.02

.11

**

.18

**

.16

**

.08

*.4

1**

.20

**

.07

*

B4

.07

*.1

1**

.19

**

.20

**

.13

**

.40

**

.22

**

.12

**

B5

-.0

2.0

8**

.10

**

.11

**

.05

.29

**

.12

**

.01

Motivation in a MOOC: a probabilistic analysis of online…

123

Table

2co

nti

nu

ed

R1

R2

R3

B1

B2

B3

B4

B5

A1

A2

A3

A4

C1

C2

C3

C4

R1

4.8

1(1

.32

)

R2

.13

**

1.9

3(1

.25

)

R3

.71

**

.17

**

4.6

9(1

.24

)

B1

.55

**

.16

**

.55

**

4.1

9(1

.31

)

B2

.27

**

-.1

8**

.25

**

.25

**

6.0

1(1

.26

)

B3

.47

**

.16

**

.48

**

.68

**

.23

**

4.0

3(1

.44

)

B4

.42

**

.12

**

.42

**

.55

**

.22

**

.60

**

4.3

8(1

.48

)

B5

.37

**

.18

**

.39

**

.60

**

.16

**

.63

**

.47

**

3.6

4(1

.45

)

Mea

ns

wit

hS

tan

dar

dD

evia

tion

sin

par

enth

eses

app

ear

on

the

dia

go

nal

*C

orr

elat

ion

sar

esi

gn

ifica

nt

at0

.05

**

Corr

elat

ion

sar

esi

gn

ifica

nt

at0

.01

T. L. Durksen et al.

123

2 = low; 3, 4, and 5 = medium; 6, 7 = high). Our solution also enhanced the

interpretability of the BN model since low, medium, and high responses to items

allowed for more general conclusions about the interconnectedness between the

components.

We first performed a structure learning algorithm for Ryan and Deci’s (2000)

three component (autonomy, competence, and relatedness) SDT which resulted in

approximating the best fitting model (see Fig. 1). This diagram has a BIC value of

-3400.1 and a probabilistic accuracy of 77.41 %. Each box (or node) represents an

item from the revised BSNS (items are listed in Table 1). The percentages in each

box represent the probabilities of learners’ response to that item and the directional

arrows connecting nodes represent the influence of one item on another. For

example, Autonomy (3) has an arrow directed towards Autonomy (1) which indicates

students’ response to Autonomy (3) has a direct effect on their response to Autonomy

(1). Additionally, Autonomy (1) also had an indirect effect on Autonomy (3), which

indicates the two items have a dependency on one another. Direct effects means we

do not need additional information to learn about the relationship between

Autonomy (3) on Autonomy (1); however, indirect effects do not describe its

influence between Autonomy (1) on Autonomy (3) without any observed evidence.

This means, if we do not know anything certain about the parameters of Autonomy

(1), then we cannot say how it affects item Autonomy (3).

In Fig. 1, all autonomy items are linked which indicates they have a dependency

on each other; similarly, all the competence items are linked. Therefore, items

measuring autonomy and competence have dependencies within their respective

groups. One of the more interesting links in Fig. 1 is between competence (e.g., ‘‘I

had the chance to show my capacities during Dino 101’’) and autonomy (e.g., ‘‘I

have been able to freely decide my own pace of learning during Dino 101’’), in

which students’ responses to the autonomy items may have an effect on their

Fig. 1 Bayesian network model of self-determination theory with autonomy, competence, andrelatedness items

Motivation in a MOOC: a probabilistic analysis of online…

123

response to the competence items. Relatedness, however, was not linked to

autonomy or competence.

We also performed an exploratory algorithm to analyse all 16 items in order to

test the inclusion of the fourth component (belonging). Figure 2 illustrates links

between four Belonging items (12, 13, 15, 16) indicating direct or indirect item

dependence on each other. An interesting link in Fig. 2 is between Competence (8)

and Belonging (16) which indicates students’ responses to the autonomy items have

an effect on their response to the competence items, which also have an effect on

some of the belonging items (i.e., 12, 13, 15, and 16). Figure 2 displays the best

fitting model for the four components (BIC = -4819.2) among all possible

diagrams. Although the accuracy value (79.63 %) of this model was slightly better

than the model presented in Fig. 1, the BIC score was much lower. Therefore, we

determined Fig. 1 (the three component model) as the best fitting model for the data.

4 Discussion

BN analysis allowed for a clear graphical representation of the relationships

between learners’ basic psychological needs as reported within the context of a

MOOC. The best fitting model was a three-component model consisting of

autonomy, competence, and relatedness. Belonging did not fit in a meaningful way

and thus was excluded from the final model. In this discussion we focus on the

closeness of autonomy and competence, the separation of relatedness, and why

belongingness did not make a meaningful contribution.

Although relatedness was included in the overall model, it was distinct from

competence and autonomy (see Fig. 1). The lack of connection between relatedness

in Fig. 1 (or belonging and relatedness in Fig. 2) to the other two basic

psychological needs provides some evidence that MOOC learners may experience

competence and autonomy support separately. The distinction may also indicate that

Fig. 2 Bayesian network model of self-determination theory with autonomy, competence, relatednesswith belonging items

T. L. Durksen et al.

123

the needs of relatedness and belonging are not being met to the same extent as

competence and autonomy. The MOOC learning context may also lack the

necessary ‘real human factors’ for relatedness or belonging (Moller et al. 2010).

High autonomy-related results were not surprising since one advantage of a

MOOC learning environment is that it provides learners with flexibility and the

opportunity to freely choose when to access learning materials. As displayed

through Fig. 1, participants with high autonomy had a high (80.01 %) probability of

having a moderate level of competence. For example, when a learner personally

chooses an assignment topic to explore, autonomy may increase, lead to more

engaged learning, and a subsequent increase in learning that contributes to

competency satisfaction. Overall, our model may provide some support for previous

suggestions that competence and autonomy are closely linked (i.e., sharing

supportive practices) and why the relationship between these constructs tends to

be more prominently highlighted through SDT research (Bachman and Stewart

2011).

Our results suggest that meeting the need of relatedness through computer-

mediated interactions can be more difficult than meeting the combined needs of

autonomy and competence, at least within this MOOC. We tied relatedness to

student–teacher interactions, a connection that was supported by Hartnett et al.

(2014) who applied SDT to online learning and revealed more themes of relatedness

connected to a teacher than to peers. For MOOCs this may be particularly

challenging as the student–teacher ratio is off the charts. Nonetheless, evidence

indeed suggests that MOOC instructors can hold both a ‘rock star’ like quality,

while appearing warm and attentive (Daniels et al. 2016; Kolowich 2014). Future

research needs to examine these interpretations of the instructor more closely,

perhaps through qualitative methodologies so that we can understand how

relatedness is supported. Measuring teacher–student interactions in MOOCs can

help provide a more nuanced understanding of the seemingly distinct nature of

relatedness and, in the process, offer suggestions for need satisfaction.

Satisfying the need for relatedness can be difficult in any learning environment.

We encourage further research that looks at the ways in which relationships among

members of the online community can contribute to the learning climate.

Researchers (e.g., Ho et al. 2014; Kolowich 2014; Waltrop 2013) recommend

classifying data according to MOOC learners’ enrolment reasons. For example,

future research about Dino 101 can explore similarities and differences across the

learners within the three course versions that were offered. Comparative studies that

take into account the type of learning environment (e.g., blended, face-to-face,

flipped, MOOC) and a range of subject offerings will also advance motivational

research. Since relatedness has also been an under-emphasized SDT need of

teachers (see Klassen et al. 2012), we recommend researchers incorporate a measure

of MOOC instructors’ level of need satisfaction, in addition to self-reported

teaching styles, in order to advance understanding of teacher motivation and gain a

more complete picture of the learners’ online environment.

Motivation in a MOOC: a probabilistic analysis of online…

123

4.1 Limitations

The results of the current study need to be considered in light of two main

limitations pertaining to the sample and survey administration. First, the current

study provided probabilistic results based on one quantitative questionnaire

completed by a convenience sample. Although our sample was representative of

Dino 101 learners, we relied on data from approximately 4 % of the total number of

learners who were enrolled in the course at any given time. Moreover, we are unable

to determine the extent to which our respondents completed Dino 101. Data from

learners who intentionally left the course part way through would provide a valuable

perspective and help inform course design efforts that promote relatedness in

connection with satisfying autonomy and competence, enhanced student learning,

and the retention of more learners. Linking motivation to the level of participation

in Dino 101 would have created a compelling case, however, that was beyond the

scope of the current study, which could not link the Survey Monkey� survey to the

Coursera� course platform. Second, we administered the questionnaire only once

and at the end of Dino 101. Future studies on motivation within a MOOC would

also benefit from longitudinal explorations that collect questionnaire outcome data

and responses at multiple time points (e.g., pre-course, during, and post-course).

This type of quantitative data paired with qualitative research using a diary method

of data collection can also provide insight into how learners’ motivational needs are

met.

4.2 Conclusion

Teachers can promote learner engagement in a range of settings, and with the ever-

changing technological landscape, motivational research is the key to ensuring we

understand how to best support learners in multiple contexts. Although MOOCs

allow for a range of learners to connect geographically, the developing practice of

technologies that rely on computer-mediated social interactions may pose a risk to

learners’ self-determined learning. While the MOOC learning environment may be

more conducive to an instructor’s competence and autonomy-supportive actions,

relatedness with thousands of learners is more challenging given the little

opportunity for genuine instructor-to-learner interactions. We tied belonging to

student–student interactions and yet found no place for this construct in the BN

model. Thus, although there are thousands of learners, this sample did not translate

the sheer magnitude of people into perceptions of belongingness. Although learners

can be technologically connected to others with shared interests, the need for

belonging may be best satisfied when a MOOC is organized into smaller

communities that are driven by social engagement and knowledge sharing.

Therefore, we recommend instructors use strategic course design and facilitation

strategies in order to provide the foundation for relatedness and feelings of

belonging among learners as they co-create their learning environment.

In this study, we explored the satisfaction of psychological needs in the context

of a learning environment that has come about through recent technological

advances. New technologies will continue to add a level of complexity to our

T. L. Durksen et al.

123

understanding of the multiple factors that influence student motivation. Though

technologies may claim to promote interactive learning, we need further compar-

ative evidence of need satisfaction with face-to-face learning environments—

specifically the need for relatedness (Newstok 2013). Research that draws attention

to the motivational need of relatedness across learning environments, and

specifically the human factor mediated by technology, will help ensure learners

and their teachers feel supported and engaged. In doing so, we can highlight the

importance of connectivism and continue to promote education as an enriching civic

transaction (Moe 2014).

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Tracy L. Durksen is a Postdoctoral Research Fellow in the School of Education at the University of New

South Wales. Her research focuses on professional learning across career phases and teachers’

interpersonal skills, motivation, and engagement. Her program of research involves studying the use of

situational judgement tests as (1) a selection method that can help assess non-academic attributes (such as

empathy and adaptability) of prospective and novice teachers and (2) an educational tool for teachers’

professional learning.

Man-Wai Chu is an Assistant Professor in the Werklund School of Education at the University of

Calgary. Her research focuses on using innovative assessments, such as interactive digital environments,

to measure students’ performance-based skills in the classroom and on standardized tests. An extension of

her research with these assessments has led her to studying students’ development of mental models and

their associations with affective variables that enhance assessment performance; this research is guided

by the Learning Errors and Formative Feedback (LEAFF) model.

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Zaheen F. Ahmad is a Master’s Student in the Department of Computing Science Artificial Intelligence

Laboratory at the University of Alberta. His primary research interests include Artificial Intelligence,

Machine Learning and Optimization.

Amanda I. Radil is a Doctoral Candidate in the School and Clinical Child Psychology program at the

University of Alberta. Her research focuses on the motivational strategies that teachers use in the

classroom, where they learn these strategies, and how to facilitate teachers’ use of adaptive strategies. She

also maintains research interests in clinical assessment and intervention and mixed methodology.

Lia M. Daniels is an Associate Professor in the Department of Educational Psychology at the University

of Alberta. Her research focuses on (1) how teachers support students’ adaptive motivation, characterized

by autonomy, relatedness, mastery, and effort and (2) how adaptive motivation is related to students’

academic outcomes including emotions and achievement.

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