DeSci EDU: course design and self-perceived evaluation in decentralized science education

Introduction: Decentralized science (DeSci) aims to address structural inefficiencies in traditional research yet lacks formal educational pathways.

Methods: This exploratory case study develops and evaluates an eight-week blended course, DeSci EDU, which combines theory-based seminars, scaffolded assignments, and alumni mentorship. Thirty-two participants completed pre-course self-assessments across ten blockchain and DeSci domains; 22 provided post-course data, and 11 supplied matched identifiers.

Results: Cohort means increased by 41% for blockchain and 53% for DeSci, with the largest relative gains in governance mechanisms and token-based incentives. Matched-pair analysis showed comparable improvements (+51% and +58%).

Discussion: The early-career, highly educated sample and reliance on self-report limit generalizability and causal attribution. Nonetheless, the results suggest that a structured practice-oriented curriculum can quickly enhance DeSci literacy. Future iterations should integrate objective performance tasks, transversal competencies, and automated participant tracking to enable scalable and rigorous evaluation.

1 Introduction

Decentralized science (DeSci) is an emergent sociotechnical movement that addresses the longstanding challenges in the traditional scientific system: opaque workflows, limited access to research outputs, and minimal opportunities for non-academic participation. DeSci leverages distributed ledger technologies (DLTs), smart contract governance, and tokenized incentive structures to enable permissionless participation, transparent funding, and collective ownership of scientific outputs (Weidener and Spreckelsen, 2024; Ding et al., 2022).

DeSci shares the core values of the open science (OS) movement, such as transparency, interoperability, and reproducibility; it advances these ideals through protocol-level guarantees of immutability and machine actionability. This ensures that research data and processes are not only open but also tamper-proof and readily usable by automated systems.

A defining feature of DeSci is the use of blockchain technology-enabled decentralized autonomous organizations (DAOs) to distribute decision-making and resource allocation across a global community. Within the DeSci ecosystem, many of these entities, commonly referred to as BioDAOs for their focus on biological and biotechnological research, are actively working to overcome inefficiencies in conventional funding systems and fragmented knowledge infrastructure (Fantaccini et al., 2024; Weidener et al., 2024). This organizational shift has led to new models of project incubation, intellectual property management, and community-led governance, challenging traditional approaches to publishing, grant distribution, and research collaboration.

As DeSci combines the technological and scientific domains, it imposes a distinctive biliteracy requirement: participants must understand contemporary research methods while also mastering the fundamentals of blockchain technology, consensus algorithms, smart contracts, and cryptographic protocols (Reder and Davila, 2005; Kabashi et al., 2023; Kalman, 2008; Budd and Lloyd, 2014). However, no cohesive programs or resources presently teach this combined skill set, limiting DeSci’s capacity to onboard new contributors and slowing the growth of decentralized research infrastructures.

This shortfall mirrors a wider pattern: most university-level blockchain technology courses remain fragmented, confined to cryptocurrency basics or credentialing, and lack coherent pedagogical frameworks, standardized assessments, and progressive learning pathways from foundational to advanced competencies (Weidener and Lukács, 2025; Kabashi et al., 2023). In addition, DeSci operates not only on Web3 expertise and biotechnology operations but also requires multiple types of contributors, such as marketing specialists, legal advisors, and tokenomics experts, skills that are not taught in Web3 or biotechnology courses.

To address these educational gaps, this study pursues three objectives:

• Develop a modular DeSci course to provide structured training for new contributors.

• Design a self-assessment instrument directly linked to the course content.

• Pilot the course as an exploratory case study with a diverse cohort and evaluate changes in perceived understanding using pre- and post-course assessments.

2 Methodology

This section outlines the methodology used to address the three objectives of the study.

2.1 DeSci EDU course design

The DeSci EDU program was launched with the primary objective of onboarding and educating new contributors in DeSci.

2.1.1 First iteration of the course: Mol.Edu

The first iteration of the DeSci EDU program, branded as Mol.Edu, was developed by Molecule AG in 2024 with the initial aim of onboarding nine scientists into the emerging DeSci ecosystem. Mol.Edu was designed as a modular, intensive onboarding experience for early contributors to DeSci, focused on enabling participants to become confident advocates and practitioners within the ecosystem.

Mol.Edu was structured as a six-week intensive onboarding course, with the first month dedicated to building foundational knowledge of DeSci and associated concepts such as Web3 or DLT. The curriculum comprised nine DeSci modules, each taught by an instructor and paired with a slide deck shared as a PDF after the lecture. Deeper explanations were delivered through public videos or podcasts; practical tasks required learners to apply concepts in authentic settings; and regular assignments with recap quizzes provided continuous feedback on their progress. Each week offered two one-hour sessions plus a recap, providing at least 3 hours of live interaction. All other work, from assignments to discussions, was handled asynchronously through Notion and Discord. An overview of the modules is provided in Table 1.

Table 1

Table 1. Modules and topics covered in Mol.Edu.

Mol.Edu was conceived with three overarching aims: education, retention, and expansion. Its primary aim was to provide participants with rigorous conceptual and practical foundations in DeSci, preparing them to actively participate in the emerging ecosystem. The second aim was to secure the graduates’ continued engagement so that newly acquired expertise would translate into sustained contributions. The third aim focused on ecosystem growth, expecting alumni to serve as informed ambassadors who initiate projects, attract collaborators, and communicate both the advantages and constraints of decentralized science to wider audiences. Program efficacy was monitored through the systematic tracking of project sourcing, self-reported competence, participant retention, and evidence of alumni-driven network growth. Given the complete absence of formal educational material, the curriculum was developed entirely by the course instructors.

2.1.2 Implementation of DeSci EDU

The second iteration of Mol.Edu, “DeSci EDU,” was jointly developed by Molecule AG and Bio.xyz as an eight-week program of eight sequential modules covering foundational concepts and advanced applications of decentralized science. While the first iteration served mainly for internal onboarding, the revised program targeted external scientists. Four alumni from the original cohort, who are now active within the DeSci community, contributed to refining the syllabus, developing instructional materials, and mentoring new participants. Each week included synchronous lectures and discussions supported by prescribed readings, podcasts, and graded assignments, with an estimated commitment of five to seven hours.

The program culminated in a capstone project that required participants to apply course concepts to practical deliverables. The assignments were aligned with learners’ disciplinary backgrounds and reinforced through peer-to-peer sessions that encouraged interdisciplinary knowledge exchange. Course resources were distributed via Notion and Google Classroom. Progress was monitored on the same platforms, and community interactions were sustained using a dedicated Discord server. Mentoring was provided by the original course developers together with four Mol.Edu alumni who remain active in the DeSci community.

The primary target audience comprised graduate students and postdoctoral fellows engaged in scientific research, along with early-career professionals in the biotechnology and pharmaceutical industries. The program also sought to involve science communicators and marketing specialists, whose expertise is indispensable for outreach and community building, as well as Web3 and cryptocurrency enthusiasts interested in bridging blockchain technology and life science applications. To reach this diverse audience, recruitment relied on open calls disseminated from February 4 to 23, 2025, via X (formerly Twitter), LinkedIn, and Discord, which were further amplified within existing DAO communities. Applications were submitted through an online Typeform that required a curriculum vitae (CV) and a brief introductory video hosted on YouTube or Loom. The combination of written and audiovisual materials functioned as an initial quality filter, facilitating the identification of highly motivated candidates drawn from diverse disciplinary and professional backgrounds.

2.1.3 Participants and response flow

Of the 189 applications, 175 remained after removing duplicates and incomplete submissions; 38 participants were selected following brief interviews. Before the first session, 32 of the 38 enrollees completed the pre-course self-assessment. After the final session, 22 participants completed the post-course questionnaire (21 fully completed; 1 with partial missingness). Item-level analyses used all available responses; therefore, post-course denominators vary (n = 21–22 by item). Eleven respondents entered identical self-generated Research IDs at both time points, enabling a matched-pairs analysis.

2.2 Decentralized science perceived understanding self-assessment

After finalizing the syllabus, the Decentralized Science Perceived Understanding Self-Assessment was constructed to operationalize the learning objectives of the eight modules. Guided by the backward design model, assessment items were generated only after the desired learning outcomes had been explicitly defined, ensuring close alignment between instruction, evaluation, and the competencies the course sought to cultivate (Wiggins and McTighe, 2005). The self-assessment was drafted by the course instructors and subsequently revised based on qualitative feedback from an external specialist in higher-education curriculum design, who commented on item clarity and relevance.

2.2.1 Survey instrument and data matching

To evaluate participant learning outcomes, this study employed the Decentralized Science Perceived Understanding Self-Assessment, a structured questionnaire comprising eight multiple-choice knowledge items and 20 Likert-type statements that explore respondents’ self-assessed understanding of the core domains of blockchain technology and DeSci. The questionnaire began with sociodemographic items, including age band, country, highest qualification, and the frequency of research engagement. The principal section invited participants to rate their grasp of ten blockchain technology constructs (e.g., distributed ledger technology, smart contracts, and tokenomics) and ten DeSci-related domains (e.g., DAO governance in science and decentralized peer review).

All items were rated on a five-point Likert-oriented scale ranging from 1: “no understanding” to 5: “expert-level understanding.” The instrument also included sociodemographic questions capturing participants’ age category, country of residence, highest qualification, and frequency of engagement with scientific research. Item design followed the backward design model, ensuring direct alignment between the course learning objectives and the assessment constructs. Ten items measured perceived understanding of blockchain technology (B1–B10), and ten items measured perceived understanding of decentralized science (D1–D10), which are reported as the domains shown in Tables 2, 13 (see Supplementary Information). The complete instrument is described in Supplementary Information 1. The five-point Likert data are ordinal by design but were treated as approximately interval-level variables to enable the calculation of means and percentage changes across items. This analytic choice follows common practice in educational and program evaluation research, where multi-point Likert responses are frequently treated as approximately interval-level variables for descriptive estimation and basic comparative summaries, given their robustness in practice (Norman, 2010; Sullivan and Artino, 2013).

Table 2

Table 2. Average perceived understanding of blockchain technology values for pre- and post-course cohorts.

The survey data were collected online via the GDPR-compliant SoSci Survey platform. All participants provided informed consent for the collection, storage, and analysis of their responses for research purposes. To preserve anonymity while enabling longitudinal matching, each respondent generated a personal Research ID composed of a number, a word, and a color (for example, “7-ocean-green”) without submitting any identifying information to the researchers. Participants were instructed to enter the same Research ID in both the pre- and post-course questionnaires. Pre- and post-course responses were treated as coming from the same individual only if the Research ID string was identical at both time points; these cases formed a matched-pair subset (n = 11). The questionnaire remained open for a two-week window before the first teaching session and a four-week window after the course was completed.

Two complementary datasets were used for analysis:

1. Cohort-level comparison: All complete pre-course questionnaires (n = 32) were compared with all post-course responses (n ≤ 22 per item), providing a broad estimate of group-level learning gains.

2. Matched-pair analysis: A subset of participants (n = 11) who consistently entered identical Research IDs in both surveys allowed within-subject matching, enabling precise estimation of individual learning progression while maintaining anonymity. Sociodemographic characteristics are reported in Tables 3-6, while cryptocurrency and DeSci awareness and engagement are presented in Tables 7-10.

Table 3

Table 3. Age distribution of the first DeSci EDU cohort.

Table 4

Table 4. Reported country of residence.

Table 5

Table 5. Highest educational qualification.

Table 6

Table 6. Scientific research engagement frequency.

Table 7

Table 7. Cryptocurrency awareness.

Table 8

Table 8. Cryptocurrency engagement.

Table 9

Table 9. DeSci awareness.

Table 10

Table 10. DeSci engagement.

2.3 Data analysis environment and statistical software

All data cleaning was conducted using Microsoft Excel (version 16.82). The dataset included structured responses from pre- and post-course surveys along with basic demographic variables. Incomplete responses and entries marked as “Not answered” were excluded from the analysis. Only participants who fully completed pre- or post-course responses were retained. Descriptive statistics, such as mean values and respondent counts by age group, were calculated using R (version 4.5.1 for macOS). All analyses were executed from a versioned R script (R version 4.5.1 for macOS), ensuring that every table and figure can be regenerated from the raw survey exports. Visualizations were also created in MS Excel to support the interpretation. This analysis included only respondents who completed the survey instruments. This does not represent the overall course participation or completion rate. Of the 38 enrolled participants, 32 completed the pre-course questionnaire. Ten participants did not complete the post-course survey and were excluded from the post-course analyses. One additional submission was only partially completed but contained sufficient item coverage to be included in item-level analyses, yielding 21–22 valid post-course responses per item. As only 11 participants entered identical self-generated Research IDs at both time points, matched-pair analyses were limited to this subset; all remaining responses were included only in cohort-level (unpaired) summaries.

Formal hypothesis testing was not performed because the post-course cohort (n ≤ 22) was small and not independent from the pre-course group. This violated the independence and homoscedasticity assumptions underlying standard inferential tests, rendering p-values unstable and prone to inflated Type I errors. In line with methodological guidance that favors estimation when design constraints limit statistical power, the results are reported as descriptive point estimates (mean ± SD) with interquartile range (IQR) estimates to illustrate variability. This approach better conveys the magnitude and precision of the learning gains under these conditions.

The analyses included all fully completed pre-course (n = 32) and post-course (n = 21) questionnaires. Responses marked “Not answered” were excluded from the analysis. A single partially completed post-course questionnaire was retained only for item-level sensitivity summaries in which the relevant item was answered, yielding item-wise post-course denominators of n = 21–22. Imputation was not performed. Matched-pair analyses were restricted to records with identical self-generated Research IDs at both time points (n = 11). Given the small matched-pair sample size, measures of variability such as the standard deviation or interquartile range were not reported for this subset because they would be statistically unstable and not meaningfully interpretable.

2.4 Ethical considerations

A formal ethics board review was not required because data were collected anonymously via the GDPR-compliant professional server of the SoSci Survey (s2survey.net), which disables IP logging, cookies, and third-party access by default and expressly renounces any ownership or analysis of the respondent data. Participants generated self-selected Research IDs composed of a number, word, and color; provided no names, email addresses, or other identifiers; and answered only domain knowledge and self-perception items, with no sensitive personal information requested. All participants gave explicit informed consent for the collection, storage, and research use of their responses. Data were transmitted over the TLS/SSL, stored on encrypted servers in Germany, and scheduled for deletion from the SoSci Survey 12 months after study completion, with no backups retained thereafter.

3 Results

This section reports the results of the DeSci EDU pilot, detailing the course modules, cohort profile, and self-assessment outcomes.

3.1 DeSci EDU module overview

Based on the feedback from the pilot iteration, the DeSci EDU curriculum was restructured into eight sequential modules that advanced from foundational concepts to applied practice and synthesis. Module 1 introduced the rationale, scope, and history of DeSci. Modules 2–4 developed core literacy in Web3, cryptocurrencies, and blockchain technology-enabled intellectual property mechanisms. Module 5 addressed communication strategies, while Modules 6 and 7 focused on decentralized governance and the organization of BioDAOs. Module 8 concluded with an overview of emerging trends and an integrative reflection. Each module combined content delivery with defined learning outcomes, practical exercises, and assignments. A summary is presented in Table 11.

Table 11

Table 11. Overview of DeSci EDU modules.

This modular structure provided a progressive pathway from foundational literacy to applied practice, ensuring alignment between course design and the intended learning outcomes.

3.2 DeSci EDU implementation

Of the 189 initial applications, 175 remained after the removal of duplicates and incomplete submissions. Each valid applicant submitted a curriculum vitae and a short introductory video and was invited to a 15-min interview to assess their fit with the course objectives and manage expectations. Based on these evaluations, 38 candidates were selected for enrollment; others withdrew or declined owing to scheduling constraints or limited alignment with the intended learning outcomes of the course.

The evaluation relied on the Decentralized Science Perceived Understanding Self-Assessment administered immediately before the first teaching session and after the final session. Of the 38 enrollees, 32 completed the pre-course survey, and 22 completed the post-course questionnaire. Of those 22, 21 fully completed the questionnaire. For item-level analyses, all available responses were used, resulting in post-course denominators ranging from 21 to 22.

Eleven respondents entered the same self-generated Research ID at both time points, enabling anonymous within-subject linkage. Consequently, the evaluation combined (i) cohort-level comparisons between all pre-course responses (n = 32) and the available post-course data and (ii) matched-pair estimates for the 11 participants observed in both waves. The analytic strategy and rationale for focusing on estimation rather than formal hypothesis testing are detailed in Sections 2.2 and 2.3, respectively.

3.2.1 Sociodemographic information

The sociodemographic profile of the cohort is presented for age, country of residence, highest educational qualification, and frequency of engagement in scientific research.

3.2.1.1 Age distribution

Fifty-six percent of the respondents were younger than 35 years. The most common age category was 25–29 years (28%), followed by 35–39 years (25%). Only one participant (3%) was aged 50 years or older.

3.2.1.2 Country of residence

Participants reported living in 13 countries. Half of the cohort (50%) lived in Europe. Germany (21.9%) and the United Kingdom (12.5%) were the largest national groups. The United States accounted for 15.6% of the respondents. The remaining respondents were distributed across North America, Asia, Africa, and the Middle East.

3.2.1.3 Highest educational qualification

Post-secondary education was common. Of the respondents, 34.4% held an undergraduate science or technical degree, 34.4% held a master’s degree, and 25.0% held a doctorate or equivalent. Only two respondents (6.2%) reported no post-secondary science qualifications.

3.2.1.4 Scientific research engagement frequency

Very frequent engagement (almost daily) with scientific research was reported by 34.4% of respondents. A further 21.9% engaged weekly, and 12.5% engaged monthly. Seven participants (21.9%) engaged with research only a few times per year, and three (9.4%) reported never engaging.

3.2.2 Interest in cryptocurrency and DeSci

Summary statistics on respondents’ awareness of and active engagement with cryptocurrency and DeSci are presented below.

3.2.2.1 Cryptocurrency awareness

More than half of the respondents (53.1%) reported being aware of cryptocurrencies more than 5 years ago. Only two individuals (6.2%) became aware within the past 12 months, and none indicated complete unawareness.

3.2.2.2 Active cryptocurrency engagement

A little over one quarter of participants (28.1%) had never actively engaged with cryptocurrency, whereas an equal fraction (28.1%) reported active involvement for more than 5 years. The remaining 43.8% became active at some point during the preceding four-year period.

3.2.2.3 DeSci awareness

Awareness of DeSci was recent: 34.4% had become aware within the last 6 months, 25.0% within the past year, and 31.2% within the past 2 years.

3.2.2.4 Active DeSci engagement

Active participation in DeSci has been concentrated in the most recent period. A total of 43.8% had become involved within the last 6 months and 25.0% within the past 2 years, while 31.2% had not yet participated at the time of the pre-course survey.

3.2.3 Perceived understanding of blockchain technology

Following the analytic strategy outlined earlier, cohort-level averages are reported first (Section 3.2.3.1). This is followed by a within-subject analysis restricted to respondents with matched Research IDs (Section 3.2.3.2).

3.2.3.1 Average values for pre- and post-course cohorts

Scores were reported on a five-point scale (1 = no understanding; 5 = expert understanding). Pre-course means were calculated from 32 fully completed questionnaires. Post-course means included all non-missing answers; corresponding sample sizes are shown in square brackets. Δ represents the absolute difference between post- and pre-course means, and “% change” reflects the difference relative to the pre-course mean.

Averaging the ten items per questionnaire yielded an overall mean of 2.59 before the course and 3.66 after the course (22 post-course records), an increase of 1.07 points, which represents approximately a 41% gain on the five-point scale. All domains showed higher post-course means, with the largest relative improvements for decentralized identity (+60%) and smart contracts (+58%). Standard deviations were generally in the ∼0.7–1.2 range at both time points (item-wise post n = 21–22), indicating moderate variability across respondents. Estimated interquartile ranges (IQRs) were typically between 1.0 and 1.5 points, further supporting a moderate level of dispersion in perceived understanding scores.

3.2.3.2 Matched-pairs analysis (blockchain technology)

Responses were rated on a five-point scale. Eleven post-course questionnaires were matched with their corresponding pre-course counterparts through Research IDs. The means and within-subject differences (Δ) are shown in Table 12.

Table 12

Table 12. Matched-pairs analysis for pre- and post-course cohorts on blockchain technology.

Averaging the ten items within each questionnaire yielded an overall mean of 2.43 before the course and 3.67 after the course, an increase of 1.25 points, corresponding to a 51% gain on the five-point scale.

The post-course scores were higher for all blockchain domains. The largest relative improvements were observed for governance mechanisms (+81%), token-based incentives (+77%), and smart contracts (+59%), indicating substantial growth in learners’ perceived competence across key blockchain technology-enabled topics (Figure 1).

Figure 1

Figure 1. Matched-pair differences in blockchain understanding (n = 11 participants with linked Research IDs). Bar chart showing individual-level learning gains across ten blockchain domains. Each bar represents the mean difference (Δ) between pre- and post-course scores, along with the associated percentage increase. Data were derived from respondents who completed both surveys using self-generated Research IDs.

3.2.4 Perceived understanding of DeSci

Consistent with the approach used for blockchain technology, cohort-level averages are summarized first (Section 3.2.4.1), followed by matched-pairs results for the subset with linked Research IDs (Section 3.2.4.2).

3.2.4.1 Average values for pre- and post-course cohorts

Scores are reported on a five-point scale (1 = no understanding; 5 = expert understanding). Pre-course means are based on the 32 fully completed questionnaires. Post-course means included every non-missing answer. The number of valid ratings is indicated in square brackets. Δ represents the simple post-minus-pre difference, and “% change” expresses that difference relative to the pre-course mean.

Averaging the ten items within each questionnaire yielded an overall mean of 2.46 before the course and 3.76 after the course (22 post-course records), an increase of 1.30 points, corresponding to a 53% gain on the five-point scale. All ten DeSci domains exhibited higher post-course means. The largest relative improvements were observed for governance structures and decision-making (+71%), smart contract-based research automation (+70%), and decentralized peer review (+60%). Mean ± SD values are shown in Table 13; SDs typically fell between ∼0.7 and 1.2 at pre and post, reflecting moderate dispersion in self-ratings. Estimated interquartile ranges (IQRs) were in the ∼1.0–1.5 range, consistent with moderate variability across participants.

Table 13

Table 13. Average perceived understanding of DeSci for pre- and post-course cohorts.

3.2.4.2 Matched-pairs analysis (DeSci)

As above, scores were recorded on a five-point scale (1 = no understanding, 5 = expert understanding). Eleven post-course questionnaires were matched to their corresponding pre-course entries using self-generated Research IDs. Reported metrics included mean scores, within-subject differences (Δ), and percentage changes relative to the pre-course means (Table 14).

Table 14

Table 14. Matched-pairs analysis for pre- and post-course cohorts on DeSci.

All ten domains showed high relative improvements, with the largest improvements observed for decentralized peer review (+86%), governance structures (+78%), and smart contract-based research automation (+68%). The composite score increased by 1.37 points, with an average increase of 58 % on the five-point scale, indicating a substantial enhancement in participants’ perceived ability to apply decentralized technologies to scientific practice (Figure 2).

Figure 2

Figure 2. Within-subject gains in DeSci understanding (n = 11 matched participants). The bar chart illustrates the mean difference (Δ) in ratings for ten DeSci concepts among respondents with matched pre- and post-survey data. The largest relative increases were observed in decentralized peer review (+86%) and governance structures (+78%).

4 Discussion

The DeSci EDU pilot demonstrates that structured, theory-practice integrated instruction can rapidly advance learner competence in emerging interdisciplinary domains that lack established educational pathways. Rather than simply documenting knowledge gains, this evaluation illuminates three critical insights for blockchain education scholarship: first, blended pedagogies combining synchronous instruction with scaffolded practice outperform fragmented approaches common in Web3 education; second, that alumni mentorship accelerates the appropriation of specialized discourse communities; and third, that governance-focused curricula address a systematic gap in blockchain technology literacy frameworks (Weidener and Lukács, 2025). These findings position DeSci education within broader debates about how emerging technologies are reshaping learning ecologies and professional development (Darling-Hammond et al., 2020; National Research Council, 2012).

The eight-week DeSci EDU module structure exemplifies the pedagogical principles that research on contemporary blockchain technology education identifies as necessary but rarely implemented (Kabashi et al., 2023; Patan et al., 2023). Most university-level blockchain technology courses remain either conceptually superficial, emphasizing cryptocurrency speculation and market dynamics, or narrowly technical, focusing exclusively on smart contract programming without an epistemic or organizational context (Themistocleous et al., 2020; Weidener and Lukács, 2025). This bifurcation leaves learners ill-equipped for participatory roles in DeSci, which requires simultaneous fluency in research methodology, distributed system architecture, and collective governance mechanisms. By deliberately coupling theory-focused seminars with practice-oriented assignments, DeSci EDU operationalizes the integrated curriculum model that systematic reviews advocate but documentation rarely demonstrates (Kabashi et al., 2023; Collins et al., 2016).

Beyond DeSci EDU, established Web3 education programs illustrate the prevailing focus of the current curricula. The Roster Mini MBA in Web3 is a 10+ week, self-paced certificate that covers Web3 fundamentals, cryptocurrency, and DeFi, DAOs, NFTs, metaverse applications, strategy and culture, and compliance and is marketed primarily to industry professionals seeking career-oriented Web3 credentials (Roster, 2025). The University of Nicosia’s MSc in blockchain and digital currency is a three-semester, 90-ECTS distance-learning degree that combines courses in digital currencies, blockchain systems and architectures, token economics, law and regulation, and entrepreneurship to prepare graduates for careers at the intersection of finance, management, and computer science (University of Nicosia, 2025). In contrast, DeSci EDU treats blockchain as an infrastructure for scientific work and structures its modules around BioDAO governance, token-based research funding, decentralized peer review, and the practical design and operation of DeSci projects.

The curriculum’s deliberate progression from foundational concepts to applied synthesis reflects backward design principles that prioritize learning outcomes over content coverage (Wiggins and McTighe, 2005). Each module builds on prior knowledge while introducing incrementally more complex tasks, such as an introduction to DAOs, that precede the detailed governance implementation strategies. This scaffolding approach aligns with sociocultural learning theory’s emphasis on calibrating instructional support within learners’ zones of proximal development (Lantolf, 2006; Vygotsky, 1978). The substantial gains in domains receiving extended practice, governance structures, token-based funding, and decentralized peer review suggest that sustained engagement with authentic artifacts, rather than passive content consumption, drives competence development in complex sociotechnical systems (Lave and Wenger, 1991; Brown et al., 1989).

4.1 DeSci EDU implementation

The cohort composition reveals both the promises and challenges of DeSci education. Participants’ youth, advanced scientific credentials, and intensive research engagement indicate a knowledge-rich audience predisposed to capitalize on sophisticated instruction. However, this demographic concentration, predominantly early-career researchers under 35 years with graduate training, simultaneously highlights a persistent adoption barrier: established scientists with greater institutional influence remain underrepresented. This pattern mirrors technology diffusion research, which demonstrates that innovations typically penetrate professional communities through early adopters before reaching pragmatic majority levels (Rogers et al., 2014). Therefore, the challenge for DeSci education extends beyond curriculum design to recruitment strategies that lower participation costs for senior researchers, who face higher opportunity costs and potentially greater cognitive resistance to paradigm shifts (Kuhn and Hacking, 1970; Christensen, 2015).

Geographic diversity spanning 13 nations positions DeSci as an inherently global movement, yet the European concentration (50% of respondents) suggests uneven awareness and access. This distribution partially reflects structural factors: many prominent BioDAOs and DeSci infrastructure projects originate in European and North American innovation hubs (Fantaccini et al., 2024; Weidener and Spreckelsen, 2024). This pattern also indicates opportunities for targeted outreach in underrepresented regions. Research on international collaboration in decentralized systems suggests that geographic diversity enhances governance legitimacy and reduces coordination failures stemming from time-zone clustering and regulatory homogeneity (De Filippi and Hassan, 2018; Ziolkowski et al., 2020). Therefore, future iterations should prioritize multilingual materials and asynchronous participation options that accommodate global learners (Jordan, 2014).

4.1.1 Interest in cryptocurrency and DeSci

The divergence between cryptocurrency familiarity and DeSci engagement highlights a critical insight: generic Web3 literacy does not automatically translate into domain-specific competencies. Over half of the participants encountered cryptocurrencies more than 5 years ago, yet nearly 70% had only learned about DeSci within the past year. This temporal gap indicates that DeSci represents a second-layer application emerging after the core blockchain technology infrastructure matures, a pattern consistent with technology adoption lifecycles, wherein enabling platforms precede specialized use cases (Arthur, 1994; Gawer and Cusumano, 2014).

More importantly, regarding the engagement gap, 28% had never transacted with cryptocurrency, and 31% did not participate in DeSci, suggesting that awareness alone is insufficient for adoption. This finding resonates with information literacy research demonstrating that declarative knowledge (“knowing about”) differs fundamentally from procedural knowledge (“knowing how”) and conditional knowledge (“knowing when and why”) (Anderson and Krathwohl, 2001; Flavell, 1979). DeSci EDU’s emphasis on hands-on tasks, wallet configuration, Discord navigation, and governance voting deliberately cultivates procedural fluency, transforming abstract concepts into embodied practices. This pedagogical choice reflects situated cognition theory’s argument that learning is fundamentally a process of enculturation into communities of practice, requiring legitimate peripheral participation in authentic activities rather than decontextualized instruction (Brown et al., 1989; Lave and Wenger, 1991).

The concentration of recent DeSci awareness and engagement validates the movement’s characterization as emergent rather than established (Ding et al., 2022; Weidener and Spreckelsen, 2024). Educational interventions during the formative stages of field development can exert a disproportionate influence on community norms, governance structures, and epistemic standards (Douglas, 2012; Jasanoff, 2004). By training early contributors when DeSci conventions remain fluid, programs such as DeSci EDU can potentially shape the field’s trajectory. This responsibility underscores the importance of embedding ethical reflection, regulatory foresight, and inclusive design principles within technical instruction (Ghodous et al., 2024).

4.1.2 Perceived understanding of blockchain technology

The pronounced improvements in governance mechanisms and token-based incentives of participants address a critical gap in blockchain technology education portfolios (Ghodous et al., 2024; Patan et al., 2023). The CHAISE sector blueprint for European blockchain technology workforce development emphasizes that technical proficiency alone is insufficient; professionals must also master regulatory compliance, ethical deliberation, and stakeholder communication (Ghodous et al., 2024; CHAISE Consortium, 2024). However, most university blockchain technology courses prioritize coding skills and cryptographic protocols over organizational design and incentive alignment (Patan et al., 2023; Themistocleous et al., 2020). This curricular imbalance leaves graduates unprepared for roles in DAOs, token-governed projects, and decentralized governance systems, precisely in the contexts in which DeSci operates (Weidener et al., 2024).

DeSci EDU’s intensive focus on governance through Modules 6 and 7, which required learners to join multiple BioDAOs, analyze live proposals, and draft resource allocation frameworks, directly cultivates competencies that standard Web3 education neglects (Ghodous et al., 2024). The substantial gains in these domains validate design-based research demonstrating that extended practice with authentic artifacts, actual DAO governance proposals, real voting mechanisms, and genuine community deliberations produces deeper conceptual understanding than simulation or case study analysis (Barab and Squire, 2016; Collins et al., 2016). Participants did not merely learn about governance; they enacted governance and experienced firsthand coordination challenges, information asymmetries, and collective action problems that theoretical instruction can only describe (De Filippi and Hassan, 2018).

The advances in decentralized identity and smart contracts similarly reflect the curriculum’s emphasis on emerging primitives, which mainstream blockchain education has not yet incorporated. Decentralized identity systems, such as soulbound tokens and verifiable credentials, represent recent innovations that scholarship is only beginning to analyze (Ohlhaver et al., 2022). By exposing learners to cutting-edge developments, DeSci EDU positions graduates at the frontier of blockchain technology applications rather than teaching historical concepts that may soon become obsolete, a forward-looking orientation that distinguishes specialized professional training from general-purpose computer science education (Patan et al., 2023).

The smaller relative gains in open principles and distributed ledger technology, while potentially reflecting ceiling effects given higher baselines, also suggest that certain blockchain fundamentals have achieved broader cultural penetration. Open-source software development, open-access publishing, and distributed systems concepts have now circulated widely in scientific and technical communities (Nielsen, 2020; Kelty, 2008). This prior familiarity accelerates DeSci onboarding but also creates pedagogical challenges: learners may assume they understand concepts like “decentralization” or “transparency” while lacking technical precision about how blockchain technology implementations differ from earlier open science initiatives (Weidener and Spreckelsen, 2024). Addressing this overconfidence requires explicit instruction to distinguish protocol-level guarantees (immutability and cryptographic verification) from social norms (voluntary data sharing and informal collaboration), a distinction that DeSci EDU’s technical modules are systematically reinforced through comparative analysis assignments (Wiggins and McTighe, 2005).

4.1.3 Perceived understanding of decentralized science

The substantial DeSci-specific understanding increases suggest the validation of the curriculum’s core premise that bridging blockchain technology and science requires dedicated interdisciplinary instruction rather than expecting learners to synthesize disparate knowledge independently. The largest improvements in governance structures and decentralized peer review correspond precisely to topics receiving extended workshop time and capstone project attention. This alignment between instructional emphasis and learning outcomes demonstrates a fundamental educational principle: time on task predicts achievement (Carroll, 1963; Gettinger and Seibert, 2002). However, this alignment also reveals strategic curricular choices. DeSci EDU prioritized governance and funding mechanisms over other potential topics because these domains most directly address the structural inefficiencies in traditional research systems that DeSci aims to transform (Ding et al., 2022; Weidener and Spreckelsen, 2024).

The importance of governance-related gains warrants further examination. Decentralized governance represents perhaps the most radical departure from conventional scientific organization, which relies on hierarchical institutions, credentialed expertise, and peer review by established authorities (Merton, 1973; Ziman, 2001). DAOs distribute decision-making authority across token-holding communities, enabling permissionless participation but also introducing coordination costs, information asymmetries, and plutocratic risks (De Filippi and Hassan, 2018; Voshmgir, 2025). Delegated voting mechanisms add complexity to governance structures, presenting trade-offs between participation efficiency and power concentration (Weidener et al., 2025). Understanding these trade-offs requires not only technical literacy about how voting mechanisms function and how proposals reach consensus but also institutional analysis: when does decentralization enhance, rather than undermine, research quality? Which decisions benefit from distributed deliberation versus expert judgment? These questions require critical thinking skills that transcend technical knowledge (Engeström et al., 1999). DeSci EDU addressed these questions through comparative analysis assignments requiring learners to evaluate actual DAO governance structures, identify design trade-offs, and propose modifications aligned with scientific norms. This pedagogical approach reflects deliberate practice frameworks, emphasizing that expertise develops through repeated cycles of performance, feedback, and reflection on increasingly complex tasks (Ericsson et al., 1993; Chi, 2009). Merely explaining governance theory produces declarative knowledge; in contrast, analyzing real proposals, participating in Discord debates, and drafting funding frameworks produces the procedural and conditional knowledge required for effective participation (Anderson and Krathwohl, 2001; Flavell, 1979).

The pronounced improvement in understanding decentralized peer review similarly addresses a domain where DeSci’s potential to transform research practices generates enthusiasm and skepticism (Morales-Alarcón et al., 2024). Traditional peer reviews face well-documented problems such as delays, bias, lack of accountability, and misaligned incentives (Smith, 2006; Tennant and Ross-Hellauer, 2020). Blockchain technology-enabled alternatives propose using token incentives, reputation systems, and transparent review histories to mitigate these failures (Tenorio-Fornés et al., 2019; Morales-Alarcón et al., 2024). However, operationalizing a decentralized review requires resolving complex questions about verifying reviewer expertise, establishing quality control mechanisms, and preventing Sybil attacks (Douceur, 2002). DeSci EDU’s Module 8 engaged in these challenges through critical evaluation of existing decentralized review platforms, analysis of their governance structures, and reflection on their epistemic implications, an analytical approach that develops not only operational competence but also critical judgment about when and where decentralized mechanisms appropriately apply (Engeström et al., 1999; Chi, 2009).

Four Mol.Edu graduates who remained active in DeSci communities helped refine the curriculum, led workshops, and supervised capstone projects, creating a mentorship structure that embedded newcomers in practice, facilitated the transmission of tacit knowledge alongside formal instruction, and deepened alumni understanding, communication skills, and networks (Lave and Wenger, 1991; Wenger, 1999; Collins, 2010; Polanyi, 2009; National Research Council, 2000; National Research Council, 2012; Chase et al., 2009; Roscoe and Chi, 2007; Fischhoff, 2013). Coupled with practical assignments such as proof-of-invention registration, BioDAO funding proposal analysis, and tokenomics evaluation, this mentorship model fostered operational competence, adaptability, and metacognitive skills, which are increasingly viewed as the most durable outcomes of emerging technology education (Schön, 2017; Darling-Hammond et al., 2020; National Research Council, 2012; Zimmerman, 2002).

4.2 Limitations

The evaluation is subject to several methodological and contextual constraints that influence the strength of the inferences drawn. The study employed a single-group pre-test and post-test design without a comparison cohort; maturation, contemporaneous events, or simple test familiarity may, therefore, account for some of the observed gains (Shadish et al., 2002). Additionally, enrollment was voluntary and attracted a young, highly educated audience already inclined toward Web3, introducing self-selection effects and restricting external validity. Attrition compounded this bias: only 11 participants supplied matching Research IDs, yielding limited statistical power and increasing the likelihood that completers differed systematically from non-completers in motivation or prior knowledge (Rovai, 2003; Jordan, 2014).

These measurement choices introduced additional limitations. Outcomes relied exclusively on self-assessed understanding, a format vulnerable to common method variance and social desirability artifacts (Podsakoff et al., 2003). Meta-analytic work indicates only moderate correspondence between perceived and demonstrated competence, particularly for emergent or loosely defined constructs, such as Web3 infrastructure (Falchikov and Boud, 1989). Furthermore, the response scale showed ceiling effects for topics with higher baselines (open-principles literacy) and floor effects for novel domains (decentralized identity), reducing sensitivity at extremes.

Course design features also shape the interpretation. The eight-week schedule prioritized governance and funding modules, leaving less time for transversal competences emphasized by the CHAISE blueprint, such as regulatory foresight and stakeholder communication (Ghodous et al., 2024). Mentorship sessions, while valuable for situated mediation, may have inadvertently introduced halo effects; alumni mentors who also served as assessors could have influenced learners’ confidence ratings through affirmation and feedback loops, inflating perceived gains. The absence of a blinded evaluation further increased this risk. Cultural and disciplinary heterogeneity within the cohort raises additional concerns about item equivalence; terminology such as “tokenomics” or “peer review” may hold divergent nuances for software engineers, life scientists, and business professionals, despite standardized wording. Finally, DeSci is a rapidly evolving construct lacking consensus definitions, which complicates longitudinal comparisons. Continuous refinement of the survey through expert Delphi panels and cognitive interviews is recommended, along with the inclusion of objective performance indicators and a controlled study arm. These adjustments would mitigate current biases, enhance causal attribution, and support more generalizable conclusions about the efficacy and scalability of the DeSci EDU.

5 Conclusion

This case study suggests that a blended curriculum can improve the perceived understanding of blockchain fundamentals and DeSci-specific applications across diverse professional backgrounds, with the largest gains in governance, token-based funding, and decentralized peer review. These outcomes align with designs that combine theory-focused seminars, scaffolded practice, and mentorship. However, self-selection, reliance on self-report, and a modest matched-pairs sample limit causal claims and underline the need for objective performance measures and broader recruitment criteria. Future iterations should integrate transversal skills, such as regulatory foresight and science communication, use automated identifiers for longitudinal linkage, and embed task-based assessments that capture on-chain behavior to enable a more rigorous evaluation and scalable deployment of DeSci education initiatives.

Data availability statement

The raw data supporting the conclusions of this article are available from the authors upon request.

Ethics statement

Ethical approval was not required for the studies involving humans because data were collected anonymously via the GDPR-compliant professional server of the SoSci Survey (s2survey.net), which disables IP logging, cookies, and third-party access by default and expressly renounces any ownership or analysis of the respondent data. Participants generated self-selected Research IDs composed of a number, word, and color; provided no names, email addresses, or other identifiers; and answered only domain knowledge and self-perception items, with no sensitive personal information requested. All participants gave explicit informed consent for the collection, storage, and research use of their responses. Data were transmitted over the TLS/SSL, stored on encrypted servers in Germany, and scheduled for deletion from the SoSci Survey 12 months after study completion, with no backups retained thereafter. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

SE: Conceptualization, Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review and editing. KK: Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review and editing. FL: Writing – original draft, Writing – review and editing. KC: Writing – original draft, Writing – review and editing. CY: Writing – original draft, Writing – review and editing. LB-C: Writing – original draft, Writing – review and editing. EM-P: Writing – original draft, Writing – review and editing. BL: Methodology, Writing – original draft, Writing – review and editing. LW: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – original draft, Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The DeSci EDU course and its evaluation were supported by Molecule AG and Bio.xyz.

Acknowledgements

The authors thank all participants of the DeSci EDU pilot course for their commitment and feedback, which were essential to the development and evaluation of the program.

Conflict of interest

Authors SE, KK, FL, KC, CY, LB-C and EM-P were employed by Molecule AGz during the design and delivery of the DeSci EDU course and received compensation for their involvement.

Author LW was employed by Bio.xyz during the design and delivery of the DeSci EDU course and received compensation for their involvement.

The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was used in the creation of this manuscript. During the preparation of this manuscript, the authors used OpenAI’s ChatGPT (version 4o) to assist with grammar correction, spelling, formatting, and reformulation of selected passages for clarity and style. All content generated through this tool was critically reviewed, edited, and approved by the authors. The authors take full responsibility for the integrity and accuracy of the final manuscript.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbloc.2025.1693540/full#supplementary-material

References

Anderson, L. W., and Krathwohl, D. R. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives: Complete Edition. New York: Addison Wesley Longman, Inc.

Google Scholar

Arthur, W. B. (1994). Increasing Returns and Path Dependence in the Economy. Ann Arbor, University of Michigan Press.

CrossRef Full Text | Google Scholar

Barab, S., and Squire, K. (2016). “Design-based research: putting a stake in the ground,” in Design-Based Research (Hove, United Kingdom: Psychology Press), 1–14. doi:10.1207/s15327809jls1301_1

CrossRef Full Text | Google Scholar

Brown, J. S., Collins, A., and Duguid, P. (1989). Situated cognition and the culture of learning. Educ. Res. 18 (1), 32–42. doi:10.3102/0013189X018001032

CrossRef Full Text | Google Scholar

Budd, J. M., and Lloyd, A. (2014). “Theoretical foundations for information literacy: a plan for action,” in Proceedings of the Association for Information Science and Technology Annual Meeting, 1–4. doi:10.1002/meet.2014.14505101001

CrossRef Full Text | Google Scholar

Carroll, J. B. (1963). A model of school learning. Teach. College Record 64 (8), 1–9. doi:10.1177/016146816306400801

CrossRef Full Text | Google Scholar

CHAISE Consortium (2024). The registry of blockchain educational and training offerings. Available online at: https://chaise-blockchainskills.eu/registry-of-blockchain-educational-and-training-offerings/ (Accessed August 15, 2025).

Google Scholar

Chase, C. C., Chin, D. B., Oppezzo, M. A., and Schwartz, D. L. (2009). Teachable agents and the protégé effect: increasing the effort towards learning. J. Science Education Technology 18 (4), 334–352. doi:10.1007/s10956-009-9180-4

CrossRef Full Text | Google Scholar

Chi, M. T. (2009). Active-constructive-interactive: a conceptual framework for differentiating learning activities. Top. Cognitive Science 1 (1), 73–105. doi:10.1111/j.1756-8765.2008.01005.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Christensen, C. M. (2015). The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail. Boston, Harvard Business Review Press.

Google Scholar

Collins, H. (2010). “Tacit and explicit knowledge,” in Tacit and Explicit Knowledge (University of Chicago press).

Google Scholar

Collins, A., Joseph, D., and Bielaczyc, K. (2016). “Design research: theoretical and methodological issues,” in Design-Based Research (Hove, United Kingdom: Psychology Press), 15–42.

Google Scholar

Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., and Osher, D. (2020). Implications for educational practice of the science of learning and development. Appl. Developmental Science 24 (2), 97–140. doi:10.1080/10888691.2018.1537791

CrossRef Full Text | Google Scholar

De Filippi, P., and Hassan, S. (2018). Blockchain technology as a regulatory technology: from code is law to law is code. arXiv Preprint Arxiv1801.02507. doi:10.5210/fm.v21i12.7113

CrossRef Full Text | Google Scholar

Ding, W., Hou, J., Li, J., Guo, C., Qin, J., Kozma, R., et al. (2022). DeSci based on Web3 and DAO: a comprehensive overview and reference model. IEEE Trans. Comput. Soc. Syst. 9 (5), 1563–1573. doi:10.1109/TCSS.2022.3204745

CrossRef Full Text | Google Scholar

Douceur, J. R. (2002). “The sybil attack,” in International Workshop On Peer-to-Peer Systems (Berlin, Heidelberg: Springer Berlin Heidelberg), 251–260. doi:10.1007/3-540-45748-8_24

CrossRef Full Text | Google Scholar

Douglas, D. G. (2012). The Social Construction of Technological Systems, Anniversary Edition: New Directions in the Sociology and History of Technology. MIT press.

Google Scholar

Y. Engeström, R. Miettinen, and R. L. Punamäki-Gitai (1999). Perspectives on Activity Theory (Cambridge University Press).

Google Scholar

Ericsson, K. A., Krampe, R. T., and Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychol. Review 100 (3), 363–406. doi:10.1037/0033-295X.100.3.363

CrossRef Full Text | Google Scholar

Falchikov, N., and Boud, D. (1989). Student self-assessment in higher education: a meta-analysis. Rev. Educ. Res. 59 (4), 395–430. doi:10.3102/00346543059004395

CrossRef Full Text | Google Scholar

Fantaccini, S., Grassi, L., and Rampoldi, A. (2024). The potential of DAOs for funding and collaborative development in the life sciences. Nature Biotechnology 42 (4), 555–562. doi:10.1038/s41587-024-02189-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Fischhoff, B. (2013). The sciences of science communication. Proc. Natl. Acad. Sci. 110 (Suppl. ment_3), 14033–14039. doi:10.1073/pnas.1312080110

PubMed Abstract | CrossRef Full Text | Google Scholar

Flavell, J. H. (1979). Metacognition and cognitive monitoring: a new area of cognitive–developmental inquiry. Am. Psychologist 34 (10), 906–911. doi:10.1037/0003-066X.34.10.906

CrossRef Full Text | Google Scholar

Gawer, A., and Cusumano, M. A. (2014). Industry platforms and ecosystem innovation. J. Product Innovation Management 31 (3), 417–433. doi:10.1111/jpim.12105

CrossRef Full Text | Google Scholar

Gettinger, M., and Seibert, J. K. (2002). Contributions of study skills to academic competence. Sch. Psychology Review 31 (3), 350–365. doi:10.1080/02796015.2002.12086160

CrossRef Full Text | Google Scholar

Ghodous, P., Biennier, F., Ehlers, U. D., Kelly, L., Whelan, A., and Greplova, B. (2024). “CHAISE: a blueprint for sectoral cooperation on blockchain skill development,” in 36th International Conference on Advanced Information Systems Engineering, 51–59.

Google Scholar

S. Jasanoff (2004). States of Knowledge (Abingdon, UK: Taylor and Francis).

Google Scholar

Jordan, K. (2014). Initial trends in enrolment and completion of massive open online courses. Int. Rev. Res. Open Distributed Learn. 15 (1), 133–160. doi:10.19173/irrodl.v15i1.1651

CrossRef Full Text | Google Scholar

Kabashi, F., Snopce, H., Aliu, A., Luma, A., and Shkurti, L. (2023). “A systematic literature review of blockchain for higher education,” in 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD) (IEEE), 1–6. doi:10.1109/ITIKD56332.2023.10100049

CrossRef Full Text | Google Scholar

Kalman, J. (2008). Beyond definition: literacy and communication technologies: distance education strategies for literacy delivery, 54, 523–538. doi:10.1007/s11159-008-9100-5

CrossRef Full Text | Google Scholar

Kelty, C. M. (2008). “Two bits: the cultural significance of free software,” in Two Bits (Durham: Duke University Press).

Google Scholar

Kuhn, T. S., and Hacking, I. (1970). The structure of scientific revolutions. Univ. Chic. Press 2 (2), 310.

Google Scholar

Lantolf, J. P. (2006). Sociocultural theory and L2: state of the art. Stud. Second Language Acquisition 28 (1), 67–109. doi:10.1017/s0272263106060037

CrossRef Full Text | Google Scholar

Lave, J., and Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge: Cambridge University Press.

Google Scholar

Merton, R. K. (1973). The Sociology of Science: Theoretical and Empirical Investigations. University of Chicago press.

Google Scholar

Morales-Alarcón, C. H., Bodero-Poveda, E., Villa-Yánez, H. M., and Buñay-Guisñan, P. A. (2024). Blockchain and its application in the peer review of scientific works: a systematic review. Publications 12 (4), 40. doi:10.3390/publications12040040

CrossRef Full Text | Google Scholar

National Research Council (2000). How People Learn: Brain, Mind, Experience, and School: Expanded Edition. Washington, DC: The National Academies Press. doi:10.17226/9853

CrossRef Full Text | Google Scholar

National Research Council (2012). in Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century (Washington, DC: The National Academies Press). doi:10.17226/13398

CrossRef Full Text | Google Scholar

Nielsen, M. (2020). in Reinventing Discovery: The New Era of Networked Science.

Google Scholar

Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Adv. Health Sciences Education Theory Practice 15 (5), 625–632. doi:10.1007/s10459-010-9222-y

PubMed Abstract | CrossRef Full Text | Google Scholar

Ohlhaver, P., Weyl, E. G., and Buterin, V. (2022). Decentralized Society: Finding Web3's Soul. SSRN 4105763.

Google Scholar

Patan, R., Parizi, R. M., Dorodchi, M., Pouriyeh, S., and Rorrer, A. (2023). Blockchain education: current state, limitations, career scope, challenges, and future directions. arXiv Preprint arXiv:2301.07889. doi:10.48550/arXiv.2301.07889

CrossRef Full Text | Google Scholar

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., and Podsakoff, N. P. (2003). Common method biases in behavioural research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88 (5), 879–903. doi:10.1037/0021-9010.88.5.879

PubMed Abstract | CrossRef Full Text | Google Scholar

Polanyi, M. (2009). “The tacit dimension,” in Knowledge in Organisations (London, Routledge), 135–146.

Google Scholar

Reder, S., and Davila, E. (2005). Context and literacy practices. Annu. Rev. Appl. Linguistics 25, 170–187. doi:10.1017/S0267190505000097

CrossRef Full Text | Google Scholar

Rogers, E. M., Singhal, A., and Quinlan, M. M. (2014). “Diffusion of innovations,” in An Integrated Approach to Communication Theory And Research (New York, Routledge), 432–448.

Google Scholar

Roscoe, R. D., and Chi, M. T. (2007). Understanding tutor learning: knowledge-building and knowledge-telling in peer tutors’ explanations and questions. Rev. Educational Research 77 (4), 534–574. doi:10.3102/0034654307309920

CrossRef Full Text | Google Scholar

Roster (2025). Mini MBA in Web3. Roster. Available online at: https://roster3.com/mini-mba-web3 (Accessed August 15, 2025).

Google Scholar

Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. Internet High. Educ. 6 (1), 1–16. doi:10.1016/S1096-7516(02)00158-6

CrossRef Full Text | Google Scholar

Schön, D. A. (2017). The Reflective Practitioner: How Professionals Think in Action. London: Routledge.

Google Scholar

Shadish, W. R., Cook, T. D., and Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.

Google Scholar

Smith, R. (2006). Peer review: a flawed process at the heart of science and journals. J. Royal Society Medicine 99 (4), 178–182. doi:10.1258/jrsm.99.4.178

PubMed Abstract | CrossRef Full Text | Google Scholar

Sullivan, G. M., and Artino, A. R., Jr (2013). Analyzing and interpreting data from likert-type scales. J. Graduate Medical Education 5 (4), 541–542. doi:10.4300/JGME-5-4-18

PubMed Abstract | CrossRef Full Text | Google Scholar

Tennant, J. P., and Ross-Hellauer, T. (2020). The limitations to our understanding of peer review. Res. Integrity Peer Review 5 (1), 6. doi:10.1186/s41073-020-00092-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Tenorio-Fornés, A., Jacynycz, V., Llop-Vila, D., Sánchez-Ruiz, A., and Hassan, S. (2019). Towards a Decentralized Process for Scientific Publication and Peer Review Using Blockchain and IPFS.

Google Scholar

Themistocleous, M., Christodoulou, K., Iosif, E., Louca, S., and Tseas, D. (2020). Blockchain in academia: where do we stand and where do we go? HICSS, 1–10. doi:10.24251/HICSS.2020.656

CrossRef Full Text | Google Scholar

University of Nicosia (2025). MSc in Blockchain and Digital Currency. University of Nicosia. Available online at: https://www.unic.ac.cy/iff/education-and-training/master-degrees/msc-in-blockchain-and-digital-currency/ (Accessed August 15, 2025).

Google Scholar

Voshmgir, S. (2025). “Token economy,” in Token Kitchen, Labirinto Ecletico Unip LDA.

Google Scholar

Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press. doi:10.2307/j.ctvjf9vz4

CrossRef Full Text | Google Scholar

Weidener, L., and Lukács, B. (2025). Development of the blockchain technology literacy test (BTLT): a scoping review of current literature. Ledger 10. doi:10.5195/ledger.2025.401

CrossRef Full Text | Google Scholar

Weidener, L., and Spreckelsen, C. (2024). Decentralized science (DeSci): definition, shared values, and guiding principles. Front. Blockchain 7, 1375763. doi:10.3389/fbloc.2024.1375763

CrossRef Full Text | Google Scholar

Weidener, L., Greilich, K., and Melnykowycz, M. (2024). Adapting Mintzberg’s organizational theory to DeSci: the decentralized science pyramid framework. Front. Blockchain 7, 1513885. doi:10.3389/fbloc.2024.1513885

CrossRef Full Text | Google Scholar

Weidener, L., Laredo, F., Kumar, K., and Compton, K. (2025). Delegated voting in decentralized autonomous organizations: a scoping review. Front. Blockchain 8, 1598283. doi:10.3389/fbloc.2025.1598283

CrossRef Full Text | Google Scholar

Wenger, E. (1999). Communities of Practice: Learning, Meaning, and Identity. Cambridge: Cambridge University Press.

Google Scholar

Wiggins, G., and McTighe, J. (2005). “Understanding by design,” in Association for Supervision and Curriculum Development (Alexandria, VA: Association for Supervision and Curriculum Development (ASCD)).

Google Scholar

Ziman, J. (2001). Real Science: What it is, and what it means. Cambridge University Press.

CrossRef Full Text | Google Scholar

Zimmerman, B. J. (2002). Becoming a self-regulated learner: an overview. Theory Into Practice 41 (2), 64–70. doi:10.1207/s15430421tip4102_2

CrossRef Full Text | Google Scholar

Ziolkowski, R., Miscione, G., and Schwabe, G. (2020). Decision problems in blockchain governance: old wine in new bottles or walking in someone else’s shoes? J. Manag. Inf. Syst. 37 (2), 316–348. doi:10.1080/07421222.2020.1759974

CrossRef Full Text | Google Scholar