Program Objectives

CTM is delivered primarily through distance learning, using a structured combination of synchronous and asynchronous learning activities. The delivery model is designed to ensure academic rigor, regular student engagement, and clear alignment with the Program’s intended learning outcomes.

Synchronous learning is implemented through scheduled live online sessions (e.g., lectures, seminars, workshops, guided discussions, guest talks). These sessions promote interaction, timely feedback, and community building. Asynchronous learning supports flexibility and self-paced study through the Learning Management System (LMS—Moodle) and may include structured virtual course classrooms, recorded lectures, curated readings, learning units, guided study tasks, discussion forums, self-assessment activities, and individual or group coursework. Within Moodle, course content and learning activities can be organized by thematic unit and/or by date, supporting clear weekly learning pathways and systematic access to resources (e.g., documents and notes).

Where appropriate, blended arrangements may be used for specific educational activities (e.g., intensive workshops), if they are announced in advance and implemented under the University’s rules and the Program’s quality requirements. Any in-person component, if offered, is complementary and does not affect the equal participation of distance learners.

Each course is delivered based on an approved course syllabus that specifies the course purpose, intended learning outcomes, weekly structure, learning activities, assessment components, grading criteria, and expected student workload consistent with the assigned ECTS. The teaching team ensures that learning tasks and assessment requirements correspond to the planned workload and that students receive timely guidance on expectations, deadlines, and academic standards.

Active and applied learning is embedded across the Program, with emphasis on problem-solving and decision-making in clean technologies contexts. Teaching methods may include case-based learning, project work, data-driven assignments, structured debates, simulation or modelling tasks, and collaborative activities, adapted to the distance-learning environment.

Teaching and learning take place through the University’s digital learning ecosystem, including the LMS (Moodle) and the supported tools for live online delivery. Access is provided only through institutional accounts and authenticated users, with role-based permissions declared and managed in Moodle (e.g., student, instructor, authorized administrative/technical support), ensuring appropriate access rights and corresponding functionalities.

Students receive ongoing academic support through course instructors and structured communication channels such as Moodle announcements, discussion forums, direct messaging/chat within the platform, online office hours, and scheduled consultations. Moodle course spaces are organized as virtual classrooms, enabling the structured management of learning resources (e.g., documents, notes, recorded material where applicable) and the organization of course content by thematic unit and/or by date. In addition, the platform supports electronic grading of online assessments developed within Moodle, with the ability to export results in Excel-compatible format, and automated notifications for assignment and examination deadlines both within the platform and via email.

At the start of each academic cycle, the Program implements an induction process that familiarizes students with the Program structure, the digital environment, rules of participation, assessment expectations, and core principles of academic integrity and data protection.

Assessment is designed to measure achievement of learning outcomes and may include a combination of formative and summative components. Depending on the course design, assessment methods may include written assignments, individual or group projects, case study reports, quizzes, presentations, reflective tasks, and written or oral examinations conducted online, including assessments developed and administered through the Program’s LMS (Moodle). The assessment scheme for each course is communicated at the beginning of the semester through the course syllabus, including the weight of each component and the grading criteria. Where Moodle-based assessments are used, electronic grading/marking is supported and results may be exported in an Excel-compatible format for administrative processing and record keeping, in line with institutional procedures.

Where online examinations are used, the Program applies appropriate procedures to ensure fairness and reliability, including identity verification and participation rules. The use of proctoring or equivalent supervision measures, where applicable, follows University policy and relevant legal requirements. Students are also informed in advance through Moodle announcements and email notifications about assessment dates and submission deadlines, ensuring timely preparation and compliance with course requirements.

Academic integrity applies to all learning and assessment activities. Students must submit original work and comply with rules on citation, collaboration limits, and permitted resources. Written coursework, projects and particularly the Master’s Dissertation are subject to plagiarism screening using tools approved or provided by the University. Suspected misconduct is handled under the applicable institutional procedures and may lead to academic consequences in accordance with University regulations.

The Program applies a transparency-based approach to generative AI: where AI tools are permitted, students must disclose their use (tool, purpose, affected parts) and remain fully responsible for the submitted content. Non-disclosed or non-permitted use may be treated as misconduct.

Students receive academic feedback that supports improvement and clarifies performance against the stated criteria. Grades are recorded in the Program’s official information system within the deadlines defined by the academic calendar and Program announcements. Where provided by institutional rules, students may request clarification on grading decisions through the standard academic communication process. The clarification process focuses on the application of published criteria and the correction of clerical errors.

Student support in the Program is organized to ensure that learners can participate effectively in a primarily online learning environment, progress steadily through the curriculum, and complete the Program requirements on time. Support is provided through a combination of academic guidance, learning-platform assistance, access to learning resources, and structured communication channels, operating within the relevant University policies on equality, accessibility, academic integrity and data protection.

Academic support is delivered at both Program and module level. At Program level, students receive clear information on the Program structure, key academic rules, assessment calendar, and progression requirements through official announcements and the Program’s online information space. At module level, each module is led by the module instructor(s), who provides subject-matter guidance, clarify learning outcomes and assessment expectations, and offer feedback on student work. Regular online office hours or scheduled consultation slots are available for academic questions, while module discussion spaces (forums or equivalent) support peer-to-peer learning and instructor moderation. Where appropriate, additional academic advising may be provided to support study planning, workload management, and timely preparation for the Master’s Thesis.

Learning resources are provided through the University’s digital ecosystem. Students have access to the LMS (Moodle), which serves as the central hub for the creation and management of virtual course classrooms, module content, learning activities, assessment submissions, feedback, announcements, and grades. Within Moodle, course spaces are structured by thematic units and/or calendar dates, supporting a clear weekly learning pathway and easy navigation for distance learners. Teaching and learning materials typically include lecture recordings or live session links, presentation material, curated reading lists, datasets or software instructions (where relevant), case studies, and guided exercises. The Program makes systematic use of digital libraries and electronic databases available through the University, enabling students to access peer-reviewed journals, e-books, standards, reports and other academic sources necessary for coursework and thesis preparation. Students are expected to engage with these resources in a responsible manner, respecting licensing and copyright restrictions.

Technical and administrative support is available to help students navigate the online learning environment. Technical support covers access issues (institutional accounts), basic LMS use, and the functioning of synchronous teaching tools. Administrative support addresses enrolment status, module registration processes, official confirmations, and procedural information related to assessment periods and thesis submission steps. Communication is conducted through official University channels and within published response-time expectations, especially during peak periods such as enrolment weeks and final assessment deadlines.

The Program places emphasis on inclusive access and study readiness. An induction process is implemented at the beginning of the academic cycle to familiarize students with the Program expectations, the LMS environment, communication norms, and the main academic policies (including academic integrity and acceptable use of systems). Students are informed about recommended minimum technical requirements for online participation (reliable internet connection, headset/webcam where needed, and access to standard productivity tools), as well as good practices for effective online study. Where students require accessibility of accommodations or learning support measures, relevant University procedures apply, ensuring equal participation in learning activities and assessments.

Support for research and thesis work is progressively strengthened as students approach the Master’s Thesis stage. The Program provides guidance on research planning, academic writing, literature searching, referencing practices, and responsible data use. Thesis supervision is the primary academic support mechanism during the thesis period, complemented by access to library services and, where applicable, training materials on research methods, data handling, and plagiarism prevention.

Finally, the Program uses feedback and continuous improvement mechanisms to strengthen support and resources. Student feedback on teaching quality, learning resources and platform usability is collected through the University’s evaluation processes and is reviewed by the Program’s academic bodies. Where recurring needs are identified, targeted improvements may include enhancements to digital materials, clearer guidance documents, additional training sessions, or refinements to communication and support workflows.