"Adapting the Teaching of Computational Intelligence Techniques to Improve Learning Outcomes"

Adapting the Teaching of Computational Intelligence Techniques to Improve Learning Outcomes

Thomas Hanne and Rolf Dornberger

Abstract In the Master of Science program Business Information Systems at a Swiss university, the authors have been teaching artificial intelligence (AI) methods, in particularly computational intelligence (CI) methods, for about ten years. AI and CI require the ability and readiness of a deeper understanding of algorithms, which can hardly be achieved with classical didactic concepts. Therefore, the focus is on assignments that lead the students to develop new algorithms or modify existing ones, or make them suitable for new areas of applications. This article discusses certain teaching concepts, their changes over time and experiences that have been made with a focus on improving students’ learning outcomes in understanding and applyingspecialAI/CImethodssuchasneuralnetworksandevolutionaryalgorithms.

Keywords Computational intelligence · Artificial intelligence · Teaching · Learning assessment · STEM

1 Introduction

In research as well as in the labor market, STEM skills are strongly required—beyond all technical disciplines—also in business and society in general, and often provide above-average job and income opportunities for qualified students [1]. However, teaching STEM subjects (Sciences, Technology, Engineering, and Mathematics) provides particular challenges (see, e.g. [2]). Students often have difficulties in under- standing and learning the respective subjects, which may have other reasons than real

T. Hanne (B) School of Business, Institute for Information Systems, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Riggenbachstrasse 16, 4600 Olten, Switzerland e-mail: [email protected]

R. Dornberger School of Business, Institute for Information Systems, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Peter Merian-Strasse 86, 4002 Basel, Switzerland e-mail: [email protected]

© Springer Nature Switzerland AG 2021 R. Dornberger (ed.), New Trends in Business Information Systems and Technology, Studies in Systems, Decision and Control 294, https://doi.org/10.1007/978-3-030-48332-6_8

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or imaginary deficiencies in their respective competencies (e.g. analytical thinking) to insufficient pre-arrangements or inadequate teaching methods.

Computational intelligence (CI), comprising mainly the nature-inspired artificial intelligence (AI) methods and further metaheuristics, is one of the fields of science at the cutting edge of STEM disciplines. It has become particularly important during the last ten years along with the new rise of artificial intelligence (see, e.g. [3]), which is assumed to provide some of the most important changes and disruptions in society since the introduction of computer and information technologies in the middle of the last century.

Usually, CI is not specified by an unambiguous definition but by enumerating subareas (see, e.g. [4]), in particular

• Fuzzy logic • Neural networks • Evolutionary computation • Swarm intelligence.

Fuzzy logic provides theoretical insights, modeling approaches and methods to cope with problems, which can hardly be described by traditional binary logic and therefore contain elements of vagueness of knowledge in order to cope with uncer- tainty similar to human reasoning. (Artificial) neural networks mimic the function of real nerves and nerve nets as observed in animals and humans. It is one of the most important approaches based on machine learning in current AI developments and, nowadays, finds particular attention in the field of deep learning. Evolutionary computation is based on a simulation of biological evolutionary processes (as first described by Darwin) to find superior (optimal) solutions to complex problems. Even with moving objectives, evolutionary computation is able to continuously adapt the solution to new conditions just as biological evolution. Swarm intelligence comprises a number of similar approaches to solving complex problems, which are based on strategies found in biology, especially the behavior of animal swarms (i.e. flocks, packs, hives), which may emerge to a complex problem solving behavior, which is not shown in the individual behavior of animals.

The CI algorithms derived from these approaches are used to find good alternative solutions, optimizing candidate solutions, identifying patterns in data, and mapping input data to possible outputs. In general, CI methods work in a static context of the problem, as well as in time-dependent, changing problems, where some of these CI methods are also used for controller design. A recent development is to apply CI methods in robotics to make the robots “more intelligent”, as swarm intelligence allows the self-organizing of a bunch of robots in a swarm.

These approaches have in common that they can deal with poorly structured and/or difficult-to-solve problems and mostly rely on solution concepts that are adapted from processes in nature. Therefore, such concepts are often denoted nature-inspired methods. As they are often based on incomplete or uncertain knowledge or allow for good, although not optimal solutions, e.g. by using heuristics or metaheuristics, soft computing or nature-inspired computation are other expressions to refer to this field of science.

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The competencies to understand and apply CI are manifold. On the one hand, there are the algorithms, which require skills in programming and software engineering for understanding and coding the algorithms or for their adaptation or further devel- opment. On the other hand, the treated problems are often complex optimization problems that require a significant mathematical understanding to define the opti- mization problem with its search space and constraints correctly. Mathematics is also (for some part) necessary to understand and analyze the respective algorithms. In addition, the power of the CI methods lies in solving real-world problems, which requires profound skills to abstract the complexity of the world up to computational models, as e.g. in computational sciences or operations research.

The goal of this article is to present and discuss the authors’ special teaching concepts. The changes of the teaching concepts over time and the authors’ expe- riences are discussed with a focus on improving the students’ learning outcomes in understanding and applying special AI/CI methods. In the presented case, the focus is set on the personal experiences while teaching neural networks and evolu- tionary algorithms and similar heuristics. However, it can be assumed that many other lecturers teaching AI methods face similar problems and will benefit from the reported measures and generalized statements.

In Sect. 2 of this chapter, we discuss selected related work. Our teaching concepts related to CI are described in Sect. 3. In Sect. 4, we discuss reasons for several changes of the course concept during the last ten years. Evaluation aspects regarding the course success are considered in Sect. 5. Conclusions are provided in Sect. 6.

2 Related Work

In general, there is very few published knowledge about teaching computational intel- ligence. It is possible to find various more or less detailed descriptions of university courses related to CI, but few insights into why they were designed as they are. In addition, little is known about the evaluation of different course designs.

Although [5] explicitly addresses CI, there is little specific insight into how to teach such topics. In [6] more specific aspects of CI teaching are considered. As one of the few concrete examples related to individual CI courses let us mention the paper by [7]. The didactic setting has some similarities with our teaching approaches, which we report on later, e.g. a student project assignment, but stronger focus is laid on traditional teaching and assessment. For instance, the project work only makes up 30% of the overall mark, whereas it is 70% in our case. While that course is mainly intended for undergraduate students, ours is offered at the Master level. Unlike the course described, our study program no longer includes any more specialized follow- up courses. Both courses have in common that they strongly support the fact that the project results should be sufficient for a scientific publication.

In [8], an elective graduate-level course on CI related to engineering applications is considered, which strongly focuses on term projects similar to our course. In addition to the standard CI approaches, the use of swarm robotics is mentioned, which is a

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topic also frequently offered in our course. In addition, summer internships related to CI are offered in the considered study program. Apart from some general remarks on STEM education, the didactic setting of the training under consideration is not described in detail. This also holds for evaluation results. Samanta and Turner [9] addresses a course, which also includes some CI contents, but the main focus is on mechatronics and robotics. Stachowicz [10] is another example that focuses on a single CI-related course. Most of the time, however, content is described, while details about the educational settings remain largely unclear. In [11], an overview of CI courses at different universities is provided. In most cases, however, only content is briefly described, while educational settings are not further discussed.

In some cases, such as [12], insights are reported from courses that focus on more specific content (such as a design optimization method) in a related university course. Another example is [13], which focuses on a tool based on a particular CI method (particle swarm optimization) for its use in a teaching setting. However, from our perspective, there are no particular reasons to use the specific “teaching implementations”ofsuchmethods.Incontrast,weassumethatitmakesmoresenseto use regular implementations, which might be a better choice to have more freedom to use them later, e.g. when working in a company. Some other publications focus on the use of gamification or serious games related to teaching computational intelligence (e.g. [14]).

However, if we consider the much larger area of STEM disciplines, there is quite a lot of published work related to teaching aspects. We only mention a few of them, since they mostly give us only rather general insights of what might be a useful setting for a CI-related course. Often, these publications are too broad to allow for useful conclusions and practical applications. For instance, [15] discusses ideological aspects rather than concrete problems and suggestions related to the teaching of information and communication technologies (ICT).

The focus in [16] is also quite broad and includes different bachelor, master, and Ph.D. study programs in the areas Financial Management, Management of Tourism, Applied Computer Science, Information Management, and Information and Knowl- edge Management. The derived recommendations are quite concrete and based on abilities and skills required in industry and business. However, recommendations are not as detailed as required for the design of individual courses.

An even broader survey of study settings in Computational Science and Engi- neering is given in [17], which analyzes respective study programs from several universities including the content of courses, curricula, and the degrees offered. Although the topic appears to be more specific, the focus here is not on one single institution, and details of teaching concepts are mainly missing. In [18], the situation in teaching in electrical and computer engineering is considered. While the main focus of this paper is quite broad, it provides some insights that are also relevant to our teaching of CI, such as the emphasis on project–problem-based learning.

Detailed aspects of learning in mathematics are considered in [19]. For instance, they point out the learner’s difficulties in the (re-)construction of general mathemat- ical knowledge. Since more general mathematics content than in CI is considered,

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the aspects of problem-based learning and individual assignments are not so much taken into account.

Although teaching concepts for STEM subjects and selected AI methods (e.g. decisionrules)atschoolanduniversitylevelarequitewellresearchedandunderstood, guidance to teach sophisticated CI methods (such as evolutionary computing, swarm intelligence, etc.) at university level is still lacking. That is why we present the case of reporting, evaluating, and discussing our continuous teaching approaches and share our experiences from more than ten years of lecturing CI at the University of Applied Sciences and Arts Northwestern Switzerland FHNW.

3 CI in the Master of Science Program in Business Information Systems

We teach CI within a course of the Master of Science (M.Sc.) program in Business Information Systems (BIS) at a school of business in Switzerland. The study program can be studied full- or part-time, which is rather an administrative distinction of an assumed study period of three or five semesters in order to obtain the required credit points. The M.Sc. BIS includes four core courses and 14 electives including the CI course to provide substantial opportunities for choice and specialization among the students. The core courses are not or only marginally related to the required skills in programming or mathematics. The only preparation in the M.Sc. BIS program provided for programming-related aspects is a short pre-course in programming that will give at least a short introduction to software development, algorithms, and programming to those students holding a bachelor degree in Business Administration or similar areas, and who have little background in these matters. Consequently, there are almost no basics from the study program, which could facilitate the learning in the CI course.

In addition, the students come from heterogeneous backgrounds in relation to their countries of origin and their culture, but also taking into account the knowledge they have acquired in their previous bachelor’s studies, their apprenticeships, and work experience. Some of them have a bachelor degree in information systems, while others come from other areas such as business administration, computer science, engineering, or social sciences. Especially students with a business administration background make up a significant part of the student population with no or little background in programming and software engineering. Moreover, the mathematical background among the students is often rather weak, because many of them followed the way of apprenticeships with more practice-oriented teaching and learning instead of a stringent high school education.

Another aspect is that our university belongs to the group of “Universities of Applied Sciences”, which put a stronger focus on practically applied content. While this is preferred for subsequent work in business and industry, theoretical skills (such

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as in mathematics) can be taught in a reduced way. The above-mentioned difficulties are particularly relevant for teaching the CI course.

A further aspect that impedes the acquisition of missing skills during the course semester is the limited amount of time among students. In addition to other learning requirements and personal requests, today, more than two thirds of the students study part-time and have a workload from their jobs of about 60%, occasionally up to 100% of full-time workload. Today, we observe that the focus of many students has shifted away from the idea of learning new topics at the university, as students want to obtain a Master’s degree with minimal effort.

The course on CI was introduced in 2009, shortly after the start of our master program in Business Information Systems in 2008. In the meantime, students can begin the M.Sc. BIS program twice a year, in autumn and spring. The course on CI is based on approximately twelve lecturing blocks of four teaching hours (4 × 45 min) and one presentation block during a semester of about 15 weeks [20]. The course is usually completed by students in the second, third, or fourth semester. Classroom lessons are used for traditional lectures, exercises, student presentations, and discus- sions. According to the European Credit Transfer and Accumulation System (ECTS), the course yields six credit points, which corresponds to approximately 180 working hours per student (including self-study time).

The lecturing part is mainly for teaching basics in CI with some focus on busi- ness applications as discussed in [21]. From the CI subareas mentioned above, we put a stronger emphasis on the subareas of evolutionary computation and swarm intelligence because we assume that these fields are still underdeveloped in various practical applications (see, e.g. [22]). The more important part of the course is based on student assignments. In these assignments, the students are expected to familiarize themselveswithaparticularproblem(usuallyanoptimizationproblem)and/orasolu- tion approach (usually an optimization method), usually based on a given publication. In addition, the following types of tasks are typical for the assignments:

• The considered problem should be solved by applying a problem-solving method different from the original problem-solving method,

• a different type of problem (similar to the considered one) has to be solved with the original problem-solving method or another one,

• variations of the methods are explored, • computational studies are done, which are more extensive than those reported in

the literature.

For that purpose, the students (in groups or alone) are given sample reference papers related to their chosen topic and are expected to make themselves familiar with the optimization problem and/or suitable methods for solving it. Then they search for additional references, e.g. other problem types or further methods, which could be used. In the following, the students are expected to implement another method to solve a given problem, or to apply a given method to a different type of problem, or to do some modifications regarding an existing method.

In the past, we have strongly recommended that the students use the OpenCI soft- ware suite for this implementation work, which has been developed at our university

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in cooperation with some other institutions [23, 24]. In recent years, we have opened the choice of a suitable software platform: We also encourage the students to use a different framework or implement or modify a stand-alone code. After that, they are expected to complete computational tests with the considered settings regarding problems and solution approaches (e.g. exploring different parameter settings) and a subsequent analysis of results. In the end, they have to submit a scientific paper that should be similar to a typical conference paper, and all the artifacts elaborated during this project, such as the developed software, a short user guide, and the data used.

As discussed above, these tasks present significant difficulties for the students due to the required mathematical and software engineering background. In particular, we try to alleviate the software engineering difficulties in the following ways: In the beginning of the course, a general introduction to the programming-related aspects is given, including an introduction to the Java-based OpenCI framework. For instance, the students learn about the installation and architecture of this tool and related software such as the integrated development environment Eclipse. In addition, they learn about the OpenCI architecture and how to embed a new solution algorithm or a new type of problem into OpenCI. OpenCI is meanwhile a rather large software framework including various implementations of problem representations, solution algorithms (basic algorithms for search and optimization and machine learning), a graphical user interface, and further tools for visualization and data analysis, and additional libraries. Therefore, it is already a challenge to understand the basic ideas of this framework despite the endeavor from our side to make it as simple as possible. The maintenance of OpenCI and the integration of the students’ implementation into one common version of OpenCI is always a great challenge for us lecturers.

In the software development-related part within the CI project, the students learn how to embed a new problem representation as a (Java) class and/or a new algo- rithm, each of them being represented as classes. While the lecturing part serves a better theoretical understanding of problems and solutions (including some related mathematics), the students receive a basic preparation for writing a scientific paper in a course on research methodology and a related project to be elaborated at the beginning of the study program. However, bringing everything together—software development, mathematics, scientific writing, and the specific contents treated in the CI course—is certainly a challenging task.

It has turned out to be useful to define milestones for the student projects such as intermediate presentations of their work. On the one hand, this allows us (i.e. the lecturers, but also other students) to give feedback and discuss problems occurring in a student group. On the other hand, the students are requested to start early with their work and work continuously during the semester, which should help to avoid situations with too much time pressure at the end of their projects. During previous semesters of the course, there were one or two mandatory presentations without grading. During the last semester of the course (spring semester, 2019) we changed that to a voluntary presentation, but with (mandatory) graded deliverables in form of short papers related to the topics. On the one hand, the grading aspect should put more emphasis on the quality of these intermediate deliverables. On the other hand,

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we skipped any other deliverables for grading apart from the student assignment (research paper) and the two intermediate papers.

Whenwestartedthemodulein2009,itwasaccompaniedbyaregularwrittenexam that focused on the contents of the lecture part, i.e. mostly basic CI contents. Later (in 2015), we changed this to an oral exam at the end of the semester, which included, in addition to already taught contents, a part related to the group assignments. After that (in 2017), the oral exam was skipped and replaced by individual tasks during the semester. These individual tasks were responsible for 30% of the overall grade, whereas the significance of the “big” group assignment was increased to 70%. In 2019, the individual tasks became related to the respective group assignments. The reasons for these changes were that the students are much more motivated to focus on their assigned topic and that learning here goes much deeper than the general lecture contents. As a disadvantage, however, the familiarity with a general basis of CI, as taught during the lectures, can be lost, since it is no longer necessary for the grading success of the students.

4 Reasons for Adapting the Course Concepts

There are several reasons why we have changed the course concept repeatedly over time and why, in particular, the group assignment was upgraded with respect to its importance in student evaluation. First, this refers to the course objectives designed to make students familiar with solving real-world problems. With regard to CI (and many other topics), this means that it is not sufficient to teach techniques such as modeling, simulation, and optimization to manage complex systems. Instead, it is necessary for students to become strongly involved with related problems and apply appropriate methods to solve them themselves without detailed supervision. Such an approach is usually denoted as problem-based learning (PBL) and usually assumes that students work in collaborative groups and learn by resolving complex, real- istic problems [25]. It is supposed that this learning concept improves process skills (teamwork, project management skills, but also autonomous learning skills and other metacognitive skills), which we consider as particularly relevant at the Master level. In addition, it is assumed that PBL increases the students’ motivation and engage- ment [25]. This assumption has been confirmed by our frequent discussions with students in the course (but also in other courses) and was also expressed in course evaluations conducted among students (see, e.g. [26] for further details).

Another reason for a stronger focus on complex assignments can be traced back to Bloom’s taxonomy [27]. It distinguishes the knowledge-based aspects of learning in six levels of objectives (each represented by a characterizing noun and describing verbs, denoted here in parentheses): 1. Knowledge (to know), 2. comprehension (to understand, to demonstrate an understanding), 3. application (to apply, to solve problems in new situations), 4. analysis (to analyze), 5. synthesis (to create), and 6. evaluation (to judge). By critical examination of these levels, we found that, although CI involves complex aspects sui generis at all levels, it is difficult to evaluate the upper

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levels in traditional examination forms such as questions to be answered in a written exam. Based on our experience with the various learning assessment concepts in our CI courses, we are in line with the general experiences of universities: Mostly only the first three to four levels of the hierarchy can be assessed in this form, whereas questions intended for higher levels often fall back to lower levels. For instance, the evaluation of a CI method is done by repeating arguments from the literature, i.e. showing achievements on the level of “knowledge”. When we changed from a written exam to an oral exam, we assumed that higher-level skills (especially levels 5 synthesis and 6 evaluation) could be better assessed in this way [26]. Although this was partly confirmed, we were still dissatisfied with the outcomes, even taking into account that the available time during oral exams was rather short for in-depth assessment and that the answers result from group experiences, which do not neces- sarily show a student’s individual achievements. For this reason we replaced the oral exam by individual student assignments (written), which turned out to be rather easy (see grades in Table 1 for 2017 and 2018) and did not prevent collaboration among students who worked on the same assignments. As mentioned above, we changed these assignments to individual assignments related to the group assignment in 2019. Another reason for abolishing the oral exams was the provided time slot within the two weeks for examination. With an increasing number of students, it is no longer possible to realize oral exams with reasonable effort and without time conflicts with other exams.

Despite such changes in relation to individual student assessments, we still believe that the “big” group assignment is best suited to support learning objectives on all levels of Bloom’s taxonomy. In particular, synthesis/evaluate (level 5) is strongly supported, as the assignment requires providing an integrated solution to a complex problem (frequently with a real-life background). Evaluation/create (level 6) is required in various ways, e.g. with respect to the decision which solution methods to use, how to adapt them, and in relation to comparisons with other approaches during computational experiments.

Another reason for repeated adaptations of course concepts is given by the accred- itation regulation of the AACSB, the Association to Advance Collegiate Schools of Business,[28],whereourbusinessschoolisabouttobeaccredited.TheAACSB“pro- vides quality assurance, business education intelligence, and professional develop- ment services to over 1,600 member organizations and more than 800 accredited busi- ness schools worldwide”. The assurance of learning (AoL) process specified by [29] requires that a “school uses well-documented, systematic processes for determining and revising degree program learning goals; designing, delivering, and improving degree program curricula to achieve learning goals; and demonstrating that degree program learning goals have been met”. Thus, the overall learning goals must be broken down to individual courses and assessed on a student’s level. Since the goals are defined in terms of students’ specific competences, the didactic concept from teaching to grading needs to consider them. The CI course mostly focuses on the following aspects:

122 T. Hanne and R. Dornberger

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• Our graduates can create innovative solutions and seize opportunities for given business situations,

• our students are able to (a) identify problems, risks, challenges, and opportunities for improvements or innovations, (b) create improvements and innovations.

Additionally, the CI course contributes to

• Our graduates have extraordinary methodological skills, • our students are able to a) identify and describe a research or innovation problem,

b) define/delineate and apply an appropriate research.

Following the AACSB paradigm “closing-the-loop”, the teaching framework will be evaluated repeatedly. When the learning outcome of the students might be improved, the teaching concepts should be adapted. The question, however, is what changes in the teaching concept result in which effects? When do the students obtain results in terms of intended learning goals? In the next section, we present and discuss the results from the last ten years in continuation of our previous reflections concerning the design of the CI course [26, 30, 31].

5 Defining and Evaluating the Course Success

While the accreditation by AACSB focuses on the program evaluation by assessing the students’ learning outcome with “exceed, meet, does not meet expectations”, the typical forms of course evaluations at Swiss universities are the absolute marks of students (see below in this section for the grading scheme), and the feedback from the students regarding their satisfaction with the course. Regarding the latter, let us mention again that a regular course evaluation based on students’ feedback is done, but mostly the number of responding students is rather small so that it does not appear to be reasonable to evaluate it quantitatively. On the other hand, we received frequent feedback in informal ways (e.g. discussions during the in-class teaching). Although this feedback cannot be evaluated in quantitative form, we had the clear impression that the students unambiguously supported a stronger emphasis on group assignments in contrast to other assessment forms (such as traditional exams). In particular, they seem to prefer the stronger real-life aspects of such assessment forms, the requisites regarding creativity and self-determination, and mostly the group work aspect as well. Only for a few students, the last requirement for this form of learning (research paper and additional documents) turned out to be difficult, because of too many other responsibilities that impede group work (esp. heavy job-related workload of part-time students). However, most of them have mastered this problem well due to modern means of communication, which do not require too many personal meetings within a group of students. Thus, a rigor evaluation of the students’ feedback is not possible due to the lack of clear and statistically relevant data.

A major aspect that we consider relevant for the course success, is the quality of the research papers as a main contribution of the student assignment. In order to

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support the possibility of getting the student papers published, we guide the students as much as possible towards research. In addition, this improves their capabilities for the subsequent Master thesis and possibly further research. To reach sufficient maturity for publication, usually some subsequent rework or extensions of the paper may be necessary. Whenever possible (i.e. the quality appears to be good enough), we support the students to get the paper published, usually as a regular conference paper including peer review, e.g. [32–37]. Students frequently like to put additional effort into a paper, when they have the chance of getting accepted in a scientific publication. In addition, we can consider the number of published papers, another less subjective measure of the students’ learning outcome, which is in alignment with our learning objectives and the AACSB accreditation.

Results from the publication efforts of course-related papers are shown in Table 1 together with other details from the various course semesters. In the early semesters of the course, the possibility of getting student papers published seemed to be rather slim, but with a more focus on these aspects, it worked out quite well. Since 2014, an increasing number of resulting papers has been accepted at various scientific conferences with regular peer reviews. Obviously, the strongest year with respect to student paper publications was 2015. In relation to the slightly weaker results in 2018, let us mention that there are still a few papers from this course in the “publishing pipeline”. For 2019, papers have not yet been submitted to conferences. In general, we assume that the stronger weighting of the student assignments has contributed to the observed improvements in student paper publications.

In relation to the number of students participating in the CI course, we do not see a clear trend. However, the number of students in the Master program increased significantly during the last years, so that we assume to have an increasing number of participants in the future. The comparably high number of students (27) enrolled in the 2019 course may be an indicator of a rising trend.

With regard to the grades, we observe that those for the assignments have remained relatively stable during the specified period. The grades for the written exam declined from 2009/10 to 2012, but then have increased until 2014. The grades for the oral exams appear to be slightly better than the average grade of written exams. The assignments in 2017 and 2018 resulted in much better grades. Possible reasons are collaborations among the students and (almost) no limitation of used time or perhaps the assignments were simply too easy. In addition, the students could submit a number of such smaller individual assignments, but only the grade of the three best results were counted.

Regarding the overall grade, we can assume a rather increasing trend (i.e. better grades) with interruptions in 2012 and 2018. On the one hand, this could be due to course improvements. On the other hand, frequently observed phenomena regarding grade inflation [38] could be relevant. However, the success of student papers in conference peer reviews might indicate real improvements at least with respect to group assignments. Though the increasing trend is mainly observed for the exam part (or individual assignments from 2017), the assignment part could still be relevant for grade improvements, since its percentage for the total grade increased from 50 to 70% during the total period 2009–2019.

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More detailed results from the student grading are shown in Table 2. According to the Swiss grading system, 6 is the best grade, while 4 is the minimum grade for passing a course. Part grades use decimal grades, where the final grades are half grades: 6.0, 5.5, 5.0, 4.5, 4.0, 3.5, and below.

Obviously, the failure rate in the CI course is rather small, always between 0 and 4 students from a group of 13–27 students. In this statistic, we did not count students who drop the module very early (without submitting an assignment or participating in a group assignment). Although the M.Sc. BIS program allows this retreat officially until very close to the final exam, the number of these students is usually quite small (1–2 students). We also observe that there is frequently a significant number of good and very good students (grades of 5 or higher).

Table 2 also shows the standard deviation of the students’ grades. These were particularly high in 2015, 2018, and 2019, whereas they remained at a similarly lower level during other years. Since the course settings were different in 2015 and 2018/2019, while some strong differences can be observed for some years with almost identical settings and course sizes (e.g. 2017 vs. 2018), we assume that the reasons are of statistical nature. For instance, we observe frequently that the student population from year to year changes significantly, i.e. in relation to their background. This could be one of the reasons for the changes in the standard deviation of grades.

6 Conclusions

Many of the decisions regarding the design of the CI course and its adaptations done over time are not deeply founded on general didactic knowledge or on our specific experiences, how the best CI course might look. On several occasions, we aimed at finding better settings in a try-out-fashion instead.

Designing a CI course (but other courses as well) is similar to solving complex problems with CI methods: One usually does not know what a good design might be and experiments with different “candidate solutions” in an unknown “fitness land- scape”. Unfortunately, we cannot make as many trials as, for instance, an evolutionary algorithm would during hundreds or thousands of generations. The fact that (a) every year we have another group of students with different background knowledge and (b) we only teach this class of students once makes our optimization problem very complex to provide a (near) optimal solution (course curriculum).

In addition, we must stick to the feasibility of solutions taking into account various restrictions on our course design (such as from the study program, institutional requirements, etc.). However, we believe that it makes sense to experiment with course design parameters and that previous efforts have mostly resulted in observable improvements.

We will continue with our modifications to the course and hope to gain further insightsintoeffectiveandefficientcoursedesigns,alsofromvariousotherresearchers who document their didactic settings and experience.

126 T. Hanne and R. Dornberger

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A valuable guidance for designing course curricula is provided within accredi- tation processes. As our school of business is in the AACSB accreditation process, the regulations for course curricula made us define how we measure the assurance of learning of our students. In the CI course, we formulate traits such as “Can the student understand the optimization problem (by identifying the problem instance and the optimization method)?” (level 2, understand, following Bloom) up to “Can the student create a computational optimization experiment to generate optimization results?” (level 6, evaluation/create, following Bloom).

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