Student Groups as Tutors in Information Systems Education

Students’ Perspectives on Collaboration and Outcomes

Antonis Natsis

, Pantelis M. Papadopoulos

and Nikolaus Obwegeser

Centre for Teaching Development and Digital Media, Aarhus University, Aarhus, Denmark

Department of Management, Aarhus University, Aarhus, Denmark

Keywords: Peer-tutoring, Collaboration, Research-teaching Nexus, Learning Strategies.

Abstract: The study explores the potential of the research-teaching nexus in a peer-tutoring setting. During the Fall

semester of 2016, students in an Information Systems course worked collaboratively on domain topics,

assigned to them by the teacher and created educational material for their fellow students. Students’ tutoring

role was concluding with a class presentation and a discussion session in each course lecture. The study

focuses on students’ perspectives in the collaborating groups and the audience and analyzes how learning

strategies in self-regulation, peer learning, and help seeking affect students’ experiences during group work.

Analysis of student activity revealed four distinct patterns of collaboration. Findings suggest that students

that rely more on group members for help were less satisfied by the communication among them. However,

students were in general satisfied with their collaboration, being able to adapt the activity to their needs.

Similarly, the teacher and the audience (students attending the student-tutoring sessions) evaluated

positively students’ performance as teachers.

1 INTRODUCTION

Integrating research and teaching is viewed by many

scholars as a desired goal to be achieved in higher

education settings. Once these are linked together,

they can promote learning (Brew, 2003) and an

increasing number of studies conclude that students

benefit in their learning process, when they actively

participate in research activities and are instructed

by active researchers (Healey, 2005; Jenkins et al.,

2003).

Healey (2005) proposed a generally accepted

model on the different ways that research activities

and the students’ role can be integrated in a course

(Figure 1). According to this model, students can act

as audience or as participants and the emphasis of

the curricula can be driven either to the research

content or to the research processes or problems.

When acting as audience, students learn through

publications of their teachers combined with the

teachers’ personal research experience. When acting

as participants, students are actively engaged in

research design, outcomes and publications or they

are asked to conduct their own research regarding a

topic. Furthermore, they can act as tutors for their

peers by presenting and discussing with them the

findings of their research. The types of research

activities may depend on the subject areas (Griffiths,

2004). Yet, the sum of activities in which students

are engaged during a course does not necessarily fall

into one unique category.

No matter the role students have when engaged

in research activities, it is imperative that they have

to accomplish tasks, which require the

comprehension and processing of scientific articles.

Students often face difficulties in accomplishing

such tasks. Inadequate knowledge about the

scientific domain and failure to apply appropriate

reading strategies are considered the main reasons

for performing inefficiently in such tasks (Kolić-

Vrhovec et al., 2011; McNamara et al., 2007).

Instead of working individually, collaborative

learning can further facilitate students’ processing of

academic literature (Eryilmaz et al., 2016; van der

Pol et al., 2006). In the context of a successful

collaboration, students are engaged in explaining

(Webb et al., 2009), questioning (King, 1998), or

arguing (Noroozi et al., 2012) and through these

activities, they can acquire both domain-specific

knowledge and cross-domain skills such as

collaboration, argumentation or peer-assessment

skills (Vogel et al., 2016).

Natsis, A., Papadopoulos, P. and Obwegeser, N.

Student Groups as Tutors in Information Systems Education - Students’ Perspectives on Collaboration and Outcomes.

DOI: 10.5220/0006286700370045

In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 2, pages 37-45

ISBN: 978-989-758-240-0

Figure 1: Curriculum design and the research-teaching nexus (Healey, 2005).

In such group settings, students’ characteristics

can affect the outcome of collaborative learning

(Noroozi et al., 2012). An important student

characteristic is the effective use of learning

strategies (Solimeno et al., 2008). Among them, self-

regulated learning (SRL), peer learning (PL) (Pifarre

and Cobos, 2010) and help seeking (HS) (Kyza et

al., 2013) are considered to influence the success of

collaborative learning. While self-regulated learning

has been extensively studied regarding individual

learning, less focus has been given for its

contribution in collaborative learning (Dettori and

Persico, 2008; Panadero et al., 2015).

Based on the above, the aim of this study is to

investigate the way students evaluate the process of

their collaboration when engaged in research-tutored

activities in small groups in the domain of

Information Systems (IS). More specifically, we

examine whether the use of different individual

learning strategies influences the assessment of the

way a group collaborated and the participation

during the group work. To explore these

relationships, we observed groups of first semester

graduate students working together in order to

critically engage with scientific papers, represent the

knowledge acquired from these papers in different

ways (e.g., identifying and extracting highlights of

the paper, summarizing the text etc.) and prepare a

presentation and discussion session in front of their

peers.

2 METHOD

2.1 Participants and Domain

The course “Information System Development and

Implementation in a Business Context – ISDI” is a

10 ECTS course, offered in the first semester of the

“Master Degree Programme in Economics and

Business Administration” in the Department of

Management and runs over 11 weeks with a total of

120 teaching hours. The course is taught in English

and it aims at giving students an understanding of

the diverse challenges, risks and complexities of

developing and/or implementing IS in organizational

environments. After the course is finished, the

students are expected to be able to describe, analyze,

evaluate, reflect upon, and apply models of

information systems development in a business

context.

The course is heavily influenced by real-life

context, making the research-teaching nexus an

integral part of the learning design. In the course,

students are experiencing the connection between

research and learning in several ways (Table 1),

playing both the roles of a peer-tutor (students as

tutors, ST) and the audience (students as audience,

SA). The current study focuses on the research-

tutored collaborative activity, in which students have

to work together on a teacher-assigned topic. Their

task is to analyze scientific literature and produce a

CSEDU 2017 - 9th International Conference on Computer Supported Education

Table 1: Activities the students are engaged in during the course.

Research Quadrant Student Activity

Research-led Students learn about research findings through their teachers’ own research activities.

Research-tutored Students work in groups of three or four. They are given a publication on a specific topic

and are asked to prepare five different types of deliverables, including a presentation

followed by a discussion session in front of their peers and their teacher.

Reseach-oriented Students have to critically reflect and discuss the research design and methods of seminal

IS papers.

Research-based Students have to hand-in a group report a month before the final exam. The exam itself is

conducted orally in a form similar to a thesis defence and is individual for each student.

Each group of students can decide the actual topic and research design by themselves.

set of five deliverables that can be used as learning

material by their fellow students. Their role as peer-

tutors ends with the presentation of their assigned

topic in the class. In addition, the student audience

needs to provide feedback on the structure,

usefulness, and overall quality of the material

produced by the student-tutors. Students’ research-

oriented (discussion of research methodology),

research-based (final report) and research-led

(teacher’s own research) activities complete the

course canvas. However, analysis on student activity

in these other quadrants span outside the limits of

this study and will not be discussed, since the course

is still ongoing.

A total of 65 freshmen students formed 18

groups of three or four members and participated in

the activity, which was a mandatory, non-graded,

part of the course. Seven of those groups consisted

of three students and 11 groups consisted of four

students. Regarding students’ background, 34 of

them were majoring on “Information Management”,

28 on “Business Intelligence”, while the rest were

studying “Logistics and SCM” and “Technology

Governance” programs. Finally, 13 students were

international/exchange students.

2.2 Research Instruments

The study employed three instruments: the

Motivated Strategies for Learning (MSLQ)

instrument (Pintrich et al., 1993), the student-tutors’

questionnaire, and the audience/teacher

questionnaire.

MSLQ was used to measure students’ strategies

related to self-regulated learning (SRL), peer

learning (PL), and help seeking (HS). MSLQ is a

comprehensive measurement instrument that can be

used in its entirety or in parts. The instrument is

divided into two sections and includes a total of 81

questions grouped in 15 scales. The three scales used

in this study (i.e., SRL, PL, HS) were selected

because they are the ones that influence more the

collaborative activity. The version of the MSLQ

used in this study included 19 closed-type questions

(SRL: 12; PL: 3; HS: 4), each one using a 7-point

Likert scale.

The student-tutors (ST) questionnaire, developed

by the authors for the purpose of this study, was

filled in individually by the students and used to

record students’ attitudes and opinions on the

collaborative activity. More specifically, the

questionnaire focused on how each student

experienced collaboration in his/her group, in

relation to aspects such as the volume and format of

communication, role assignment, and own

contribution. Both the volume and format of

communication were assessed by a set of four

closed-type questions, each one using a custom 5-

point Likert scale. Role assignment was assessed

against a scale ranging from “One of us was

responsible for producing the final version” to “We

worked together on the same parts producing

together the final version”. Students’ contribution to

the creation of each artifact ranged from

“Discussant” to “Leader” in a 5-point scale.

Furthermore, this instrument also included a

dichotomous item on students’ general preference

towards collaborative/individual activities and a set

of four open-ended questions in which students

could further elaborate on peer support, reaching

consensus, sharing understanding, and

communication satisfaction.

Finally, both the students audience (SA) and the

teacher completed the same questionnaire to

evaluate the peer tutoring session in terms of

structure of the presentation, quality of material

used, effectiveness of presentation, and student-

tutors’ ability to respond to audience questions and

provide clarifications on the presented topic. This

instrument included six 5-point Likert scale

questions and was used after each peer tutoring

Student Groups as Tutors in Information Systems Education - Students’ Perspectives on Collaboration and Outcomes

session by the SA (i.e., students attending the class)

and the teacher.

2.3 Procedure

In the beginning of the course, students were asked

to respond to the adjusted MSLQ instrument.

Students were also asked to provide their names in

all instruments, to allow the researchers to follow

their activity throughout the study. Students were

aware of the research aspect of the course

assignment, knowing also that their identities and

responses would not be shared with their fellow

students.

Next, students formed groups of three to four

members and were assigned a course topic by the

teacher. Each group received a seminal scientific

paper on a course-related topic and had to produce

five deliverables:

 An annotated version of the paper, with

comments and emphasized parts;

 A list of five highlights, providing a concise

view of the paper;

 A list of five questions, along with their answers,

that would cover the major issues discussed on

the paper;

 A short summary of 200-300 words;

 A comprehensive presentation of the topic for

the peer tutoring session that could use slides or

any other material. The total duration of the

presentation should not exceed 40 minutes,

including a discussion session with the class

audience and the teacher.

After each peer tutoring session, these deliverables

were made available to all students as part of the

course material. To assist the work of the group

acting as a peer-tutor and to create a homogenous set

of learning material, the teacher provided detailed

instructions and generic examples of how these

deliverables should look like. In addition, each

group received access to a separate Google Drive

folder containing the necessary assignment material,

along with empty templates for the five deliverables.

The three main reasons for using Google Drive tools

were that (a) students were familiar with these, (b)

the tools allowed for real-time co-authoring, and (c)

these tools provide a revision history log, offering

the possibility of examining students’ co-authoring

strategies during the semester. Nevertheless, it

should be noted that working online was not

mandatory for the students (e.g., students could

upload their deliverables after producing them

offline or with other tools). Each group had two

weeks to prepare its peer-tutoring session and

present the topic in the class.

To support meaningful collaboration, students

were given general guidelines. However, since we

were interested in understanding emerging

collaboration patterns and their relationship to

different learning strategies, we purposefully

allowed for a high degree of self-regulation within

the groups. More explicit collaboration scripts may

have provided better scaffolding for some students,

but at the same time potentially influence the impact

of personal strategies on collaborative activities. In

particular, the students were advised to:

 Communicate as much as necessary and have all

group members on the same page;

 Contribute to all group deliverables, even if it is

on different levels;

 Reach a shared understanding, demonstrated by

the ability to explain, analyze, argue, and answer

questions on all deliverables;

After each peer-tutoring session, student tutors (STs)

filled in the student-tutor questionnaire. At the same

time, the teacher and the remaining students in the

audience (SA) evaluated the presentation, by using

the SA/teacher instrument.

The assessment of the rest of the deliverables

(i.e., annotated version, highlights, summary,

questions & answers) is planned to be conducted at

the end of the semester after the students have

studied this material for their exams.

2.4 Data Analysis

For all statistical analyses, a level of significance at

.05 was chosen. In order to explore whether

students’ learning strategies influenced students’

assessment of group communication and

collaboration as well as of student participation, the

sample was half-split in low and high level MSLQ

subscales (SRL, PL, HS), using the respective

median as the cut-off point.

Independent-samples t-tests were conducted to

compare the volume of communication, the

collaboration among group members and the

participation in the deliverables’ creation with High

and Low groups of SRL, PL and HS. To investigate

whether there is an association between the

preference in individual or collaborative working

and the MSLQ subscales, a series of Chi-square tests

for independence was conducted.

Paired-samples t-tests were conducted to

examine the difference in students’ knowledge

before and after the presentations.

CSEDU 2017 - 9th International Conference on Computer Supported Education

Finally, a qualitative analysis of students’

responses in the instruments was carried out to

identify the collaboration patterns in the group work

and examine whether these patterns were influenced

by the self-regulated learning, peer learning, and

help seeking learning strategies.

3 RESULTS

3.1 Students as Tutors (ST)

The Cronbach’s alpha coefficient for the self-

regulated learning, peer learning and help seeking

subscales of MSLQ were 0.82, 0.73, and 0.71,

respectively, indicating satisfactory reliability. In

general, the students scored (scale 1-7) rather high in

the SRL (M = 4.36, SD = 0.93, min = 2.50,

max = 6.50) and HS (M = 4.27, SD = 1.21,

min = 1.50, max = 6.50) subscales, while they were

split in PL (M = 3.96, SD = 1.35, min = 1.33,

max = 6.67), with 22 students having a negative

disposition towards peer learning.

Table 2 presents student responses in the

questions regarding the volume and format of

communication. In addition, response analysis

showed a strong students’ preference in organizing

meetings with all the group members in a

synchronous manner either face-to-face or online,

i.e., chatting, mainly in Facebook. This finding from

the students’ answers was further supported by

examining the revision history of their co-authoring

activities in Google Docs. The low number of

different versions in students’ deliverables suggests

that the majority of groups completed the larger part

of the assignment offline and used the Google Docs

templates for minor edits or for submitting the final

draft of their deliverables.

Figures 2 and 3 present student responses in the

questions concerning the way their groups worked

during the collaborative activity and their level of

responsibility during the process. It can be seen that

students collaborated less during the production of

the annotated version of the paper, while the

presentation was the deliverable in which most of

the collaboration happened.

In order to examine whether all members of each

group perceived the same volume of communication

and collaboration during the group work, the

coefficient of variation (CV) for the questions Q1

and Q4 was computed and used as an indicator. A

group was classified as perceiving the same volume

of communication and collaboration, when CV < 0.3

and having different perception when CV ≥ 0.3. No

differences were found regarding the perceived

volume of communication between the members of

all groups. As far as the perceived volume of

collaboration is concerned, differences were found

in 10 groups. The differences in all of these groups

were attributed to the way members perceived their

collaboration in the creation of the annotated version

of the paper. Only in three of these groups there

were also differences observed in the list of

highlights, list of questions and short summary. No

differences were found regarding the perceived

volume of collaboration for the presentation.

The analysis of the students’ answers in the

open-ended questions revealed that their main

problem during the collaboration was to arrange

meetings with all members of the group. Apart from

that, the vast majority of the groups (15 out of 18)

stated that they were satisfied with their

communication and collaboration in accomplishing

the group task. Their members participated equally

and they were engaged in transactive discussions

while they were trying to give or receive support

about their tasks or trying to reach common

understanding of the paper. In three groups the

problem identified in the collaboration was the ‘free

riding’ behavior by one of the members in each

group, which caused frustration and complaints by

the rest of the members. These groups are the same

as the aforementioned groups in which the

differences in perceived collaboration among their

members were observed.

Table 2: Student responses in the student-tutor questionnaire, regarding the volume and format of communication.

Question M SD

Q1. How much communication happened in your group? (1: A little; 5: A lot) 4.28 (0.71)

Q2. How much of this communication was one-to-one (one member communicating directly with another member)?

(1: A little; 5: A lot)

2.63 (1.39)

Q3. How much of this communication was one-to-many (one member communicating directly with two or more

members)? (1: A little; 5: A lot)

4.00 (1.21)

Student Groups as Tutors in Information Systems Education - Students’ Perspectives on Collaboration and Outcomes

Figure 2: Student responses (N=65) in the student-tutor (ST) questionnaire regarding the way their groups worked (Q4.

“For each of the learning artifacts, select the phrase that describes best the way your group worked.”).

Figure 3: Student responses (N=65) in the student-tutor (ST) questionnaire regarding their level of responsibility (Q5. “For

each of the learning artifacts, select a value that describes best your level of responsibility.”).

Table 3: Patterns of collaboration among groups (N=18).

Pattern Student participation

Homogeneous

Groups

Heterogeneous

Groups

Collaboration

Equal participation - group members worked together on the same

parts producing together the final version

2 1

Mainly collaboration

Equal participation in most of the deliverables – in some

deliverables one was responsible for the final product incorporating

feedback from the other members

3 2

Mainly cooperation

Students split the work in most of the deliverables - one was

responsible for the final product and the others had to provide

feedback

3 5

Cooperation

Students split the work in all the deliverables – one was responsible

for the final product and the others had only to approve it

2 0

CSEDU 2017 - 9th International Conference on Computer Supported Education

T-test result analysis revealed that students with

extreme values in the HS subscale had a significant

difference on their perception of the volume of

collaboration that occurred in their groups (HS

High

M = 3.18, SD = 0.93; HS

Low

: M = 3.86, SD = 0.98;

t[63] = 2.57, p = 0.01). Students who characterized

themselves as ‘help seekers’ perceived the way their

group worked as less collaborative. Chi-squared

tests for independence indicated a significant

association between preference in group working

(question Q6 in the ST questionnaire) and the

students’ score in the PL subscale (χ

(1, 65) = 6.78,

p = 0.01), as expected. Conversely, no significant

association between Q6 and the use of SRL and HS

strategies was found.

As far as the collaboration patterns that emerged

in the groups are concerned, comparative analysis of

group member responses in Q4 and Q5 and their

statements in the open-ended items of the student-

tutor questionnaire revealed four distinct patterns

(Table 3). Three groups worked collaboratively,

with all members participating equally in all of

them, by working either together for all the different

parts of the deliverable or in separate parts which

they had to compile later in the final version after

reaching consensus. Another five groups worked

mainly collaboratively for the majority of the

deliverables while in some other deliverables, one

member had the leader role and was responsible for

the final product incorporating feedback from the

others. The rest of the groups applied patterns that

resemble different aspects of cooperation by

splitting the workload and working individually (see

Dillenbourg (1999) for a detailed discussion on the

nature of collaboration).

To examine whether the observed collaboration

patterns were influenced by the learning strategies of

the students, each group was first characterized as

homogeneous/heterogeneous. The coefficient of

variation (CV) for the three MSLQ subscales was

used as an indicator of homogeneity for the group. A

group was classified as homogeneous, when

CV < 0.3 for all three MSLQ subscales, and, as

heterogeneous when CV ≥ 0.3 at least in one of the

three subscales. According to this process, ten

groups were characterized as homogeneous and

eight as heterogeneous. Specifically, no differences

was observed in the self-regulation subscale between

the members of any of the groups, while one group

was characterized as heterogeneous because of

differences in HS and seven additional groups for

differences in PL (two of them also varied on the HS

subscale). However, analysis did not reveal any

visible link between group homogeneity and

collaboration patterns, since the groups appeared

equally distributed into the four observed patterns

(Table 3).

3.2 Students as Audience (SA)

Table 4 shows the mean and standard variation of

the evaluation of the 18 student presentations by the

teacher and the audience (students attending the

class on the day of the presentation). The population

of the audience varied during the semester

(approximately 50 on average each week) and the

aggregated values refer to the overall structure and

quality of all the ST presentations through the course

timeline.

Table 4: Evaluation of student-tutor presentations (N=18) by the audience and the teacher.

Audience Teacher

Questions M SD M SD

Q1. What is your opinon about the organization/structure of the presentation? (1: Several

issues on structure and/or time; 5: Timely and well-organized)

3,69 (0,61) 4,00 (1,00)

Q2. What do you think about the presentation material (slides)? (1: Too packed, boring, or

confusing; 5: Clear, easy to follow, and aesthetically nice)

3,69 (0,65) 3,68 (0,97)

Q3. What is your opinion about the effectiveness of the presentation? Were the topics

explained clearly? (1: The paper was poorly outlined; 5: The main topics were clear

and easy to understand)

3,54 (0,64) 3,45 (1,16)

Q4. What is your opinion about the group’s responses to audience questions?

(1: Confusing or incomplete answers; 5: Clear and correct answers)

3,46 (0,66) 2,77 (1,17)

Q5. How knowledgeable were you on the topic, before the presentation? (1: Not at all;

5: Very much)

2,07 (0,57) - -

Q6. How knowledeable do you feel on the topic, after the presentation? (1: Not at all;

5: Very much)

3,47 (0,56) - -

Student Groups as Tutors in Information Systems Education - Students’ Perspectives on Collaboration and Outcomes

As it can be seen from Table 4, both the audience

and the teacher evaluated positively the structure,

the quality, and the effectiveness of the student-

produced presentations. This evaluation by both of

them suggests that the group work resulted in

cohesive and aesthetically pleasing presentations

whose main topics were clear and understandable.

The discussion sessions, which followed the

presentations, received the lowest scores by both the

audience and the teacher, despite the discrepancy in

the respective scores. This indicates that the

students-tutors may have not been well-prepared to

provide appropriate answers to their peers. Yet,

paired-sample t-test showed that the audience

knowledge increased significantly during the

student-tutor presentations for all presenting groups

(p<0.05). Furthermore, there was agreement among

students and the teacher in the ranking of the

respective presentations in all the assessed factors,

suggesting that students in the audience could

accurately differentiate the quality of the different

presentations.

The analysis of the students’ comments in the

open-ended questions regarding the activity showed

their positive attitude towards the deliverables. It

also showed that they considered them useful and

helpful as preparation material for the final exam.

4 DISCUSSION AND

CONCLUSIONS

Results analysis shows that students were

successfully engaged in collaborative research-

tutored activities in the domain of Information

Systems. Analysis also shows that students did not

fully exploit the available digital tools that could

facilitate their collaboration. As such, they faced

difficulties in time management and mainly, in

arranging meetings with all the members of the

group. However, they were satisfied with their

collaboration. They managed to communicate

adequately and almost every student in a group

contributed to all group deliverables, although to a

different extent. Finally, they reached a shared

understanding, as demonstrated by their ability to

present the topics in a clear and understandable way

and answer questions on all the group deliverables.

Four collaboration patterns were identified. The

majority of the groups preferred to set a student as a

leader for the creation of each deliverable and the

role of the other members was either to just approve

the prepared deliverable or provide constructive

feedback and help in producing the final deliverable.

The presentation was the deliverable in which

students collaborated the most, probably because

they had to be also prepared to answer their peer’s

questions in the discussion session followed the

presentation.

The aforementioned patterns were not influenced

by the variance of group members regarding their

self-regulated learning, peer learning and help

seeking strategies. The preference for face-to-face or

online synchronous meetings, in order to

communicate and collaborate along with the

difficulty of finding common meeting hours, might

have led to the patterns identified.

Students with higher scores on the help seeking

scale were concerned of the way their group

collaborated. One explanation for this could be that

these students had higher communication demands

and were relying more on help coming from their

fellow students in their groups.

Self-regulated and peer learning strategies did

not seem to influence the students’ self-assessment

of group collaboration nor their role during the

activity. Regarding self-regulated learning strategy,

the study sample consisted of graduate students who

all had relatively high scores and thus, showed few

differences among them. As for peer learning

strategy, the fact that the students had not been given

a detailed collaboration script may have led them to

adapt the activity, creating a flexible space that

allowed even students with extremely low scores on

the peer learning scale to participate in a self-

satisfying manner.

Students also played the role of the audience and

evaluated their peers in the presentation and

discussion sessions. The evaluation of the

presentations by both the students as audience and

the teacher showed that the groups managed to

create high quality presentations of the topics under

study. In addition, all the students were significantly

more knowledgeable of the topic under study after

the presentation and discussion session with their

peers.

In conclusion, this study provides preliminary

evidence that engaging students into collaborative

activities that utilize the research-teaching nexus

offers the students the opportunity to apply and

develop their skills in processing, presenting, and

discussing academic work, assuming also the

responsibilities of a tutor. It should be noted that the

long-term evaluation of the examined approach of

the research-teaching nexus will have to be

corroborated through future studies in different

CSEDU 2017 - 9th International Conference on Computer Supported Education

contexts. Nevertheless, the current study provides a

useful reference for further discussion.

ACKNOWLEDGEMENTS

This work has been partially funded by a Starting

Grant from AUFF (Aarhus Universitets

Forskningsfond), titled “Innovative and Emerging

Technologies in Education”.

REFERENCES

Brew, A. (2003). Teaching and Research: New

relationships and their implications for inquiry-based

teaching and learning in higher education. Higher

Education Research & Development, 22(1), 3-18. doi:

10.1080/0729436032000056571

Dettori, G., & Persico, D. (2008). Detecting Self-

Regulated Learning in Online Communities by Means

of Interaction Analysis. IEEE Transactions on

Learning Technologies, 1(1), 11-19. doi:

10.1109/TLT.2008.7

Dillenbourg, P. (1999). What do you mean by

'collaborative learning'? In P. Dillenbourg (Ed.),

Collaborative-learning: Cognitive and Computational

Approaches (pp. 1-19). Oxford: Elsevier.

Eryilmaz, E., Thoms, B., Mary, J., Kim, R., & Canelon, J.

(2016, 5-8 Jan. 2016). Task Oriented Reading of

Instructional Materials and Its Relationship to

Message Scores in Online Learning Conversations.

Paper presented at the 2016 49th Hawaii International

Conference on System Sciences (HICSS).

Griffiths, R. (2004). Knowledge production and the

research–teaching nexus: the case of the built

environment disciplines. Studies in Higher Education,

29(6), 709-726. doi: 10.1080/0307507042000287212

Healey, M. (2005). Linking research and teaching:

exploring disciplinary spaces and the role of inquiry-

based learning. In R. Barnett (Ed.), Reshaping the

University: New Relationships between Research,

Scholarship and Teaching (pp. 67 - 78.): McGraw Hill

/ Open University Press.

Jenkins, A., Lindsay, R., & Brew, A. (2003). Reshaping

Teaching in Higher Education: Linking Teaching with

Research. London: Routledge.

King, A. (1998). Transactive Peer Tutoring: Distributing

Cognition and Metacognition. Educational Psychology

Review, 10(1), 57-74. doi: 10.1023/a:1022858115001

Kolić-Vrhovec, S., Bajšanski, I., & Zubković, B. R.

(2011). The role of reading strategies in scientific text

comprehension and academic achievement of

university students. Review of psychology, 18(2), 81-

90.

Kyza, E., Georgiou, Y., Hadjichambi, D., &

Hadjichambis, A. (2013). Teacher Framing,

Classroom Collaboration Scripts, and Help-Seeking

and Help-Giving Behaviors. In M. K. Nikol Rummel,

Mitchell Nathan, Sadhana Puntambekar (Ed.), To See

the World and a Grain of Sand: Learning across

Levels of Space, Time, and Scale: CSCL 2013

Conference Proceedings Volume 1 — Full Papers &

Symposia: International Society of the Learning

Sciences.

McNamara, D. S., Ozuru, Y., Best, R., & O'Reilly, T.

(2007). The 4-pronged comprehension strategy

framework. In D. S. McNamara (Ed.), Reading

comprehension strategies: Theories, interventions,

and technologies (pp. 465-496). Mahwah, NJ, US:

Lawrence Erlbaum Associates Publishers.

Noroozi, O., Weinberger, A., Biemans, H. J. A., Mulder,

M., & Chizari, M. (2012). Argumentation-Based

Computer Supported Collaborative Learning

(ABCSCL): A synthesis of 15 years of research.

Educational Research Review, 7(2), 79-106. doi:

http://dx.doi.org/10.1016/j.edurev.2011.11.006

Panadero, E., Kirschner, P. A., Järvelä, S., Malmberg, J.,

& Järvenoja, H. (2015). How Individual Self-

Regulation Affects Group Regulation and

Performance: A Shared Regulation Intervention. Small

Group Research, 46(4), 431-454. doi:

10.1177/1046496415591219

Pifarre, M., & Cobos, R. (2010). Promoting metacognitive

skills through peer scaffolding in a CSCL

environment. International Journal of Computer-

Supported Collaborative Learning, 5(2), 237-253. doi:

10.1007/s11412-010-9084-6

Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie,

W. J. (1993). Reliability and Predictive Validity of the

Motivated Strategies for Learning Questionnaire

(Mslq). Educational and Psychological Measurement,

53(3), 801-813. doi: 10.1177/0013164493053003024

Solimeno, A., Mebane, M. E., Tomai, M., & Francescato,

D. (2008). The Influence of Students and Teachers

Characteristics on the Efficacy of Face-to-Face and

Computer Supported Collaborative Learning.

Computers & Education, 51(1), 109-128.

van der Pol, J., Admiraal, W., & Simons, P. R. J. (2006).

The affordance of anchored discussion for the

collaborative processing of academic texts.

International Journal of Computer-Supported

Collaborative Learning, 1(3), 339-357. doi:

10.1007/s11412-006-9657-6

Vogel, F., Wecker, C., Kollar, I., & Fischer, F. (2016).

Socio-Cognitive Scaffolding with Computer-

Supported Collaboration Scripts: a Meta-Analysis.

Educational Psychology Review, 1-35. doi:

10.1007/s10648-016-9361-7

Webb, N. M., Franke, M. L., De, T., Chan, A. G., Freund,

D., Shein, P., & Melkonian, D. K. (2009). ‘Explain to

your partner’: teachers' instructional practices and

students' dialogue in small groups. Cambridge Journal

of Education, 39(1), 49-70. doi:

10.1080/03057640802701986

Student Groups as Tutors in Information Systems Education - Students’ Perspectives on Collaboration and Outcomes