A Technology-Enhanced Learning Approach to Upskill Adult Educators: Design and Evaluation of a DigComp-Driven IoT MOOC

Abstract

This study presents the design, implementation, and evaluation of a Massive Open Online Course (MOOC) on the Internet of Things (IoT), developed to upskill adult educators by equipping them with both technical and pedagogical competencies. Following a structured, multi-phase instructional design model grounded in the DigComp framework and supported by Open Educational Resources (OERs), the course was delivered over three training cycles via a MOODLE-based platform. The research employed pre- and post-course competence tests to assess the course’s impact, as well as post-course surveys with both quantitative and qualitative elements to assess participant experiences. The findings indicate high levels of satisfaction and perceived effectiveness.

1. Introduction

The Internet of Things (IoT) stands at the convergence of various digital technologies, marking the emergence of a new computing paradigm, in which every day physical objects are interconnected within a global ecosystem of computation, communication, and data exchange [1,2]. By enabling devices to collect, transmit, and act upon data, IoT bridges the divide between the physical and virtual worlds, transforming traditional workflows and lifestyles [3]. This transformative power is reshaping how businesses operate, how individuals interact with technology, and how services are delivered across sectors [4,5].

The revolutionary potential of IoT, along with other key digital innovations, has drawn attention at the European Union (EU) policy level [6]. A number of strategic documents emphasize the urgent need to promote broader societal awareness of the opportunities created by digital transformation [7]. Equally important is the need to strengthen digital competencies and skills among citizens, especially in light of rapid technological developments [8,9,10]. Educating adult learners to use IoT has already been recognized as important [11]. IoT skills are still crucial under Industry 4.0’s framework for digital transformation in businesses. Therefore, these needs are particularly pressing in adult education, where educators and institutions must continuously update their training offerings to include contemporary digital topics [12]. However, these learning centers often face limitations in terms of time, financial resources, infrastructure, and access to specialized equipment [13].

In response to these challenges, it becomes imperative to empower adult educators and enhance the capabilities of adult learning centers in delivering foundational and advanced digital skills [14,15]. In this work we are concerned with both the physical aspect of IoT, i.e., objects that are fitted with sensors/actuators, uniquely addressable, and networked (typically over IP), but also with architectures and services supporting the deployment and seamless integration of IoT devices. Fostering a better understanding of these emerging technologies that are increasingly shaping the digital economy and society is imperative. Understanding technical and social issues, learning about platforms, and dealing successfully with their deployment are some of the core dimensions of our learning objectives.

To address this educational gap, we have designed and implemented a Massive Open Online Course (MOOC) focused on IoT, drawing upon Open Educational Resources (OERs) available in various formats (text, video, interactive content, and real-world case studies). This MOOC has been developed in the context of an Erasmus+ KA2 project titled “All Digital Academy: Upskilling adult educators on key digital emerging technologies” and thus it will be referred to as ADA IoT MOOC; it presents a well-structured, modular curriculum that explores IoT not only from a technological standpoint but also through conceptual and socio-economic lenses. It is designed for a broad audience interested in understanding IoT at an intermediate level, catering to both newcomers and educators seeking to integrate IoT topics into their training abilities.

In this paper, we present the design and implementation strategies employed in the development of this MOOC, including decisions related to the modular structure, the creation and integration of OERs, and the alignment with the DigComp framework. Through this analysis, we aim to contribute to ongoing discussions around instructional design for digital upskilling. Our insights can support future efforts to create scalable, accessible, and pedagogically sound IoT training courses—ultimately helping educators become confident facilitators of digital transformation.

2. Literature Review

The Commission Staff Working Document on ‘Advancing the Internet of Things’ describes IoT as a “major innovation engine” that Europe must harness [16]. IoT is seen as a key driver of innovation and competitiveness, with the potential to stimulate job creation. To support this, the EU has launched numerous projects under the “Europe’s Internet of Things Policy,” emphasizing the importance of collaboration between industry, academia, and policymakers. Realizing the full potential of IoT, however, depends on developing a workforce equipped with strong digital competencies. Beyond technical skills, individuals must also be able to use digital devices with confidence and critical awareness, understanding how IoT systems connect, exchange information, and influence their daily lives, including issues related to data protection and privacy.

As IoT technologies continue to advance, the demand for skilled professionals capable of designing, managing, and securing interconnected systems has intensified [12,17,18]. However, the current educational landscape reveals a significant skills gap in relation to IoT-related competencies [19,20,21,22]. Training in these topics is important because it equips individuals with the skills and adaptability needed to thrive in new technological professions, where rapid innovation, automation, and evolving digital tools continuously reshape job requirements [23]. In high-tech fields like artificial intelligence, data science, IoT, and other similar areas, ongoing training ensures that professionals remain competitive, informed, and capable of addressing complex, real-time challenges. Without continuous upskilling, even the most talented individuals risk falling behind in industries that demand both technical proficiency and a mindset of lifelong learning.

The development of IoT-related skills is essential for the successful deployment and operation of IoT systems [24]. The study introduces the EU-IoT Skills Framework, which categorizes required competencies into four areas: technical skills, business and regulatory knowledge, end-user/operator skills, and soft skills. The framework also defines learning paths and professional profiles based on industry needs, helping to align training with real-world demands. The growing skills gap in Data Science and the Internet of Things (IoT) is addressed also in [25] by presenting a modular, learning outcomes-oriented VET curriculum for IT professionals.

Formal education remains a foundational approach to IoT training, particularly in engineering, computer science, and vocational programs. Universities and technical institutions are increasingly incorporating IoT modules within broader curricula or offering dedicated degrees in IoT and cyber-physical systems [26,27,28]. These programs typically combine theoretical instruction with laboratory-based practical exercises, thus providing a structured pathway to skill acquisition. However, scholars have identified limitations in formal IoT education and in technological education more generally, including a lack of agility in updating course content and a frequent misalignment with the current needs of industry [29,30,31]. Furthermore, formal programs often demand prerequisites that may exclude non-traditional learners or mid-career professionals seeking reskilling opportunities [32].

Informal training environments, such as workshops, bootcamps, and hackathons, offer accelerated and practice-oriented experiences, often organized by industry stakeholders or innovation hubs. These platforms emphasize experiential learning and typically leverage real-world tools and projects, enabling learners to gain tangible competencies in a short period [33]. Nonetheless, the informal nature of such learning experiences also introduces challenges related to quality assurance, scalability, and the absence of recognized certification. As a result, while informal training may be effective for rapid upskilling or prototyping, it often lacks long-term educational structure and formal recognition [34,35].

IoT products remain less understood due to their relative novelty. To address this knowledge gap, a crowdsourced peer learning activity has been developed using an online platform called OLYMPUS [36]. This platform helps learners explore and understand IoT products and their design choices more deeply. Graduates often lack the skills to handle IoT’s complex mix of software, hardware, and human interaction. The challenge of teaching IoT is discussed in [37]. It presents two courses: an introductory one on pervasive computing and an advanced one on testing software systems. The approach focuses on essential topics, hands-on microcontroller projects, and collaboration with industry. Student feedback over five years shows this method boosts engagement, confidence, and real-world readiness, suggesting it could work well in other IoT courses too. In another approach students have been guided through designing, building, and testing solutions [38]. Combining these three-phase methodologies with hands-on experimentation encourages active student participation and improves concept retention. The study reports higher project grades and greater student satisfaction, showing that the approach boosts both performance and transversal skills.

Various e-learning methods have been used to teach IoT-related topics. For instance, Kanellopoulos et al. [1] report positive results from a MOOC developed jointly by academia and industry, focusing on Data Science and IoT. Nižetić et al. [4] describe an initiative designed to teach both students and schoolteachers how to work with Arduino microcontrollers. An online Arduino student academy is presented by Antonopoulos et al. [39]. Papastefanopoulos et al. [5] present a framework for creating a Learning Management System (LMS) with built-in IoT features, while other studies highlight how IoT can support digital literacy through e-learning platforms [10,40].

Despite this growing body of literature and the increasing number of pilot programs for IoT training, there remains a notable lack of standardized approaches when it comes to defining competencies, setting evaluation methods, and assessing learning outcomes. Many existing initiatives tend to focus primarily on students in secondary or tertiary education, aiming to equip them with technical and practical IoT skills. However, they often overlook the unique needs of adult educators, who require not only technical knowledge but also traversal skills to effectively integrate IoT concepts into their own teaching practice. This gap limits the scalability and broader impact of IoT education, as adult educators play a crucial role in disseminating knowledge across diverse learning environments and age groups. Addressing this shortfall by developing standardized competency frameworks and tailored training programs for adult educators would significantly strengthen the ecosystem of IoT education.

3. The ADA IoT MOOC

As previously explained, the integration of IoT technologies has expanded, so the demand for a workforce equipped with both technical and pedagogical skills has become increasingly urgent. This is particularly important in the context of adult education, where educators play a crucial role in transferring knowledge and shaping the digital literacy of diverse learner populations. In our initiative, recognizing the importance of enhancing the digital competencies of adult educators, we determined that an e-learning environment would be the most effective and scalable approach to reach a wide audience. The use of Open Educational Resources (OERs) was prioritized, due to their flexibility, accessibility, and adaptability as learning materials [41]. OERs not only allow participants to engage in meaningful, self-paced learning but also equip them with high-quality, reusable teaching materials that can be adapted to the specific needs of their future instructional activities. This dual purpose aims to amplify the long-term impact of the educational intervention, ensuring that knowledge transfer continues beyond the duration of the course.

To define the scope and focus of the training program, an extensive review of the literature on IoT education, digital skills development, and adult learning methodologies was conducted, alongside an analysis of existing IoT-related curricula and training initiatives.

The methodological framework for the development and delivery of the course followed a structured, multi-step process (see Figure 1), which was based on the hierarchy of postsecondary outcomes model [42]:

1.

Needs Analysis

An initial analysis was conducted to identify the learning gaps, training demands, and contextual needs related to IoT education for adult educators.

2.

Formulation of Learning Objectives

Based on the needs analysis, clear and measurable learning objectives were defined to guide the design of the course content and activities.

3.

Trainee Profile Analysis

The characteristics, prior knowledge, and professional backgrounds of the prospective participants were examined to ensure the course would be relevant, accessible, and aligned with their learning needs.

4.

Definition of Targeted Competencies

The key competencies, skills, and knowledge areas that participants needed to acquire were identified, ensuring alignment with the demands of IoT integration in educational practice.

5.

Design of Assessment Mechanisms

A system of formative and summative assessment methods was established to monitor progress, provide feedback, and evaluate learning outcomes effectively.

6.

Development and Delivery of Online Materials

Online learning materials were created, incorporating interactive components and multimedia elements. A delivery and monitoring plan was set up to support participants throughout the course.

7.

Establishment of Evaluation Methodology

Finally, an evaluation framework was developed to assess the overall success and impact of the course, drawing on participant feedback, achievement of learning outcomes, and recommendations for future improvements.

The course was thus designed to equip participants with a balanced set of knowledge, skills, and attitudes related to IoT. The design and development methodology can be generalized across several subjects, tailored however to specific subjects according to the needs analysis and the delineation of learning objectives. Learners will gain an understanding of the core concepts, architectures, and enabling technologies that form the backbone of IoT systems, as well as the societal and business implications of their use. They will develop the ability to analyze IoT system components, apply best practices for security and ethics, and design simple IoT solutions to address real-world problems. Beyond technical proficiency, the course fosters critical reflection on the broader impact of IoT and encourages participants to adopt a proactive, lifelong learning attitude. For educators, there is an additional focus on integrating IoT concepts and tools into their own teaching practice, ensuring they are prepared to pass on relevant digital competencies to their learners.

Based on the above learning objectives, the course covers a wide range of topics, from technical aspects and system architectures to ethical considerations, business models, and emerging trends (see Figure 2). Through this multi-dimensional approach, learners can explore how IoT systems are designed and operated and, in addition, understand how they create value, address global challenges, and impact everyday life.

Our work has an additional objective beyond training participants in IoT: it aims to promote digital citizenship and, by extension, strengthen democratic participation. To this end, the instructional design was guided by the European Digital Competence Framework (DigComp—https://joint-research-centre.ec.europa.eu/digcompedu/digcompedu-framework_en (accessed on 9 November 2025)) [43].

Table 1. DigComp competences developed through our MOOC.

DigComp Competence ID	DigComp Competence Title
1.3	Managing data, information and digital content
2.1	Interacting through digital technologies
2.2	Sharing through digital technologies
2.3	Engaging in citizenship through digital technologies
3.4	Programming
4.1	Protecting devices
4.2	Protecting personal data and privacy
4.4	Protecting the environment
5.3	Creatively using digital technologies

outlines the DigComp competences we addressed with the ADA IoT MOOC. By founding the curriculum on these competences, we sought to ensure that participants not only develop technical knowledge and practical IoT skills but also acquire the critical digital abilities needed to navigate, engage with, and contribute responsibly to the digital society. The methodology we followed to align the course structure and contents with DigComp commenced with the production of statements regarding knowledge, skills, and attitudes for each DigComp competence related to IoT. Keywords were extracted from approximately 70 statements, resulting in a list of around 50 IoT topics encompassing all fundamental facets of the field. In the final step, these topics were organized into modules, which formed the final structure of the course. It has to be noted that since an IoT topic could relate to multiple DigComp competences, the previously described approach managed to transparently address all DigComp competences, delivering a course focusing on IoT rather than DigComp.

The ADA IoT MOOC is a structured online course designed to provide comprehensive, modular training on IoT (see Figure 3). Based on a detailed mapping of competences, an extensive review of research literature, technical resources, and input from domain experts, the course is organized into 12 modules covering key aspects of the IoT landscape.

Each module is carefully designed with clearly defined prerequisites and learning aims, ensuring that participants follow a logical and progressive learning path. The modules are composed of Learning Units (LUs), which are delivered in various formats, i.e., PDF documents, PowerPoint presentations, and video materials, each addressing specific IoT topics. All LUs are developed as Open Educational Resources (OERs), ensuring they can be reused and adapted beyond the course. All materials for each module were designed and developed to correspond to two hours of study effort.

In addition to instructional content, each module includes exercises and quizzes designed to assess participants’ understanding and help consolidate the knowledge and skills gained. The course is hosted on a MOODLE-based e-learning platform, offering an interactive and flexible learning environment that supports self-paced study. This combination of curated content, hands-on exercises, and open access resources makes the ADA IoT MOOC a robust tool for both individual learners and educators seeking to integrate IoT topics into their own teaching practice.

4. Methodology

4.1. Research Aims and Design

The objective of this study is to assess the efficacy of a MOOC designed for IoT education. More specifically, the study aims to evaluate both the organization and the teaching methods employed in the course, examining how effectively they address the learning goals set for participants. In addition, the research seeks to measure the extent to which the course influences participants’ own teaching practices, particularly in terms of how they integrate IoT-related content and methods into their educational settings. By combining an analysis of course design with an exploration of its practical impact on participants’ pedagogical approaches, the study aspires to provide insights that can inform future improvements in IoT-focused online education.

The research adopted a mixed-methods design of an explanatory nature, integrating quantitative and qualitative indicators. Data collection involved two instruments: a web-based questionnaire distributed to participants of the ADA IoT MOOC upon course completion, and a competence assessment test administered at the beginning and after the completion of the MOOC. Both instruments were anonymous and accompanied by a statement clarifying that all information would be gathered and processed in compliance with the General Data Protection Regulation (EU) 2016/679 and Regulation (EU) 2018/1725, exclusively for research purposes. Learners were required to provide consent for the use of their responses; otherwise, they could not submit the forms. In addition, further quantitative indicators, such as successful course completions, assignment submissions, and similar performance records, were retrieved from the MOOC platform during course delivery to strengthen the examination of the research questions.

The main research questions of the current work are as follows: i. Do the participants consider the training in IoT important? ii. Do the participants consider their participation in the training activity effective? iii. Which pedagogical approach is more efficient?

4.2. Research Context

The ADA IoT MOOC was structured to operate in both a standard and an accelerated mode. The standard version was planned to be conducted over 12 weeks, with each week allocated to a specific course module. The accelerated version necessitated six weeks of study, comprising two modules weekly. The initial two cycles were executed in standard mode, whilst the final cycle was conducted in accelerated mode. Since each module was designed to require two hours of study, the first two training cycles involved a weekly workload of two hours, whereas the third cycle required four hours of study on a weekly basis. Furthermore, in the initial cycle, participants received tutor support, whereas subsequent cycles were conducted in a self-paced format. Tutor support meant that we had weekly posts on the course forum, both for individual and group activities/assignments, with the provision of feedback, support through asynchronous messages on the learning platform, and discussion of generic and course-related questions in the course forum.

Successful course completion required a minimum average assessment test score of 60% or more. Forum participation was ungraded, though learners were strongly encouraged to contribute. Similarly, assignments did not affect the final grade but were reviewed qualitatively by the course tutors (applicable only during the first training cycle).

4.3. Data Collection and Analysis

This study combined mostly quantitative methods to provide a well-rounded evaluation of the learning experience. Data were collected through the following means:

• The e-learning platform, which provided data on assessment tests’ performance, engagement/activity, and completion rates.
• A competence assessment test, which allowed us to assess participants’ digital competence.
• A survey aimed at collecting participants’ feedback on various aspects related to the importance and effectiveness of the ADA IoT MOOC.

The Competence Assessment Test (CAT) was produced to assess the competence of participants with regard to the ten DigComp competences addressed by the ADA IoT MOOC. We initially determined the three dominant IoT topics for each of the ten DigComp competences and created questions corresponding to the foundation and intermediate proficiency levels of DigComp, as our MOOC aims at providing knowledge and skills up to the intermediate level. We created five questions for each topic and for each level, and the platform would randomly select one of them. The logic was that it started with the first topic and randomly gave one of the five questions for the Foundation level. If the participant answered correctly, they were then given one of the five questions for the Intermediate level at random. If not, the test moved on to the next topic. A correct answer at the Foundation level awarded 5 points, and a correct answer at the Intermediate level awarded 10 points. Therefore, the maximum total score a participant could achieve for one DigComp competence was 45 points (3 topics × (5 + 10) points from the Foundation and Intermediate questions, respectively). This test was taken two times; one before starting the MOOC (participants could not access it otherwise), and one after its completion.

The post-course survey (Appendix A), using mostly 5-point Likert scale questions (from Strongly Disagree to Strongly Agree) aimed at collecting participants’ views on the relevance, quality, usefulness and satisfaction. Additional qualitative data have been gathered through open-ended survey questions (Appendix A), designed to capture detailed feedback on participants’ experiences and the course’s influence on their teaching practices. These open responses allow participants to express their reflections in their own words, offering richer insights. Also, the importance of tutor support is further examined to provide useful insights for MOOC designers and providers. The evaluation dimensions were as follows:

A.

MOOC Perceived quality;

B.

Learning process quality;

C.

Usefulness;

D.

Continuance intention;

E.

Satisfaction;

F.

Retention factors and suggestions.

Information obtained from the CAT and the post-course survey underwent pre-processing, coding, and entry into a CSV file. The SPSS v30 statistical analysis software was utilized for the analysis. We assessed the quality of the survey instrument by computing its reliability using the Cronbach alpha. Various statistical techniques were applied for both descriptive analysis and inferential purposes through hypothesis testing. Specifically, boxplots and bar charts were used to visualize nominal and ordinal variables. The non-parametric Wilcoxon signed-rank test was employed to compare the means of two related samples with non-normally distributed data, while the t-test was used to compare the means of two independent samples.

5. Evaluation Results

Table 2. Registrations, active users, and completions per training cycle.

	Registrations	Active Users	Completions	% Completions Based on Active Users
1st cycle	291	70	42	60%
2nd cycle	83	47	12	25.53%
3rd cycle	169	60	20	33.33%
Total	543	177	74	41.80%

shows that across three training cycles, the first had 291 registrations, 70 active users, and 42 completions (60%). The second had 83 registrations, 47 active users, and 12 completions (25.53%). The third cycle had 169 registrations, 60 active users, and 20 completions (33.33%). The participants were adult educators or vocational training center officers who completed the e-course (95% university graduates, 5% students; 43% females, 57% males). Overall, out of 543 registrations and 177 active users, 74 completed the training, with a total completion rate of 41.80%. Completion rates declined during the last two cycles, suggesting a reduction in user engagement when tutor support was no longer available or when the learning process required greater time investment and independent effort, which is a finding corroborating with other studies [44]. This pattern highlights the importance of guided support and manageable workload in sustaining participation and motivation throughout the course. Learners may have faced challenges balancing the course with other commitments, leading to lower completion rates. Figure 4 presents demographic information about the active users, offering further context for understanding participation trends across different learner groups.

5.1. Competence Assessment Test Results

Assessing the extent to which participants developed knowledge and skills was a central component of the evaluation strategy, as it demonstrates the value of the educational intervention and, more specifically, the effectiveness of each pedagogical approach. 74 users completed both the pre- and post-MOOC competence assessment test in all three training cycles. Figure 5 illustrates learners’ competence levels prior to and following the course. The results show clear progress across all ten DigComp competences addressed in the ADA IoT MOOC.

Table 3. Detailed statistical data of overall competence assessment in all training cycles.

Cycle	Mean Before MOOC	SD Before MOOC	Mean After MOOC	SD After MOOC	Wilcoxon/t-Test	p-Value
Cycle 1	185.95	114.53	293.93	92.42	w(35.5)	0.000
Cycle 2	177.08	100.12	209.58	80.01	t(−1.96)	0.0756
Cycle 3	145.75	69.57	199.00	64.47	w(0.0	0.000
All	173.65	102.17	254.59	94.41	w(109.0)	0.000

presents a detailed overview of the overall competence assessment results, expressed as the mean total score across all ten DigComp competences of all learners, in the three iterations of the ADA IoT MOOC. The data show improvements in average competence scores in all training cycles. In the first cycle, we applied the Shapiro–Wilk test to the pre- and post-course overall competence scores, which indicated that the variables did not follow a normal distribution (p < 0.05). Consequently, the Wilcoxon signed-rank test was used to determine whether the observed increase in the average overall competence was statistically significant, and the results indicated a statistically significant improvement at the 0.05 level. The same procedure was repeated for each individual competence, revealing statistically significant gains across all ten DigComp competences.

In the second training cycle of the ADA IoT MOOC, the Shapiro–Wilk test applied to the pre- and post-course overall competence scores indicated that the variables followed a normal distribution (p > 0.05). Accordingly, a paired t-test was conducted, which showed that the increase in mean overall competence after the course was not statistically significant (p > 0.05). The same result was obtained for all ten individual DigComp competences. Similarly to the first cycle, the third and final training phase demonstrated an overall increase in average overall competence scores, which proved to be statistically significant (Wilcoxon, p < 0.05). Nevertheless, the magnitude of this improvement was notably lower than in the first cycle, and, moreover, the gains observed in four competences (i.e., 4.1, 4.2, 4.4, and 5.4) did not reach statistical significance at the 0.05 level.

Finally, when educational intervention is considered as a whole across all training cycles, statistically significant improvements are evident in both the overall competence scores (Wilcoxon, p < 0.05) and in all individual competences (see Appendix B). These findings represent a highly promising outcome of our work, as enhancing participants’ competence levels was the primary objective of the ADA IoT MOOC. Moreover, the evidence of consistent competence improvement across all training cycles, although with varying magnitudes, underscores the effectiveness of the course design, while also pointing to the strengths and limitations of different instructional practices.

5.2. Post-Course Survey Results

The questionnaire was filled out by 74 learners who completed the e-course. Based on the type of data for each variable, the χ²-test of independence was used to detect statistically significant differences and correlations between the variables in the study. The reliability of the questionnaire responses was determined using Cronbach’s internal consistency coefficient, which was found to be acceptable (α ≥ 0.78 in all three cycles).

The findings from the “MOOC Perceived Quality” chart (see Figure 6) reveal consistently positive feedback across all three training cycles, with the third cycle showing the highest overall satisfaction. Participants rated five aspects of the MOOC, i.e., content quality, relevance, currency, structure, and suitability, using a 5-point Likert scale. In all cycles, the majority of responses were concentrated in the top two categories (“4” and “5”), indicating strong agreement regarding the quality and relevance of the content. The consistently high ratings across all cycles confirm that the course was well-received and suitable for the participants’ level of understanding. These results highlight the effectiveness of the MOOC and suggest a positive impact on learners’ perceptions of content quality and relevance.

Figure 7 presents the opinions of the learners regarding the learning process quality. A key point of divergence appears in Q6, “The support of the tutor was adequate.” In the first cycle, where tutor support was provided, the majority of respondents rated the support positively. This reflects a strong sense of guidance and availability from the tutor. However, in the second and third cycles, where no tutor support was offered, responses were mostly negative. This sharp contrast highlights the perceived importance of tutor presence in the learning experience and clearly indicates that participants valued direct instructional support as part of the MOOC.

Regarding Q8, “The workload was reasonably spread,” results were generally positive in the first two cycles but declined in the third. In contrast, Cycle 3 participants gave more negative or neutral responses, with one-third selecting disagreeing. This can be directly linked to the accelerated pace of the third cycle, which appears to have led to a perception of increased workload pressure and reduced time for absorption and reflection. For Q7, Q9, and Q10, which focus on assessment adequacy, content alignment with objectives, and the usefulness of assessment items, the feedback remains largely positive across all cycles.

The responses to Q11–Q14, which assess the usefulness of the MOOC, indicate consistently positive perceptions across all cycles (see Figure 8). Most participants agreed that the course content was relevant to their job duties (Q11) and useful for their professional roles (Q12), with particularly strong results in the first cycle. Q13 shows broad agreement that the MOOC contributes to increased efficiency at work, while Q14 received overwhelmingly high ratings in all cycles, confirming that participants view the course as a valuable asset for enhancing employability. Overall, the data confirm the MOOC’s strong perceived usefulness in both current job performance and future career advancement.

The findings related to continuance intention are highly encouraging (see Figure 9). Across all three cycles, Q11–Q14 show strong agreement that the MOOC content is relevant to job roles, useful in professional contexts, and beneficial for both efficiency and employability. Particularly in Q13 and Q14, the overwhelming majority selected “4” or “5,” indicating a clear belief that the course will improve job performance and support career advancement. These consistently positive responses suggest a strong intention to continue using or applying the MOOC content in the future. Even in the absence of tutor support in cycles 2 and 3, participants maintained high ratings, especially regarding the course’s impact on employability.

The findings under the Satisfaction dimension indicate a high level of learner contentment and endorsement of the MOOC experience across all three cycles (see Figure 10). The majority of participants felt they achieved their personal goals through the course, with all or nearly all respondents in each cycle selecting Agree/Strongly Agree. This suggests that the MOOC successfully met learners’ individual expectations and objectives. Overall satisfaction also received strong positive responses. While a few participants in the first and third cycles selected lower ratings, the vast majority still expressed satisfaction, indicating a generally positive user experience even in cycles without tutor support. Q20 shows that participants overwhelmingly felt they made the right decision in enrolling in the MOOC. In all three cycles, responses were concentrated in the highest categories, especially in the first and third cycles, where over 90% selected “5—Strongly Agree.” This reflects both confidence in the course offering and alignment with participants’ learning needs. Perhaps most significantly, Q21, regarding the recommendation of MOOCs for other course subjects, received near-unanimous endorsement. In the first cycle, 41 out of 42 participants strongly agreed, and similar enthusiasm was observed in the second and third cycles. This shows not only satisfaction but also a strong willingness to advocate for MOOCs as a broader educational approach.

Figure 11 displays the results on the factors that inspired or helped participants to complete the course. The data highlights the key motivational factors that influenced participants to complete the MOOC across three cycles. The most prominent and consistent factor was the award of a certificate, which was marked as the most important reason by a significant number of participants in all cycles, especially in the third cycle, where 17 out of 20 respondents ranked it as their top reason. This underscores the strong extrinsic motivation associated with formal recognition of learning. Another highly valued factor was the relevance of the content to participants’ everyday work duties, consistently receiving high rankings across all three cycles. “The course was very interesting” and “The course covered contemporary topics” were frequently ranked in the top three across all cycles. These findings point to the importance of engaging, up-to-date content in sustaining learner motivation.

On the other hand, factors such as collaboration with other learners, trainer support, and balanced workload were generally less influential, particularly in the second and third cycles where tutor support was absent. Finally, intrinsic motivation such as “wanting to learn something new” ranked very low in all cycles, suggesting that while curiosity may be present, practical utility and certification were stronger drivers of course completion.

Finally, participants were invited to share their positive impressions of the course and offer suggestions for improvement (Q23, Q24). These open-ended questions were optional, yet a significant number of learners chose to respond, providing valuable qualitative insights. Approximately one-third of the respondents highlighted the course topics as the most beneficial aspect, noting their relevance, clarity, and practical applicability. A smaller group specifically praised the interactive and engaging format of the course, noting that the combination of multimedia materials, self-paced modules, and clear structure made the learning experience enjoyable and easy to follow. These elements were seen as contributing to sustained motivation and a deeper understanding of the content.

In terms of improvement, around 40% of participants of the first two cycles suggested extending the timeframe for completing mandatory tasks, indicating that the current schedule may feel restrictive, especially for working learners or those balancing multiple responsibilities. Additionally, 15% of respondents expressed a desire for more practical examples and real-world applications within the course content. Some of the participants of the last cycle suggested that only a few of exercises should be made optional, indicating that the workload felt demanding within the shortened timeframe. This recommendation reflects a desire for greater flexibility, allowing learners to focus on core content while engaging with additional activities based on their individual needs, interests, or available time.

6. Discussion

In the preceding sections, we examined the structure and implementation of the ADA IoT e-course, developed to address the growing need for adult educators to be equipped with both technical and pedagogical skills in the context of the expanding use of IoT technologies across sectors. The course content was presented and analyzed, demonstrating its relevance to contemporary educational and technological demands. In addition, the evaluation methodology and assessment process were discussed, along with findings from a participant survey that offered valuable insights into learners’ experiences and overall satisfaction with the course.

The research question, “Do the participants consider important the training in IoT?”, is strongly supported by the participants’ responses to several key survey items. The data shows that over 83% of respondents agreed or strongly agreed that the MOOC content was relevant to their job responsibilities, useful in their professional context, likely to increase their efficiency at work, and valuable for enhancing their employability. These findings clearly indicate that participants recognized the practical importance and applicability of IoT training in their careers. Furthermore, the high level of endorsement continues in questions related to future engagement. More than 91% of participants indicated that they are likely to revisit the course materials, recommend the MOOC to others, and continue using MOOCs on IoT-related topics. This demonstrates not only satisfaction with the current training but also a strong intention to continue learning in the field of IoT, underscoring the perceived relevance and long-term value of such training. Together, these results provide convincing evidence that participants consider IoT training to be important and beneficial, both for their current roles and future professional development.

The research question “Do the participants consider effective their participation in the training activity?” is strongly supported by the evaluation results. High levels of agreement across multiple indicators demonstrate that participants not only valued the course content but also felt that their learning experience was meaningful and impactful. Specifically, more than 83% of participants agreed or strongly agreed that the MOOC content was of high quality, relevant to its subject, up to date, well structured, and suitable for their level of understanding. These responses reflect a clear appreciation of the course’s design and instructional approach, indicating that participants found the content engaging and appropriately targeted. In addition, over 91% of respondents expressed agreement that the assessment of knowledge gained was adequate, that the content was coherent with the course objectives, and that the assessment items helped them better understand the learning materials. This suggests that the course not only delivered valuable knowledge but also effectively supported learning through well-aligned evaluation mechanisms. Finally, when asked about their overall experience, more than 83% of participants reported feeling satisfied and affirmed that they made the right choice by enrolling in the MOOC. This strong endorsement highlights the perceived effectiveness of the training activity as a whole, from content quality and structure to learning outcomes and personal satisfaction. Collectively, these findings provide robust evidence that participants considered their engagement in the training activity to be highly effective.

Moreover, both our research aims are further reinforced by the qualitative and motivational data provided by course participants. A key factor contributing to both the perceived quality and effectiveness of the training was the intrinsic interest and contemporary relevance of the course. Many participants reported that “the course was very interesting” and “covered contemporary topics,” which helped sustain their engagement and motivation throughout. These aspects not only reflect positively on the course design but also suggest that the content resonated with current educational and technological trends, which is an essential element of perceived quality.

Approximately one-third of the respondents identified the course topics as the most beneficial aspect, emphasizing their relevance, clarity, and practical applicability to their professional contexts. This highlights the strong alignment between the course content and the learners’ needs, further validating the course’s effectiveness.

In addition, a smaller group specifically praised the interactive and engaging format, noting that the use of multimedia materials, self-paced modules, and a clear course structure made the learning experience both enjoyable and manageable. These features played a significant role in enhancing learner satisfaction and reinforcing positive perceptions of both the quality and impact of the MOOC.

Regarding the more effective pedagogical approach, the findings suggest that the first approach, characterized by the presence of tutor support and a balanced workload, proved to be more successful in maintaining learner engagement and promoting course completion. The availability of tutor guidance likely provided learners with timely feedback, motivation, and clarification of complex topics, while the manageable workload helped prevent cognitive overload and time-related stress. Together, these factors created a supportive learning environment that fostered consistent participation and improved overall learning outcomes.

The findings highlight some key takeaways for MOOC designers and providers. First, ensuring relevance and practical value are critical, as participants appreciated content aligned with their job roles and real-world needs [45,46,47]. Including contemporary and engaging topics further enhanced motivation and satisfaction. A clear, well-structured format and self-paced flexibility were also valued, helping adult learners manage their time effectively [48]. The certificate of completion proved a strong motivator [45,49], while optional tutor or peer support can benefit those seeking additional guidance [50,51]. Finally, designers should aim for a balanced workload and realistic deadlines to maintain learner motivation and reduce drop-out rates [52].

The study has certain limitations that may moderate the generalizability of its findings to other MOOC contexts, including limited experimental control due to the open nature of online learning, and reliance on certificates as a key motivational factor for participation.

The study has certain limitations that may moderate the generalizability of its findings to other MOOC contexts. Reliance on certificate attainment as both motivator and outcome risks construct validity. This single-course case study with a small, self-selected cohort limits external validity and introduces selection bias. Platform data lacked granularity on key covariates (e.g., prior knowledge, time constraints), and no long-term follow-up was conducted. Findings should be replicated across larger samples and multiple courses, topics, and languages to assess generalizability.

7. Conclusions

In response to the growing integration of Internet of Things (IoT) technologies across sectors, the ADA IoT MOOC was developed to address the urgent need for adult educators equipped with both technical and pedagogical skills. Recognizing the crucial role educators play in shaping digital literacy among diverse learners, the course was designed as an open, scalable e-learning program built on Open Educational Resources (OERs). This approach ensures flexibility, accessibility, and long-term impact by enabling participants to engage in self-paced learning while gaining reusable teaching materials for future use.

The course structure was informed by a comprehensive needs analysis, literature review, and the hierarchy of postsecondary outcomes model. It follows a multi-phase instructional design process, from identifying learner profiles and defining competencies to developing interactive materials and implementing evaluation mechanisms. Aligned with the European Digital Competence Framework (DigComp), the course emphasizes not only IoT skills but also digital citizenship, data protection, sustainability, and ethical technology use.

Across three MOOC runs, the first cycle achieved the lowest attrition and the largest competence gains. The second cycle showed the highest dropout and no statistically significant competence improvement, likely due to its extended duration coupled with the absence of tutor support; future iterations could test added scaffolding (e.g., guided projects, voice-over/talking-head lectures, periodic workshops). The third cycle produced moderate outcomes but higher attrition than the first despite similar active cohort size; its aggregate competence gain was roughly one-third of the first, with several competencies showing no significant change—patterns consistent with a half-length course concentrated into a doubled weekly workload that limited depth.

Effective MOOC design should prioritize tutor support as it consistently improves engagement and learning outcomes. Completion rates are higher with a light, predictable workload, with about two hours per week outperforming heavier pacing. Perceived usefulness rises when modules are bundled into scenario-based learning paths tailored to specific educational needs. Course materials should be varied, with a strong emphasis on high-quality videos alongside readings and interactive assets. For advanced offerings, incorporate hands-on, active learning through projects and practice tasks. For technology-focused courses for educators, the use of DigCompEdu as the foundational framework is preferable. Completion certificates are also important for retention purposes.