CATALOGUE OF KNOWLEDGE

CATALOGUE OF KNOWLEDGE

LEARNING OUTCOMES ON THE STUDY PROGRAMME LEVEL

When preparing the study programme, the learning outcomes and educational objectives paradigm was used, as recommended by the Bologna Declaration. The recommendations from the SOCRATES European network were used when designing the catalogue of knowledge, and the ASIIN accreditation recommendations were met within the scope of more applied oriented IT programmes specializing in the field of computer engineering (SSC 04, Type 2). When defining particular learning outcomes, outcome level descriptors have been respected (both EHEA Framework Dublin descriptors and the Baseline of the Croatian Qualification Framework).

A) OVERVIEW OF EDUCATIONAL OBJECTIVES ON THE STUDY PROGRAMME LEVEL

Educational objectives represent the “desires” of the educational institution embedded in the educational programme. They provide potential employers with the knowledge of what a student will know when they complete the study, as the application of learning outcomes ensures the testing of the successful acquisition of the educational objectives. This does not make the approach unusable or unnecessary considering that it provides a strategic profiling of the study from the perspective of the educational institution, and does so compactly and clearly in a singular document. The educational objectives are integrated in the study programme courses, and are provided here so students and employers could gain a broad insight into the basic tenets of the specialist graduate professional study. The educational objectives are:

  1. To build students’ understanding and ability to independently apply contemporary theories, concepts, methods and practices in the field of computer engineering, with a special focus on the ability to independently use specific computer tools, platforms and methodologies,
  2. To ensure analytical knowledge and an approach based on mathematics and theoretical knowledge required for solving unusual, incompletely defined or confusing problems in the field of applied computer engineering,
  3. To develop critical thinking based on understanding the best methods, solutions and practices in the field of applied computer engineering,
  4. To provide complete knowledge of the field: analyses and designs of software solutions, IT systems implementation, complex computer networks, computer security and multimedia computing (depending on specialization),
  5. To train students to combine knowledge from various fields in order to be able to deal with complex and interdisciplinary problems within the profession,
  6. To build an understanding of how information systems, information technology, and the business and social environments influence each other regarding infrastructure and a wider context,
  7. To provide students with the knowledge that will both ensure their understanding of the current state of computer engineering, and serve as a basis for innovative thinking, observing new achievements, and developing innovative solutions,
  8. To ensure that students become independent and responsible, and to teach them the value of respecting and using business and professional standards,
  9. To provide the students with the knowledge and skills required to prepare and manage projects, as well as coordinate teams using widely accepted methodologies and models for resource, budget and risk management,
  10. To raise students’ awareness and motivation for further learning and professional development in the field of computer engineering and in interdisciplinary fields related to their workplace requirements.

B) LEARNING OUTCOMES ON THE STUDY PROGRAMME LEVEL

The learning outcomes of the study programme are directly connected with the learning outcomes of particular courses, and they do not represent just the desires of the educational institution, but also the tested knowledge and educational achievements attained by successfully completing the programme. The minimal and desired learning outcomes are defined for the study programme and each course. Each student who graduates will achieve the minimal learning outcomes. Excellent students will achieve the desired outcomes. Particular desired outcomes differ from minimal outcomes only by a student’s success in terms of grade, not by the scope of the knowledge and skills defined by the learning outcome.

Students who graduate with a grade less than excellent will achieve the learning outcomes between minimal and desired, and the diploma supplement will clearly show to what degree the student has acquired each learning outcome for each course. On the educational programme level, only the minimal learning outcomes will be shown, i.e. the abilities each professional computer engineering specialist must have.

Upon the successful completion of the study, each student will be able, regardless of sub-specialization:

General:

  1. To evaluate and analyze complex and insufficiently defined problems in their field of expertise by using the concepts in information theory, applied mathematical theory, and the best engineering practices,
  2. To propose innovative solutions in the field of applied computing through critical analysis and evaluation of current knowledge, models, and solutions in the profession, by applying “best practices solutions” as well as established and modified problem cases,
  3. To apply complex research and analysis methods in order to determined detailed user or organisational demands for IT solutions or systems,
  4. To recognize, analyze, and elaborate on the problems regarding the application, improvement and implementation of existing IT systems in a wider business context, and to propose adequate solutions,
  5. To manage relations with users and team members by recognizing potential sources for conflicts and misunderstandings, and to proactively and efficiently act on deterring them,
  6. To design, prepare, and manage the implementation of developmental projects in the field of applied computing by using widely accepted methodologies while bearing in mind available resources, budget and risks,
  7. To be aware of business, organizational and sociological aspects of planning, designing and applying IT systems, and their impact on the environment (the users, organization, and society),
  8. To evaluate an entrepreneurial idea and propose adequate business and organisational conditions for its realization,
  9. To proactively manage their own professional and personal development, and to acquire new knowledge and skills in different environments and contexts (for example through successful and unsuccessful projects, continuous independent learning, following scientific and technological advances, additional education, etc.),
  10. To independently plan and manage IT projects within the limits of available resources, by taking responsibility for personal and team assignments in unpredictable business conditions and environments,
  11. To independently design a relevant final project by taking into consideration the set requirements and standards, and applying modern technologies, tools and methodologies.

Specialist study, data sub-specialization:

  1. To critically evaluate the impact of disruptive technologies on the business environment, to evaluate the impact of different disruptive technologies within the sector in which they appeared, and to analyze the possibility of new disruptive technologies to appear,
  2. To select adequate methods for working with missing data and data transformation, to propose solutions for problems determined in the process of data preparation, and to select adequate solutions for particular problems in the process of data integration, normalization and discretization,
  3. To independently create program solutions to solve parts of data problems,
  4. To evaluate the impact of different types of security risks, to analyze the provisions of the ethical code that protect privacy, and to elaborate on the conceptual difficulties in determining the right to privacy,
  5. To evaluate the impact of different types of data dimensionality reduction, to apply adequate basic methods of data dimensionality reduction, and to select adequate algorithms for machine and in-depth learning for solving observed business problems,
  6. To select, interpret, and determine basic measures of central tendency and dispersion in the sense of applicability, interpretability and usefulness, and to interpret the basic aspects of correlation and regression analysis,
  7. To present what social network analysis is and what its goals are, to propose basic measures of networks, centrality, prestige and grouping in a network, and to rank the basic functionalities of social network analysis software,
  8. To critically analyze the advantages and disadvantages of cloud analytics, to select adequate cloud services, and to apply them to solving specific business problems,
  9. To analyze the features of psychosomatic, vocal, verbal and facial expressions in the context of developing models for the automated detection of affect in industries,
  10. To re-examine the potential of big data and big data analysis techniques, and to rate product quality using knowledge mined from big data,
  11. To evaluate the role and advantages of data visualization compared to numerical representation, and to select adequate visualization and exploratory analysis tools for a given problem.