1. Introduction
Enhancing the student’s experience with online learning platforms is a goal of many educational institutions at any school or university level. Providing students with online materials, activities, courses and feedback can improve students’ achievements and satisfaction even if the main part of the teaching is organized in traditional physical classrooms [
1]. School and university closures caused by the COVID-19 pandemic have led all educational organizations to adopt online teaching and learning platforms, experiencing the advantages (and the barriers too) of online classes [
2] to the point that many of them have decided to maintain e-Learning tools even after the recovery of in presence activities [
3]. Among benefits, educational institutions appreciate in particular the potential of digital technologies to provide rapid feedback to students, supply continuous assessment environments, enable students to share educational achievements both through informal channels (for example, social networks) and formal certifications. As we know, assessment and evaluation have a central role to measure the level of transmission of knowledge and expertise to the student [
4,
5].
Concerning blockchain technology, we found five main areas of application for the education domain. (1) Certificate/degree verification: studies of how blockchain can assist institutions in validating student diplomas and can provide better control over how students earn certificates [
6,
7,
8]. (2) Students’ assessments and exams: automated mechanisms for the organization of exams and assessment schemes for students [
9,
10]. (3) Credit transfer: applications for storing student records and transcripts, and transferring credits between educational institutions [
11,
12]. (4) Data management: applications for connecting students’ records across institutions as well as exchanging smart contracts for managing students’ data [
13,
14]. (5) Admissions: applications for facilitating students in providing documents when applying to universities [
15,
16]. Beyond clear benefits, researchers are facing a number of problems in exploiting blockchain technology for education, the most important for our purpose concern the usability (the terminology is often unclear and the user may have to deal with several complicated settings) [
6,
8], the immutability (that is a challenge for some processes, such as diploma revocation) [
12], and the scalability (increasing the number of participants as educational institutions, students, market players) [
14,
15].
Open Badges are fundamentally digital entities that can be earned both online and offline, and that indicate the learner’s achievements (formal and informal), providing information about the issuing institution [
17]. Badges are created, issued and managed through an open-source system, named Open Badge Infrastructure, conceived by the Mozilla Foundation and currently maintained by the IMS Global Learning Consortium [
18]. Despite an initial increasing adoption, however, critics have raised many objections as the lack of shared meaning of digital badges [
19], their role in the commodification of learning [
20], the theory of motivation displacement [
21], and other concerns about credibility, reliability and privacy [
22]. Attempting to overcome these issues, in particular the lack of content standardization, systematic and shared approach, credibility, reliability, and privacy that are key properties for issuing formal certifications, in the last years, researchers started to integrate digital badges with blockchain Refs. [
23,
24,
25]. Nevertheless, agile, usable and reusable applications still are in their infancy. No general software frameworks are available for institutional organizations.
Regarding machine learning, in recent years there has been a clear progressive trend toward applying it to foster personalized learning and precision education [
26]. The newest map** study of using machine learning approaches for precision education [
27] showed us that the major part of modern research focuses on defining new artificial intelligence methods (i) to make predictions of the student performance, (ii) to profile the student’s behavior and to cluster learning styles, and (iii) to provide recommendations to students of appropriate learning contents for their profiles. Particularly important for our research purposes is the study of recommendation algorithms for e-Learning to supply personalized learning objects to students that want to earn digital badges and certifications. In literature, we found four types of recommendation systems known as (i) Content-Based, (ii) Collaborative Filtering, (iii) Knowledge-Based, and (iv) Hybrid Systems [
28]. The hybrid strategies are those that can deliver better recommendations especially if coupled with appropriate machine learning and deep learning methods for data filtering [
29].
The research we briefly cited before, provided us with a strong scientific ground, and, at the same time, showed us the need to invest more effort on delivering open and flexible online environments to help students in finding and passing courses with final formal certifications of learning. In other words, the research gap we intended to fill, is to provide an intelligent framework that innovates the way students can obtain and share formal certifications of learning they earn during studies, enhancing the student’s experience and preserving, at the same time, security, transparency and openness to link administrative processes. Trying to give our contribution to improve the state of the art in a research domain we already faced in the past [
30,
31], we evolved an existing commercial e-Learning platform named Digital Brick (learning.digitalbrick.it, in Italian), coming to a novel one specifically designed to enhance the students’ experience in obtaining formal certifications of their competences. We put our conceptual and experimental effort following two orthogonal directions: on the one side, we exploited digital badges and blockchain as first-order entities that enable organizations to share recognized achievements among earners and issuers. On the other side, we defined new machine learning algorithms to provide students with personalized recommendations of online learning content, online courses and learning paths that are more suitable for their profiles and learning styles. In other words, we worked around two research questions associated to the research gap:
- RQ1.
It is possible to connect open badges, social networks and blockchain to share formal certifications of learning, having a minimum impact both on the student’s digital habitat and on the administrative information systems of educational institutions?
- RQ2.
It is possible to define new recommendation models to give students the personalized guidance they need on online learning materials, suggesting them learning paths useful to earn formal certifications?
We started our work by conducting a deep study of the state of the art addressing two related topics: (i) the use of blockchain technology and open badges for education credit transfer, and (ii) existing machine learning models for personalized education to guide students in the learning material more useful to study for earning credits and related certifications. To obtain a reusable approach, we designed a novel system architecture around a SCORM compliant learning management system. We added a specific module to provide services for the definition and issuing of formal certifications using digital badges according to the IMS Open Badges standard. We introduced a separate module exploiting blockchain technology to make the sharing of badges among actors more secure, transparent and open. Finally, we designed a complete recommendation algorithm based on a pipeline of different machine learning techniques (clustering, singular value decomposition, reinforced learning) to give advice to students about study materials and learning paths they have to follow to prepare themselves to obtain certifications.
The rest of the paper is organized as follows:
Section 2 provides the reader with all the details of how we conducted our study, in particular the design of the system architecture focusing on Certification and Recommendation modules, and the machine learning algorithms we defined.
Section 3 reports on quantitative results of the Digital Brick usage through a laboratory case study that involves all the components of the architecture.
Section 4 analyzes the results, discusses their implications, and presents research limitations. Finally,
Section 5 summarizes our key messages, sketching future research directions too.
5. Conclusions
As educational organizations plan to keep active online teaching and learning platforms even after the recovery of presence activities because of the COVID-19 pandemic, is crucial to enhance the students’ experience in obtaining credits and formal certifications of their competencies.
In this paper, we presented the Digital Brick platform built around a standard SCORM-compliant learning management system that provides all actors involved in the learning field with an open certification system exploiting blockchain and open badge technologies. In this way, the sharing of certifications among actors becomes more secure, transparent and open, and at the same time students experience automatic guidance tools through learning materials and learning paths; this first part of the research work we designed in
Section 2.2, we validated in
Section 3.2, and we discussed in
Section 4.1, is our explicit answer to RQ1; moreover, in this paper we presented a novel machine learning enabled recommendation system; this second part of the research work we designed in
Section 2.3, we validated in
Section 3.3, and we discussed in
Section 4.2, is our explicit answer to RQ2. To the best of our knowledge, this is the first time an Ethereum blockchain and a reinforcement learning scheme are applied to education evolving the process of certification of competencies. Our quantitative results showed that for the CS we achieved better transaction latency and transaction throughput than the Ethereum single node blockchain as reported in the state of the art. For the RS, as well, we obtained better prediction accuracy in measuring MAE and RMSE indexes. These results fill the research gap we identified and explicitly expressed in the introduction section of this article.
We know that educational institutions may have some resistance and caution to introducing blockchain technology in their information systems, given the fluctuation of cryptocurrencies observed in recent years; however, in this work, we proposed the use of blockchain as a distributed ledger to share credits certifications and open badges. From a market point of view, the Gartner group expects that the business generated by blockchain applications will exceed USD 10 billion in 2022 [
43], pulled by the needs of the Internet of Things and smart contracts (like certifications) in market segments as logistics, energy, food, and agriculture; this should reassure educational institutions, which can find in the market a big number of blockchain operators, experts and cloud platforms. At the same time, it could be for the educational institution the opportunity to provide their information systems with a modern payment interface able to accept cryptocurrencies; moreover, a recent bibliometric analysis of blockchain technology research [
44] reveals that blockchain fosters sustainability, i.e., it contributes to join the SDGs.
As a future step, we are organizing in vivo experiments engaging classrooms of Italian secondary schools and ITSs (Higher Technical Institute), offering them learning contents and certifications for ICDL digital skills on the topics of Industry 4.0. The involvement in the research group of Webscience Srl company, technical partner of AICA for ICDL (
www.icdl.it), gives us the possibility to engage more than 2000 students during the experimental phase of the Digital Brick project planned for September 2022.