TMBQ-LT: A Student-Facing Learning Tool to Support Time

Management Skills

Ana-Gabriela N

nez

1 a

, Vanessa Echeverria

2,3 b

, Miguel Zu

niga-Prieto

1 c

, Benito Auria

and Tinne De Laet

4 d

Department of Computer Science, Universidad de Cuenca, Cuenca, Ecuador

Escuela Superior Polit

ecnica del Litoral, ESPOL,

Centro de Tecnolog

ıas de Informaci

on, Guayaquil, Ecuador

Monash University, Clayton, VIC, Australia

Faculty of Engineering Science Engineering and Science Education Center, KU Leuven, Leuven, Belgium

Keywords:

Time Management, Student-Facing Dashboard, Learning Analytics Dashboard.

Abstract:

To be successful in Higher Education, students must acquire good self-regulation and learning skills. Past

studies have reported that undergraduate students are overconﬁdent in recognizing their self-regulatory strate-

gies. This overconﬁdence can be detrimental during the ﬁrst years of their undergraduate program if they are

not properly nurtured. The lack of students’ motivation, self-regulation and time management strategies can

lead to higher rates of drop-out. In this sense, student-facing learning tools can provide timely feedback to

support awareness, strengthen this self-regulation and time management skills, and thus be instrumental for

students in attaining their learning goals. In this paper, we present the TMBQ-LT, a student-facing tool that

consists of 1) a set of questions derived from the Time Management Behavior Questionnaire (TMBQ), 2) a vi-

sualization showing student’s time management (TM) predispositions and 3) tailored recommendations based

on students’ self-reported TM skills. This paper illustrates a case study on the deployment of the TMBQ - LT

by students from three HE institutions and provides recommendations for future implementations and adop-

tion of the tool.

1 INTRODUCTION

To succeed in Higher Education, students must ac-

quire good self-regulation and learning skills. Past

studies have reported that undergraduate students

are overconﬁdent in recognizing their self-regulatory

strategies (Zimmerman, 2008) when they have not ac-

quired a speciﬁc skill (Kruger and Dunning, 1999).

This overconﬁdence in their self-regulation strategies

can be detrimental during the ﬁrst years of their un-

dergraduate program if they are not properly nurtured.

Current research points out that student’s lack of mo-

tivation, commitment, and self-regulation skills may

increase the risk of drop-out and dissatisfaction with

the learning experience (Heikkil

a and Lonka, 2006),

https://orcid.org/0000-0002-4996-0390

https://orcid.org/0000-0002-2022-9588

https://orcid.org/0000-0001-9369-1813

https://orcid.org/0000-0003-0624-3305

which has been exacerbated by the COVID-19 (Ham-

dan et al., 2021).

Self-Regulated Learning (SRL) refers to the cog-

nitive, metacognitive, and motivational factors that

students use to attain and accomplish their goals dur-

ing the learning processes (Zimmerman, 2000). Re-

search has found that SRL strategies, such as goal set-

ting, strategic planning, and time management, are as-

sociated with students’ performance and attainment

of goals (c.f., (Kizilcec et al., 2017)). Therefore,

HE institutions should provide counseling strategies

to foster students’ SRL skills, aiming at improving

their academic performance and learning experience

(Heikkil

a and Lonka, 2006). In this work, we are in-

terested in fostering time management skills, an SRL

sub-construct.

Nurturing students’ time management skills is

time-consuming for different HE stakeholders (i.e.,

teachers, advisors, and counselors). As students pos-

sess different learning competencies, it is hard to

Núñez, A., Echeverria, V., Zuñiga-Prieto, M., Auria, B. and De Laet, T.

TMBQ-LT: A Student-Facing Learning Tool to Support Time Management Skills.

DOI: 10.5220/0011847500003470

In Proceedings of the 15th International Conference on Computer Supported Education (CSEDU 2023) - Volume 1, pages 217-224

ISBN: 978-989-758-641-5; ISSN: 2184-5026

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

217

develop a one-size-ﬁts-all strategy (Ga

sevi

c et al.,

2016). Learning Analytics (LA) tools may provide a

solution to address the challenge of delivering timely

and tailored feedback at scale (Pardo et al., 2019),

aiming at raising students’ awareness about their reg-

ulation strategies and triggering reﬂection about their

learning and performance.

According to Winne and Perry (2000), SRL can

be distinguished at component-oriented and process-

oriented levels. The component-oriented level is

deﬁned as students’ attributes or predispositions for

learning, regardless of the learning environment in

which they occur. In contrast, The process-oriented

level is focused on the coordination, control and reg-

ulation of strategies during the learning process. In a

recent literature review (N

nez et al., 2022), the au-

thors reported that SRL tools often support time man-

agement strategies at the process-oriented level, for

instance, by displaying the time spent when students

interact with a learning platform (e.g.(P

erez-

Alvarez

et al., 2017)) or by supporting the scheduling and

organization of activities (e.g., (Alario-Hoyos et al.,

2015)). However, to our knowledge, SRL tools that

support time management at a component-oriented

level are limited. This work aims to contribute to

these under-explored tools to support time manage-

ment skills. To this end, we designed and deployed

the TMBQ-LT, a student-facing learning analytics

tool to allow students to 1) assess their time man-

agement skills by ﬁlling the Time Management Be-

havior Questionnaire (TMBQ); 2) visualize their time

management predispositions; 3) get tailored feedback

based on their levels of time management predisposi-

tions.

2 RELATED WORK

In Learning Analytics (LA) and Educational Tech-

nology (EdTech) research, there have been many

initiatives to support the development of such time

management skills. For instance, most of the re-

search have used log data from digital learning en-

vironments, such as Learning Management Systems

(LMS). In the literature review by Perez-Alvarez et

al. (P

erez-

Alvarez et al., 2018), the authors reported

a list of EdTech tools that support SRL in online

environments, such as MOOCs. The most promi-

nent tools were the data coming from online student

activity in the LMS (e.g., days and number of lo-

gins to the LMS, the number of views on weekly

course videos and e-books, the frequency of partici-

pation in the forum environment (Karaoglan Yilmaz

and Yilmaz, 2020)), and assessment data (assessment

scores on weekly assignments or quizzes or knowl-

edge levels obtained from such assessments (Garc

ıa-

Sol

orzano et al., 2018)). Another source of data

was self-reported data through the LMS (e.g., self-

set goals and surveys to measure students’ percep-

tions or overall experience or beliefs) (Aguilar et al.,

2021; Inan-Karagul and Seker, 2021; Karaoglan Yil-

maz and Yilmaz, 2020). In addition, self-reported sur-

vey data from students has only been studied to un-

derstand their beliefs and current motivations about

SRL when moving from high school to a higher ed-

ucation institution (Garc

ıa-Ros and P

erez-Gonz

alez,

2012). Nonetheless, the results from questionnaires

are often used for research purposes, for example, to

understand the impact of time behavior skills on aca-

demic performance (e.g., (Adams and Blair, 2019)),

meaning that students often do not receive their re-

sults as feedback for improvement.

Another strand of literature indicates that feed-

back on SRL is most often presented through LA

dashboards, due to its potential beneﬁts for students

success or learning outcomes (Viberg et al., 2020;

Matcha et al., 2019). Viberg and colleagues (Viberg

et al., 2020) presented a review of empirical research

on LA and self-regulation for online learning envi-

ronment, highlighting the potential of digital traces to

measure SRL strategies. For instance, PeerLA (Kon-

ert et al., 2016) allows students to keep track of their

knowledge level (self-report data) and compare this

information with automatically extracted measures

collected from Moodle through visualisations. In an-

other work, authors presented LASSI (Broos et al.,

2017), a dashboard that presents feedback to ﬁrst-

year university students about their personal skills

(concentration, failure anxiety, motivation, use of test

strategies, and time management). Students also re-

ceive recommendations on how a particular skill can

be improved. LASSI has been widely applied in Eu-

ropean Universities, and empirical evaluation demon-

strated its positive impact on scalability, usefulness

and usability. Inspired by this latter research, we ap-

plied a similar approach in a Latin American context.

Nevertheless, we are interested in a deeper analysis of

time management dimensions instead of analyzing it

as a unique skill.

3 TMBQ-LT: AN LA TOOL TO

SUPPORT TIME

MANAGEMENT

The TMBQ-LT tool has been developed as a web ap-

plication. The tool provides Higher-Education stu-

CSEDU 2023 - 15th International Conference on Computer Supported Education

218

dents with tailored feedback on time management

strategies according to their current time management

skills levels.

3.1 Assessment of Student’s Time

Management Skills

To assess students’ time management skills or pre-

dispositions levels, we use the Time Management

Behavior Questionnaire (TMBQ) self-report ques-

tionnaire, which has been previously validated with

college students (Garc

ıa-Ros and P

erez-Gonz

alez,

2012). The TMBQ consists of 34 statements dis-

tributed in four dimensions: establishing objectives

and priorities (D1), use of time management tools

(D2), preferences for organization (D3), and percep-

tion of control over time (D4). Thus, TMBQ-LT pro-

vides tailored recommendations for each dimension.

The statements can be answered using a ﬁve-point

Likert scale (1: never, 5: always). To assess students’

time management skills, we assign the score given by

a student in the Likert scale, meaning that each state-

ment could have a minimum score of 1 if the student

selects ”never” and a maximum score of 5 if the stu-

dent selects ”always.” After, we calculate a total score

by summing up all scores from the 34 statements and

a score per dimension by summing up the scores from

the statements per dimension. To facilitate the inter-

pretation of results, these scores are scaled to 100.

Thus, we will have ﬁve scores per student: the total

and D1 to D4 scores, ranging from 0-100.

Following a similar approach as in well-known

academic tests (e.g., TOEFL iBT, GRE), we assigned

a skill level per dimension. Each dimension has ﬁve

levels, so when a score falls within a range level, it

will assign that level to the speciﬁc dimension. Table

1 summarizes the dimensions and their corresponding

labels and value ranges.

Table 1: Scores boundaries for skill levels.

low medium medium-high high

D1 [10,30) [30,35) [35,40) [40,50]

D2 [11,25) [25,32) [32,39) [39,55]

D3 [8,14) 14 [15,17) [17,25]

D4 [12,24) [24,28) [28,31) [31,40]

To account for the variability and context of our

population, these ranges were calculated using a sam-

ple (N=1083) of students that completed the TMBQ.

We processed the dataset from the TMBQ to calcu-

late quartiles and their boundaries per dimension. The

participants obtained a score between the lowest and

highest boundary in each dimension, e.g. regarding

D1: If students obtained scores between [10,30), it

means that those students have low skills in estab-

lishing objectives and priorities. On the other hand,

if students got scores between [40,50), it means that

they have high skills in D1.

3.2 Visualization Components and

Tailored Feedback

The feedback tool is composed of A) the total score,

B) a polar area diagram to display four quadrants -

one per dimension - with its corresponding score (see

Figure 1, and C) a set of recommendations based on

their levels of personal skills (for example, see Figure

2).

As depicted in Figure 1, each quadrant is color-

coded to support the interpretation of the score. The

color represents one of the four levels in which a

student can be allocated: high (green), medium-high

(light green), medium (yellow) and low (orange). The

diagram also portrays a red line, representing the av-

erage score from students with a similar proﬁle (i.e.,

age), allowing students to compare their score to other

learners and improve their motivation, as suggested in

(Wise, 2014).

Below each dimension’s name, a student can click

the link “See recommendation”, which opens a win-

dow showing advice tailored to the score and level

obtained (Figure 2). These recommendations were

co-constructed with two advisors from Institution 1.

Each advisor was invited to participate in a semi-

structured interview to examine the recommendations

experts would give students to improve time manage-

ment skills. In the interview, led by one researcher,

the advisor was asked to propose three tips that stu-

dents can easily follow and three digital tools to help

them manage their time. After these interviews, the

researcher crafted a ﬁnal list of tips and tools, which

the two advisors further veriﬁed and agreed upon.

4 EXPLORATORY STUDY

The main goal of this exploratory study was to in-

troduce the TMBQ-LT to students from different HE

institutions and explore their perceptions of the tool’s

usage and potential impact. Therefore, we aim to:

1. Explore the current situation of students’ time

management strategies.

2. Understand students’ perceptions of the score sat-

isfaction, usefulness, and information clarity.

Figure 3 depicts the deployment of our study tak-

ing a learning analytics process (Clow, 2012). Our

study is mainly focused on students enrolled in HE

TMBQ-LT: A Student-Facing Learning Tool to Support Time Management Skills

219

Figure 1: Overall score and LA visualization depicting scores per each dimension. The red line represents the average score

of students with a similar proﬁle (i.e., age).

institutions. We collected self-reports from these stu-

dents using the TMBQ. We used statistical analysis,

as explained in section 3.1, to generate visualizations

and recommendations tailored to students’ time man-

agement skill levels. Finally, we aim to provide rec-

ommendations and support the reﬂection of their cur-

rent skills.

The following sections describe the methods and

analysis to address the two goals of this exploratory

study.

4.1 Participants, Tasks and Data

Collection

Three HE institutions participated in this study; in

partnership with their academic and counselling ser-

vices, we invited students to participate in the webinar

”Techniques to Manage Time” (executed four times)

and in a workshop, hereafter referred to as dissem-

ination sessions. We used different HE institutions’

ofﬁcial social media channels (i.e., Facebook, Twit-

ter, Instagram), and email to invite students. Dissem-

ination sessions were programmed online via zoom

between June and July 2022. During the execution of

the dissemination sessions, an expert presented sev-

eral strategies to learn and improve time management

and invited students to use the TMBQ-LT tool. Stu-

dents ﬁlled out the TMBQ questionnaire including de-

mographic questions (i.e., age, gender), obtaining the

tailored feedback and recommendations generated by

the tool. In order to measure the usefulness and stu-

dents’ satisfaction with the tool, students ﬁlled out

a questionnaire. This questionnaire includes three

ﬁve-point Likert-scale questions (Q1-score satisfac-

tion: ”How do you feel about your scores?”; Q2-

usefulness:”Are the recommendations and scores use-

ful?”; Q3-clarity: ”The visualization and recommen-

dations are clear and concise?”) and one open-ended

question (”Tell us more about your perception of the

tool and suggestion for improvement”).

CSEDU 2023 - 15th International Conference on Computer Supported Education

220

Figure 2: An example of the recommendations given to stu-

dents. This is an example for a student who clicked on the

recommendations for D2 and got a medium skill level.

Figure 3: Our study taking a learning analytics process.

4.2 Results

A total of 1083 students completed the TMBQ. Table

2 shows the distribution of students who completed

the TMBQ according to their gender. Table 3 summa-

rizes the age of students per institution.

First, to explore the current situation of students’

time management strategies, we analyzed the results

from the TMBQ questionnaire per institution and per

dimension. Figure 4 depicts the results per institution.

From these results, we can observe that, for D1 (estab-

lishing objectives and priorities), most students have

Table 2: Distribution of students per institution.

I1 I2 I3 Total

Female 224 125 42 391

Male 344 231 113 688

Prefer not to say 1 2 1 4

Total 569 358 156 1083

Table 3: Students’ age descriptive statistics per institution.

I1 I2 I3

min age 17 17 17

max age 42 51 30

average 19.64 21.40 20.67

std dev 2.34 3.81 2.51

a medium-high level for this dimension, independent

of the institution. As for D2 (use of time manage-

ment tools), most students exhibited a medium (I2) or

medium-high level (I1 and I3). In D3 (preferences of

organization), most students reported a medium level

(I1, I2 and I3). Finally, for D4 (perception of control

over time), students exhibited a medium-high level.

Second, to understand students’ perception of the

usefulness of the feedback tool, we calculated de-

scriptive statistics for the three Likert-scale questions,

as shown in Table 4. 662 out of 1083 students eval-

uated their perceptions of the tool. From Table 4,

we can observe positive results reported by students

about the usefulness (Q2) and clarity (Q3) of the tool.

Nevertheless, students were not completely satisﬁed

with their scores (I1 - M:3.39, SD: 0.69; I2 - M:3.88,

SD: 0.77; I2 - M:3.40, SD: 0.75).

Table 4: Students’ perceptions about (Q1) score satisfac-

tion, (Q2) usefulness and (Q3) clarity.

M(SD)

Q1 3.69 (0.69) 3.88 (0.77) 3.40 (0.75)

Q2 4.17 (0.69) 4.18 (0.85) 3.89 (0.85)

Q3 4.24 (0.73) 4.32 (0.78) 3.92 (0.82)

Additionally, we conducted a thematic analysis

(Clarke et al., 2015) of the qualitative data from open-

ended questions. Two researchers analyzed the quotes

and got a consensus after interpretation discussions.

A total of 164 quotes extracted from students’ re-

sponses were thematically coded into 1) Positive, 2)

Negative, 3) suggestions for improvement, and 4) re-

ﬂective comments.

Regarding positive comments, 47% of students

gave positive comments about the feedback presented

in the tool. They think that the tools recommended

help meet their goals. One student mentioned that:

”the tools suggested will help us to improve our time

management strategies” (S32, I2) and another student

shared that: ”the information will help us to know

the way to meet our goals” (S34, I2). Students also

think that the tool is consistent, and the visualization

helps them reﬂect on their weaknesses and ways to

improve their organization. For instance, P54 (I2)

mentioned that ”The questionnaire is consistent with

the visualization so that we can self-assess our daily

TMBQ-LT: A Student-Facing Learning Tool to Support Time Management Skills

221

(a) I1. (b) I2. (c) I3.

Figure 4: TMBQ results per institution.

performance”. Supporting this idea, P116 (I1) ex-

pressed that ”the tool is a perfect way to know the

weak points when organizing my day.” And, P46 (I2)

reﬂected on the usefulness and future use as follows:

”The diagram presented in at the end [visualization]

was handy, because it allows me to see my general

performance, what I should improve, and what tools

I can use to help me organize my time and academic

activities. If I retake this questionnaire, I will be able

to reﬂect on my past and be more organized”. In addi-

tion to the positive comments about the tool, students

also liked these dissemination sessions and feedback

initiatives. One student mentioned that ”the univer-

sity should provide students ways to improve our time

management to achieve our goals” (S44, I2) and an-

other expressed that ”I hope they [university] con-

tinue to carry out more dissemination sessions with

this topic” (S42, I2)

Regarding negative comments, 4% of students

gave negative comments about the tool. Some stu-

dents requested to make the information clearer and

more concise. For example, one student expressed

that ”a more speciﬁc explanation could be added”

(P86, I1). Improvements in multi-platform mobile

devices were also raised. As one student expressed:

”The mobile version had some complications and is-

sues, such as the legibility of the information. There-

fore, different devices should be considered in a fur-

ther version” (P33, I2).

Regarding suggestions for improvement, 20% of

students provided suggestions concerning the dissem-

ination session content, the information gathered from

the questionnaire, and the information presented in

the recommendations. Students suggested that more

real-world examples should be included in the dis-

semination sessions (P5, P23-I1; P115-I3).

Concerning the questionnaire, students mentioned

that the questionnaire should include questions that

reﬂect real-life examples to capture their reality cre-

atively (P12-I1; P115-I3). For example, one student

expressed that ”there should be a section to ask if

you dedicate more time to social activities compared

to other activities” (P105, I1); similar to what was

indicated by P124 (I1): ”There should be questions

about the relationship between your time for study

and the time you dedicate to your friends and fam-

ily.” In addition, one student suggested that the self-

report should include questions about the use of dig-

ital tools, as the current TMBQ only focuses on gen-

eral tools such as agendas: ”I think you could add a

question to ask if you use a digital tool for time man-

agement. Personally, I don’t use agendas, but I prefer

to use an app installed in my phone to manage my

time and habits” (P81, I1). P149 (I1) commented,

”You should consider that not all students are used to

carrying out an agenda.” Another student suggested

adding questions about willingness, motivation, and

engagement (P25, I3). In contrast, another student

suggested adding ”open-ended questions” to explain

their situation (P74, I1).

Concerning the feedback information (i.e., the

visualization and recommendations), students sug-

gested that it would be beneﬁcial for them to add

other topics to the recommendations: ”I would add

more recommendations to improve my time manage-

ment skills” (P115-I3, P142-I1.) For example, stu-

dents would like to receive a study plan for short and

long-term goals: ”I would like to receive a study plan

for my short and long-term goals” (P95, I1). Another

student suggested including learning strategies: ”You

could also add learning strategies and not only fo-

cus your recommendations on tools like Pomodoro”

(P125, I1). One student recommended adding more

details and context about their ﬂaws (P30, I1).

Regarding the recommendations of digital tools,

students mentioned that other tools should be con-

sidered for this matter. One student expressed that,

while ”Pomodoro” can work well for some students,

he would prefer other tools that do not interrupt while

working: ”I don’t like Pomodoro because it limits

CSEDU 2023 - 15th International Conference on Computer Supported Education

222

your time and sometimes the alarm goes on when you

have increased your motivation to work” (P40, I2).

Other tools could be included as part of the list of rec-

ommendations, such as ”Forrest,” which allows keep-

ing your study times using gamiﬁcation elements.

Other students mentioned that the recommendations

should also factor in external aspects that could affect

their time management strategies: ”The tool should

also focus on external factors that could affect their

organization. It is not only a matter of assuming that

a student is lacking commitment for an organization”

(P68, I1). Finally, concerning reﬂective comments,

3% of students reﬂected on their results, even though

this was not asked. Students were mostly surprised by

these results. One student mentioned, ”I was some-

what surprised, I know that I don’t use 100% of my

energy at work, but I didn’t know it was low” (P17,

I3). Another student expressed: ”To be honest, I

didn’t know I have to improve a lot” (P133, I1). An-

other student reﬂected on the misalignment between

his self-reﬂection and results displayed in the tool:

”I have very important goals and objectives for my-

self and to improve this society. Maybe I gave lower

scores to questions regarding this topic. Hence, I got

lower results” (P78, I1).

5 DISCUSSION

This work aimed to address two goals. The ﬁrst

goal was to explore the current situation of student

time management strategies in a Latin American con-

text. Using the TMBQ embedded in the tool, we ex-

tracted the survey results from ﬁrst-year students en-

rolled in three local HE institutions. While the re-

sults reﬂect students’ positive perception of their time

management skills, some reported a medium to high-

medium skill level in using time management tools

(D2). The TMBQ-LA tool gave students recommen-

dations about digital tools that they could use to im-

prove this skill. Although these results suggested that

students’ time management strategies are acceptable,

it is worth mentioning that this subjective data should

also be supported by more objective data, such as dig-

ital traces from learning environments. In this way,

the tool could give better insights, as it is well known

that young students exhibit errors when systemati-

cally self-reporting data. Hence it could hinder the va-

lidity of results (Colladon and Grippa, 2018). In addi-

tion, while we reported the TMBQ survey results, our

intention was not to explore the differences between

students enrolled in different institutions. Instead, we

aimed to test the current status of our students and

how we can best support them.

The second goal was to explore students’ percep-

tions of the tool in terms of score satisfaction, use-

fulness, and clarity. Our results suggest that, overall,

students perceived the tool as helpful, and the infor-

mation presented was clear and concise. This was re-

ﬂected in students’ positive comments about the tool.

On the contrary, students reported a neutral position

regarding their satisfaction with their results and feed-

back. Two main points could explain this. First, as

mentioned above, students’ self-report data is prone

to unconscious errors, which may cause a disconnec-

tion between their beliefs and the actual score they re-

ported. Second, students’ comments pointed out that

the questionnaire should factor in external situations,

such as social, economic and life-related factors. In

our local context, students often face uncertainty in

several social planes, which may cause disruptions in

their learning and performance. Another social fac-

tor that affects current young students is procrastina-

tion. Students suggested that more information about

learning strategies or study plans would be beneﬁcial

to (at least try to) overcome these diverse challenges.

Students also valued this initiative, as it is not a com-

mon practice for students to receive this type of feed-

back or recommendations. In our country, LA solu-

tions are scarce. Hence this work is one of the ﬁrst

initiatives to build the infrastructure needed to imple-

ment a LA solution at scale.

6 CONCLUSION

This work presented the TMBQ-LT, a LA feedback

tool to measure the time management predisposition

of students and provide tailored feedback based on

their skill levels. This tool is one of the ﬁrst be-

ing deployed in our local context. Results revealed

a student’s positive attitude towards the tool’s useful-

ness and the clarity of the information. Future work

should address more longitudinal studies to measure

the tool’s impact. It should also include follow-up in-

terventions to support more reﬂective practices.

ACKNOWLEDGEMENTS

This work was supported by LATHEE (Learning

Analytics Tailored to Higher Education in Ecuador)

project (EC2020SIN321A101), which is co-ﬁnanced

by VLIR-UOS.

TMBQ-LT: A Student-Facing Learning Tool to Support Time Management Skills

223

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