Goal modeling in general, and i* in particular, are typically taught in specialized courses that are part of postgraduate programs. In this paper, we report on our experience concerning teaching i* and its basic, essential dialect called simple i* to over 130 first-year students of a bachelor degree in information science. We present the intended learning outcomes and activities, we introduce the simple i* dialect that was used in the labs, we discuss the gained knowledge was tested in the final exam, and we discuss the obtained results.
Despite requirements engineering has seldom been part of the standard computer and
information science programs [
We focus on the teaching of goal modeling in general, and i* [
To the best of our knowledge, the common practice in higher education is to teach
goal modeling in master-level courses, or in advanced bachelor courses. Our hypothesis
is that the basic intentional and social primitives are also suitable for first-year students.
On the other hand, we believe that more advanced themes, such as formal goal modeling
languages like KAOS [
In this paper, we share our experience and make the main findings available to the
requirements engineering research community. The following sections describe:
– The intended learning outcomes (ILOs) [
The course where i* modeling was included is the 2014/2015 edition of Organizations and ICT (http://oictuu.wordpress.com/), and it is a compulsory first-year course in the Information Science bachelor degree at Utrecht University, the Netherlands. The main purpose of the course is to introduce the students to the interplay between organizations and Information & Communication Technology (ICT).
Intended Learning Outcomes. The successful student is one that, upon passing the exam
and the practical assignments, achieves the following intended learning outcomes:
1. Can use the main organizational theories to describe how an organization functions;
2. Can explain fundamentals and challenges of integrating ICT in an organization;
3. Knows the key types of ICT systems, and can show how they support the operation
of an organization;
4. Can critically analyze the ICT systems that exist within one organization;
5. Can effectively study an organization using modeling techniques and frameworks.
The i* language was taught as part of the learning activities towards the fifth of these
learning outcomes, i.e., as a modeling technique for effectively studying an
organization, alongside mainstream frameworks such as the Business Process Modeling
Notation [
Outcome a. requires the students to understand that i* enables modeling the why
behind actor behavior, social dependencies among them, and alternative ways of fulfilling
the actors’ goals. Outcome b. focuses on the ability to adequately read an i* model.
Outcome c. concerns the capability of selecting the modeling primitive that is best suited
to represent a state of affairs within an organization. Finally, outcome d. focuses on the
ability to create useful models, with usefulness being defined as fit for purpose [
Learning Activities. In order to achieve the learning outcomes a–d, several learning activities were devised in the course, each contributing to one or more ILOs, as indicated between square brackets: – A 2-hour lecture was given to present the i* framework, and to introduce the simple i* dialect that we devised for the OICT course [a,b,d]; – A 1-hour workshop was conducted on the same day of the lecture to get students acquainted with simple i* modeling [b,c]; – A group homework assignment was created, where students had to model part of a real-world organization [c,d]; – The final exam included one question focusing on the i* framework [c].
The simple i* language was devised to facilitate first-year bachelor students in the
creation of models for their group homework assignment, without employing the full
ontology of i*, which is still a research theme [
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OR Experiment conducted by MSc Simple i* in practice. 46 groups of students applied simple i* to model part of a chosen organization that was studied from multiple angles throughout the course. The modeling was done through the collaborative online tool Lucidchart (http://www.lucidchart.com/), and using a custom drawing palette that we provided to the students. The quality of the models was good, especially considering that the students were in the early phase of their higher education. Most of the errors concerned the use of AND/OR-refinements, which were sometimes used to connect tasks to goals, or to represent a dependency (the link indicated a dependency for that goal on an actor). Moreover, we found that several groups replicated very similar patterns to those in the example that was used in the lecture (Fig. 1), especially the orthogonal contributions of two alternative goals/tasks to two soft-goals (see the bottom left of Fig. 1). 4
To test the knowledge that individual students gained about i*, we included one associative question in the final exam where the student was required to link a statement with an i* element chosen from a list that we created (see Table 1). We made this choice to align with ILO c. in Section 2. ID Statement Associated Element S1 fAolldoowctionrg raesqpueirceifiscapnroucrseedutore take a blood sample from a patient Task Dependency S2 A “nurse” of the Utrecht Medical Center Role S3 Every patient of the hospital has a birthdate S4 The well-defined process of making a “Pizza Margherita” Task S5 tThoe tselaidcehs,lebcutturaels8o toof pthreissecnotuthrsee,slFidaebsiatnoothheasstundoetnotnsly to prepare AND-decomposition S6 FCarbeaiatinnogtoa sQav&eAtimpaege on the course website as being useful for Positive contribution S7 eTihtheerpoMssoizbzilairteylltaoocrrGeaotuedtaheChtoepespeing of a pizza either by adding OR-decomposition S8 An “insurance policy” for a driver who has provoked a car accident Resource S9 dTahteabraescetsor relying on professor Mike for teaching a course on Goal Dependency S10 A patient’s desire to “have his broken knee repaired” Goal S11 oTfhUetrreelcahtitoUnsnhiviperbsiettyw”eaenndt“hUet“rDecehptaUrtnmiveenrtsiotfy”Computing Science S12 TUhtreec“hDtUepnairvtemrseintyt of Information and Computing Sciences” of Agent S13 Utrecht University’s aim of “improving the students satisfaction” Soft-goal S14 aPnuxbileistyhionfgstthuedegnrtasdes within 2 days from the exam to reduce the Positive contribution S15 A student’s desire of obtaining high-quality education Soft-goal 123 students participated in the exam; on average, 59% of the statements about i* were associated correctly, compared to an average of 63.2% for the other 8 exam questions. Thus, the average percentage is slightly lower for the i* question, yet comparable.
Table 2 presents more detailed results from the exam. Although we have not run advanced statistical analyses yet, our preliminary results provide interesting insights: a. Concerning the individual statements, S7 and S2 are those that the students associated more accurately; these statements concerned OR-decomposition (89%) and role (85%), respectively. The statements that presented more difficulties were S9 (21%), S14 (27%), S4 (32%), and S8 (36%), which described a goal dependency, a positive contribution, a task, and a resource, respectively. Our experience shows that some link types are harder to grasp than the i* entities; however, we also hypothesize that the phrasing of the sentences may have had an impact as well. b. Analyzing the association correctness per element type, statements on actors and decompositions were well addressed, while most problems occurred with dependency links (36%), and with contributions (47%). c. We classified the individual student results for the i* question and for the other questions according to the corresponding quartile. The aim was to detect if the performance of a student in the i* question deviates from her performance in the other questions. For example, if a student lay in the first quartile for i*, and in the second quartile for the other questions, that student would have lost one quartile (-1 QT). The chart shows that there is no visible tendency towards gaining or losing quartiles, despite a minor skewing towards losing quartiles. Our usage of i* in a first-year bachelor course was positive, although some problems arose, as expected when teaching advanced topic to students at the beginning of their journey in higher education. Looking at the ILOs from Section 2, we conclude that: a. Comparison of i* to other organizational modeling techniques. This outcome was largely achieve, as shown by our qualitative analysis (omitted, due to space limits) of the simple i* models that the students have created for their projects. b. Recognition of the modeling primitives and their meaning in an existing model. We did not explicitly assess this ILO, and no conclusion can be therefore drawn. c. Choice of the most adequate primitive to denote an organizational phenomenon.
This objective was partially achieved, as shown in Section 4. Some constructs were more difficult than others, especially contributions and dependencies. However, this may also be due to a misinterpretation of the statements in Table 1. d. Creation of useful organizational models with the fundamental primitives of i*.
Our qualitative analysis of the project outcomes show a satisfactory use of simple i*, with models conveying the rationale of the actors and their relationships, despite some modeling errors that do not hinder communication. Higher-quality modeling would require substantial training, which was outside the scope of this course.
Our future work on goal modeling in higher education comprises several lines: (i) obtaining results from multiple student cohorts; (ii) defining validated tests to assess i* knowledge; (iii) using automated reasoning to detect patterns in the created models; and (iv) employing gamification during learning to improve students motivation.