A Project Manager Skill-up Simulator Towards Problem

Solving-based Learning

Masanori Akiyoshi

, Masaki Samejima

and Norihisa Komoda

Faculty of Applied Information Science, Hiroshima Institute of Technology, 2-1-1 Miyake Saeki-ku, Hiroshima, Japan

Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka, Japan

Keywords:

Project Management, Skill-up Simulator, Scenario Generation, Association Rule Mining.

Abstract:

This paper addresses a project manager skill-up simulator which aims to provide problem-solving learning

environment. Project management is inherently human-centric activities, and research work for education has

been done by using simulation. The proposed simulator environment is designed to provide well-conﬁgured

functionalities that make it possible to generate high ﬁdelity scenario on project situation with event rule ﬁring

mechanisms, evaluation on a learner’s operations as to the system development progress management phase.

The proposed environment realizes effective scenario generation and well-insighted evaluation along with a

learner’s interactive operations.

1 INTRODUCTION

Recently we have faced with the problem occurred

in the project management of IT system develop-

ment such as delivery delay and cost increase, even

if development organization and division are well-

prepared at the project planning phase. Project man-

agers are strongly expected to handle various situa-

tions at the project ﬁelds based on deep understand-

ing of “body of knowledge on project management”,

for instance, PMBOK(PMI, 2009) (Project Manage-

ment Body Of Knowledge) and so forth. As for the

consolidation of “body of knowledge on project man-

agement”, we have a world-wide used examination by

PMI (Project Management Institute) and could eval-

uate whether project managers have it or not. As for

handling various situation at the real project, OJT (On

the Job Training) is mostly applied to train such skills.

Success and failure factors of projects(Horine, 2005)

are also argued. However OJT has drawbacks that it

sometimes causes risks against practical projects and

depends on the ability of tutors as excellent project

managers.

This paper addresses a concrete methodology to

enhance problem solving ability of learners using our

developed project simulator. The simulator-based

learning environment have been proposed for project

management(Davidovitch et al., 2007)(Drappa and

Ludewig, 2000), for instance, “SESAM”(Drappa and

Ludewig, 2000) provides interactive functions to

manage the system development project. The simu-

lator just mimics some aspects of real projects and

the learners can make task assignment to members in

the simulator and receive evaluation of the ﬁnal re-

sult. The learners may judge members’ skills through

Q&A (Question and Answer) style communication in

the simulator. Of course it is important to mimic real

project aspects in the simulator, besides we believe

more signiﬁcant factors when using a simulator are to

provide intended scenario generation along with the

learning objectives set by tutors and detailed feedback

on each learner’s operations. “intended scenario gen-

eration” is not patterned one, but dynamically induced

one by using project model and a learner’s interactive

operation.

This paper proposes such a new type of project

manager skill-up simulator which realizes scenario

generation mechanism with event rule ﬁring and

feedback mechanism concerning a learner’s defec-

tive operation identiﬁcation from “reference opera-

tion” viewpoints.

190

Akiyoshi M., Samejima M. and Komoda N..

A Project Manager Skill-up Simulator Towards Problem Solving-based Learning.

DOI: 10.5220/0004140401900195

In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2012), pages 190-195

ISBN: 978-989-8565-31-0

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2 PROJECT MANAGER

SKILL-UP SIMULATOR

2.1 Speciﬁc Features on Problem

Solving-based Learning

Project management includes several phases, for in-

stance, planning, progress management, negotiation

to clients and so forth. Negotiation involves uncer-

tainty coming from human relationships, so we focus

on the phases common to planning and progress man-

agement hereafter. As for the target phases, required

abilities of project managers are logical thinking-

based actions to check usable resources and promised

constraints, and make appropriate operations towards

the situation. In a sense, PDCA (Plan-Do-Check-

Action) cycle is repeated until ﬁnalizing problem-

solving towards the situation.

Then what is the approach for providing such a

PDCA cycle as a scenario? Patterned scenario makes

it possible for tutors to set intended learning objec-

tives explicitly. On the other hand learners opera-

tions are so restricted to follow the patterned scenario,

which means the simulator just generate the same

results along with the intended learning objectives

and seems not to contribute to skill-up as “problem-

solving”. Unexpectedness is necessary for learners

to do the PDCA cycle with logical thinking, which

makes it difﬁcult to guarantee the learning objectives

set in advance by tutors.

Based on the above-mentioned features on

“problem-solving in the project management”, the is-

sues to be addressed are as follows.

• Learning scenarios are generated from the simula-

tor when tutors simply set the learning objectives.

• Tutors get the reference operations as a solution

concerning the learning scenario even if they are

either optimal or semi-optimal.

• Learners make similar operations which mimic

essential ones in real projects under recognizing

the learning objectives.

• Learners get appropriate feedback as to their op-

erations based on evaluation of problem-solving

process.

2.2 Conﬁguration of PM Skill-up

Simulator

In the section 2.1, the PDCA cycle is argued on “plan-

ning” and “progress management” as a prerequisite

for the simulator. Hereafter our proposed simulator

addresses the “progress management” from the is-

sues of “problem solving” viewpoints, because the

model and related functionalities are more concrete

than “planning” ones. Figure 1 shows the conﬁgura-

tion of the project manager skill-up simulator.

Figure 1: Conﬁguration of PM Skill-up Simulator.

The project model mainly consists of “Project”,

“Module”, and “Person” as they exist in the real

project. “Project” deﬁnes a set of modules to be de-

veloped and members to be assigned to the project.

The dependency of modules is depicted as “parent

module” and “child module”. “Module” deﬁnes each

estimated man-hour and technical domain such as

“database”, and its difﬁculty grade ranked with “A

(difﬁcult)”, “B (normal)”, and “C (easy)”. “Person”

deﬁnes each member’s skill grade on technical do-

mains ranked with “A (expert)”, “B (normal)”, and

“C (novice)”. These three level description on module

difﬁculty and person skill generates project dynamics,

e.g. mismatching between module difﬁculty and per-

son skill is main cause of schedule delay and quality

loss in a project. And the most crucial task ofa learner

as a project manager is to detect/predict such unde-

sirable situation and make proper operations, for in-

stance, “overtime directive”, “collaborative work di-

rective with expert members” and “member assign-

ment change”. “collaborative work directive with ex-

pert members” is especially found in the real situation

of debugging. The process of debugging generates

new bugs and some low-skilled level members cannot

solve such iterative chain of generating bugs without

supervised collaboration. The simulator also models

latent bugs which makes it difﬁcult to estimate ﬁnal

product quality. The result is evaluated using indices

such as Q(uality), C(ost), and D(elivery), where “Q”

corresponds to the total bugs, “C” does the overtime

cost, and “D” does the completed date against the ini-

tially scheduled date.

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What we explained so far is normal situations in a

sense, since the project model generates daily project

dynamics and operations by a learner are mostly nor-

mal reactions if he/she has knowledge on project man-

agement such as PMBOK and experiences on real

projects such as small delay and standard volume oc-

currence of bugs. In addition to imitated model of

a project, “event rules” are used to make some dis-

turbances towards project dynamics that reﬂect the

learning objective by tutors. For instance, the learn-

ing objective is “quality management is tough on

the project”, then unexpected explosive occurrence of

bugs is triggered by the corresponding event rule ﬁr-

ing to the situation.

Scenario is generated based on the above-

mentioned intertwined interaction of model-driven

dynamics, event rule ﬁring, and a learner’s operations.

Project status is displayed using “Gantt chart panel”

and operations are done through “interaction panel”

interface.

As shown in Figure 1, “Event rule set genera-

tor” and “Learner-oriented feedback ” are necessary

mechanisms of our proposed simulator to realize un-

patterned scenario-based learning. “Event rule set

generator” is to generate necessary event rule set from

all event rules so that the learning objective is guar-

anteed. “Learner-oriented feedback” is to detect a

learner’s defective operations by comparing them to

“reference operations” and provide related questions

to a learner.

2.3 Event Rule Set Generator

“Event rule” is deﬁned as disturbance to cause un-

expected effects on the project status that reﬂect the

learning objectives set by a tutor. Figure 2 shows the

descriptive forms for depicting such a rule. For in-

stance, if the learning objective is concerning “Qual-

ity Management”, the simulator should generate the

situation on “quality” even if a learner makes appro-

priate operations and vise versa. As indicated in Fig-

ure 2, descriptive parts are combinations of each part

items, that is, 27 rules are used. Of course all the

rules are not necessary to guarantee the project sta-

tus, which means the number of rule set is 2

. There

inherently exist so many variances as to the project

status in the simulator under intertwined interaction

of project model dynamics and a learner’s operation.

Therefore it can not be feasible to set such a rule set

by hand.

Figure 3 shows our proposed mechanism to in-

duce such a rule set by using agent programs that

perform various operations against the project sta-

tus. Agent programs are characterized to make either

Figure 2: Description of Event Rule.

Figure 3: Event Rule Set Generator.

proper or improper operations for the learning objec-

tive. First “Event rule selector” sets a certain set of

event rules. Then by using “project model”, “agent

programs”, and the “selected rule set”, the simula-

tor outputs the project status log and “QCD (Qual-

ity, Cost and Delivery)”. “Situation evaluator” judges

whether the intended project status is generated along

with the learning object which gives a score on the

“selected rule set”. Again a pair of “selected rule set”

and its score is inputted to the “Event rule selector”.

This repetitive process continues until one “rule set”

as optimum is decided. To realize this optimization,

GA(Genetic Algorithm) is used.

2.4 Learner-oriented Feedback

The project result from “QCD” viewpoints is output

of the simulator which is derived from a learner’s

operations. Simple evaluation of a learner is done

by using these indices, however, it does not lead to

skill-up from “problem solving” viewpoints. Excel-

lent project managers are considered to have judg-

ment criteria, so called “project management princi-

ple”, and make proper operations(hereafter, we call

them “reference operations”) along with it. There-

fore acquisition of such criteria is positioned as one

of the goals when providing the “problem solving-

based learning”. If a learner makes some mistakes

on his/her operations with lack of such criteria, the

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learning simulator should point out the mistakes and

give related questions to conﬁrm his/her knowledge

on it. The “reference operations” are derived from

applying the “project management principle” to the

project status in the simulator. So this paper assumes

that such “project management principle” and “refer-

ence operations” are known in advance, to focus on

the feedback problem to a learner.

2.4.1 Mechanism Overview

The feedback consists of two mechanisms, e.g. “iden-

tiﬁcation mechanism on learner defective operations”

and “question generation mechanism”. Figure 4

shows the conﬁguration of the feedback, using a

learner’s log and a reference operation-induced log.

The log includes several set of results with project

models, because “a learner’s defectiveness” is de-

tected across several project models.

Figure 4: Overview of Learner-oriented Feedback.

2.4.2 Difference Extraction

The difference of operations is done for each module

• the times of “overtime directive” and “collabora-

tive work directive”

• ﬁrst operation timestamp

The predicates are “more/same/less” for the times

and “late/not late” for the timestamp.

The data representing the difference includes the

above items, module name, operation type, predi-

cates, and the project status attributes indicated in Ta-

ble 1.

2.4.3 Identiﬁcation Mechanism

There exist three types of defective operations; 1) de-

fective ones frequently happened in several project

Table 1: Project Status Attributes.

Name Value

module difﬁculty three level; “A”, “B”, “C”

module work volume real number

skill mismatch logical value; true, false

delivery delay logical value; true, false

is on critical path logical value; true, false

models, 2) defective ones frequently happened in a

certain project model, and 3) defective ones happened

by chance. The defective operations frequently hap-

pened in several project models are the target opera-

tions which are caused by the lack of knowledge on

“project management principle”.

Figure 5 shows the process ﬂow for identify-

ing our target operations. Association rule mining

process generates association rules between “project

status attributes” and “difference predicates”, which

means applying these rules makes it possible to ﬁl-

ter “defective operations happened by chance”. Then

among the generated association rules, exact match-

ing ones to “project management principle” are ex-

tracted. The extracted rules makes it possible to ﬁlter

defective operations frequently happened in a certain

project model.

Figure 5: Process Flow of Identiﬁcation.

2.4.4 Question Generation Mechanism

Questions related to defective operations frequently

happened in several project models are generated by

using templates with “choice list style”. The exam-

ple on output dialogue from the simulator is shown in

Appendix A.

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3 EVALUATION

3.1 Result of Event Rule Set Generation

To verify the intended scenario generation by our pro-

posed mechanism, we assume the followingcondition

to the simulator. A tutor sets “quality loss project” as

the learning objective, and 5 modules and 3 members

are used as the 30 days-long project model. Table

2 shows the generated event rules on the descriptive

parts.

Table 2: Generated Rules on Quality Loss.

No.

Evaluation

function

Event Level

Estimation

on Quality

Additional bugs M

Estimation

on Cost

Work efﬁciency S

Estimation

on Quality

Additional bugs L

As indicated in Table 2, the part of “Evaluation

function” is used as to “Estimation on Quality” that

means disturbances from bugs management are trig-

gered when “quality” is well-maintained. Therefore

this will make such a situation to care about “quality”

and do some operations for it. We also evaluate how

many times of the “quality loss” project status hap-

pens in the project as to agent programs entirely. Av-

erage times of such project status is 2.67, and it seems

to be reasonable for the reason that “quality loss” sta-

tus happens every 10 days in the 30 days-long project.

3.2 Result of Learner-oriented

Feedback

We used 10 project models for 2 learners; learner-A

and learner-B. Investigation by hand detected 3 de-

fective operations as to the learner-A and 2 defective

operations as to the leaarner-B. Then our proposed

mechanism extracted rules as shown in Table 3.

Table 3: Extracted Rules.

learner-A learner-B

initial association rules 99 38

rules for identiﬁcation 2 1

Table 4 shows the identiﬁed rule and the num-

ber of generated questions. As indicated in Table 4,

unidentiﬁed defective operation still remains; “ade-

quate skill against the module ⇒ collaborative work

directiveis many”. This was caused by the reason that

various action part descriptions for the condition part

“adequate skill against the module” existed in the log,

which causes ﬁltering such association rules by our

proposed mechanism.

Table 4: Generated Questions.

learner rule(number of question)

low skill against the module

⇒

collaborative work directive is few (7)

low skill against the module

⇒

collaborative work directive is late (6)

low skill against the module

⇒

collaborative work directive is late (5)

4 CONCLUSIONS

This paper addresses a project manager skill-up sim-

ulator which aims to provide problem-solving learn-

ing environment. The distinctive features of the sim-

ulator is to provide un-patterned scenario generation

along with a learning objective and feedback as to a

learner’s defective operations from the lack of knowl-

edge on “project management principle”. Experimen-

tal results shows our proposed functionalities does

work and make sense from “problem solving-based

learning” viewpoints. Our future work is how to mod-

ify the scenario generation and feedback along with

the progress of a learner’s study.

ACKNOWLEDGEMENTS

This work was partially supported by KAKENHI;

Grant-in-Aid for challenging Exploratory Research

(23650537).

REFERENCES

Davidovitch, L., Shtub, A., and Parush, A. (2007). Project

Management Simulation-Based Learning For Systems

Engineering Students. In Proceedings of Interna-

tional Conference on Systems Engineering and Mod-

eling 2007, ICSEM ’07, pp.17–23.

Drappa, A. and Ludewig, J. (2000). Simulation in software

engineering training. In Proceedings of the 22nd in-

ternational conference on Software engineering, ICSE

’00, pp.199–208.

Horine, G. (2005). Absolute Beginner’s Guide to Project

Management (Absolute Beginner’s Guide). Que

Corp., Indianapolis, IN, USA.

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PMI (2009). The PMBOK Guide. Project Management In-

stitute, fourth edition.

APPENDIX

The project status derived from your

operation at "module 4" of "project 2"

is shown below.

Which operation is the most proper one

to improve your operation?

Please choose it from the choices.

[Your operation]

overtime directive;

7th day, 10th day, 13th day

[Your result]

Bugs;

4.58567

Cost;

3.5

Developpment term;

[Choices]

1. 4th day; progress check

8th day; overtime directive

10th day; progress check

2.11th day; overtime directive &

progress check

12th day; progress check

3. 4th day; overtime directive

7th day; overtime directive &

progress check

4.12th day; overtime directive &

progress check

15th day; overtime directive

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