Although the COVID-19 pandemic has provided a reason for remote lab sessions, research has also made the sessions possible remotely. Consequently, during the pandemic, remote teaching has been the prevalent educational practice in the case of laboratory courses [
1,
2,
3]. Traditional laboratory courses aim at helping students develop practical skills. These skills are mastered through well-designed experiments. In the case of remote labs, remote experiments are used for the same end.
A specific study accentuates the role of remote experiments, underlining that experiments constitute an important part of remote labs. It is also essential to point out that the same study focuses on an important aspect of remote experiments, clarifying that remote experiments remove barriers for students with special needs. In contrast to traditional experiments, which are only performed once, remote experiments can be repeated at any time and from any place [
4]. From a pedagogical perspective, experiments in traditional labs contribute to students’ conceptual learning and help students develop analytical and experimental skills. Most experiments in traditional labs call for student collaboration. In this sense, experiments in traditional labs could help students develop collaborative skills [
5]. Remote experiments should also achieve the previously mentioned goals. Another study emphasizes experiments, underlining that they are part of an active learner-centered approach, implementing inquiry-based learning with a focus on critical thinking and problem-solving [
6]. Remote experiments should also answer the same purpose. Since remote education is rising, remote experiments are integrated into the new educational strategy. Therefore, the design of remote experiments should be well considered in the case of remote labs. In this sense, modern remote labs are designed upon ”digital twins” capabilities. The authors of “Environment and Planning B: Urban Analytics and City Science” clarify that the term “digital twin” refers to an absolute mirroring of a physical process. In that aspect, the digital twin executes the respective operation in the same way the physical process is executed. The use of digital twins has been expanded to serve the purpose of entire systems. A system that mirrors another system’s operation could be deemed to be an abstraction of the critical features of the existing system [
7]. From this point of view, the operation of experiments could be mirrored by their respective digital twins. Therefore, digital twins ensure the success of remote labs.
The introductory section is divided into seven sections providing the theory for remote lab features (
Section 1.1), describing the equipment (
Section 1.2 and
Section 1.2), predicting students at risk (
Section 1.4), presenting the challenges for Education for Sustainable Development (
Section 1.5), designing remote labs according to ESG (
Section 1.6), and our research aims (
Section 1.7).
1.1. Remote Labs’ Features
Remote labs could be deemed experiments that are carried out and controlled through the Internet [
8]. Remote labs aid educational institutions in covering the need for space, instrumentation, and human support [
6]. Remote labs could be used by many students [
9]. Some issues that should be considered to make labs remotely accessible are centered on the following processes: hardware selection and installation, data digitization and collection, visualization, and network selection and installation [
10].
Remote labs are mainly based on a client—server architecture to deal with the issue of complexity. Two main parts stand out in this architecture. The first part is the Remote Lab Client Side, a web-based application that interacts with the server. The other part is the Remote Lab Server Side, which is usually built in the philosophy of the LabView or Matlab simulation [
11].
Several benefits related to remote labs are:
The challenges pertain to the need for error management, re-usability, and the need for extra security due to liable malicious web pages [
13,
14,
16].
Finally, the main drawbacks of remote labs are [
15]:
Physical presence is needed to foster collaboration between students and teachers and also among students.
There is a need for IT administrators to solve possible technical problems and support students during the experimental process.
Some practical skills and hands-on dexterities cannot be easily developed through simulations and online remote experiments.
1.2. NI-ELVIS Remote Labs
NI-ELVIS remote labs offer [
17]:
Multimedia Web-based operation
Interactive labs that contribute to theory and emphasize on projects.
Real-time Experiments that are performed by using real hardware.
Hardware-sharing capability at a course level.
Programming potential (Python and C).
In terms of the pedagogical benefits, NI-ELVIS remote labs achieve the following goals:
Help students to develop innovative qualities through real experiments.
Aid students to assimilate knowledge by appropriate resources
Stir students’ engagement by offering a web-driven environment, augmenting students’ desire to get involved in the learning process.
A cardinal function of NI-ELVIS remote labs is the real-time measurement outcome. The NI-ELVIS measurement station is illustrated in
Figure 1:
This function is materialized by a potent measurement station, the units of which are depicted in
Figure 2.
Another function of NI-ELVIS remote labs is based on an online teaching environment that provides students with theoretical material on the syllabus and tests students’ comprehension through appropriate exercises. This function is like the one implemented by e-learning systems. The theoretical material includes slides, videos, and other multimedia resources, whereas the exercises include multiple-choice questions, true/false questions, matching elements questions, and specific experiments.
It is essential to highlight that these exercises are graded, and students will be notified about their grades. It is also essential to note that educators can modify the context mounted on the respective e-learning system (LMS) to provide students with courses tailored to their needs. It is vital to stress the valuable statistical data that the e-learning system offers regarding students’ engagement. As a potent LMS, the NI-ELVIS e-learning system provides educators with meaningful data related to students’ activities (activity/assignment grades, logins into the system, and activity completion time). Therefore, educators can obtain an overall picture of students’ performance.
1.3. Metrics for Assessing Remote Labs
A study mentioned before highlights that remote labs should be widely available and widely accessible, and they should offer a safer experimentation environment in comparison to traditional labs [
12]. Another study has scrutinized over 100 articles on remote labs. It explains that some factors affecting the remote lab’s remote monitoring process are the extent of difficulty, limitations in the number of users, reliability, and security [
13].
ELVIS LabVIEW applications in automation should offer flexibility in design and be equipped with slight modification code capabilities and advanced accessibility potential [
18].
A critical study puts another issue on the table, pointing out that the success of virtual and remote laboratories is encircled by appropriate software selection [
19]. The same study lists a set of criteria that should be met to select the appropriate LabVIEW application software. These criteria are affiliated with LabVIEW application capabilities in every functional territory, including modularity; compatibility of code and hardware; debugging potential; executability; performance; and intuition.
Some studies contribute to getting perspective on the pedagogical issues related to remote labs [
18,
20,
21]. In detail, ELVIS LabVIEW applications aid students in practicing their communication skills and familiarize them with the learning process at home, enabling them to take in the rudimentary knowledge of experiments through a mixture of conventional teaching and self-practice [
18].
One study proved that remote and virtual experimentation practices increase students’ comprehension of concepts [
20]. Another study indicates that another pedagogical aspect affecting the effectiveness of remote labs is related to the opportunity to learn through a failed learning process, insinuating that students could make mistakes and learn from them [
21].
Another critical study indicates that factors that should be considered in a remote lab assessment process are usability, instruction comprehension, total time allotted to exercises and experiments’ completion, and entire procedure reliability [
22]. In parallel, the same study stresses students’ overall satisfaction as a cardinal metric reflecting a remote lab’s effectiveness.
Finally, some other studies shed light on another aspect that should be considered in the assessment process. They point out that only one student can use the remote experiment at a given moment, and therefore it is necessary to ensure an efficient reservation system. Students can thus choose the most suitable time for them and work at their own pace or return to the experiment at home if they do not understand something [
23,
24].
1.4. Predicting Students at Risk in Remote Labs
Many studies indicate e-learning platforms’ vital role in predicting at-risk students [
25,
26,
27,
28,
29,
30,
31]. Previous studies have also indicated that a proper analysis of students’ behavioral engagement could lead to developing competent risk models and generating respective prediction models. Statistical and machine learning techniques have been employed to answer the individual purpose of these studies. For instance, some studies refer to risk models developed by employing binary logistic regression analysis [
25,
26,
27,
28,
31], whereas a specific study refers to a risk model developed by employing other classification techniques. In parallel, one specific study underlines the contribution of the discriminant function analysis to the prediction model generation [
26].
In the case of NI-ELVIS remote labs, one study has proved that the student’s interaction with the NI-ELVIS LMS strongly predicts students’ critical performance. The same study has pointed out that the activities’ completion concerning the theoretical material and the theoretical exercises significantly affect students’ outcomes [
31]. As the studies mentioned above underline, no specific set of factors affects students’ critical performance in any course. Risk factors are course oriented. In this sense, risk models, along with prediction models, are also course oriented. Therefore, although binary logistic regression analysis and discriminant function analysis are deemed to be competent statistical methods that could be used to develop robust risk models and competent prediction models, it is essential to stress the fact that they cannot be used to develop models which are suitable for any risk occurrence. For instance, risk and prediction models developed in terms of a specific course are only valid for the specific course, given that we cannot rule out the possibility of emerging risk factors in subsequent course runs. In this sense, the prediction model generated using the respective methods should be validated in terms of many courses to develop a warning system for students at risk. Consequently, any risk models or prediction models developed in terms of a specific remote lab course do not necessarily work well when coming into effect in other remote lab courses.
1.5. Education for Sustainable Development
The education sector’s reaction to UNESCO to the severe and pressing problems the planet is facing is called Education for Sustainable Development (ESD). UNESCO’s ESD for 2030 education program aims to promote the societal and individual transformation required to reverse direction due to catastrophic human activities. On the UNESCO website, with the most recent initiative being in 2015 with the establishment of the 2030 agenda and the Sustainable Development Goals (SDGs), it is underlined that it will take a comprehensive approach to address environmental, social, and economic challenges if we are to stop global warming before it reaches catastrophic proportions. Teaching and learning about crucial sustainable development topics, such as climate change, disaster risk reduction, biodiversity, poverty alleviation, and sustainable consumerism, are known as education for sustainable development [
32]. However, although the integration of SDGs in universities is becoming apparent, the university’s role still needs to be more crucial in achieving the SDGs. The “5 Ps”, or five pillars of sustainable development, stand for people, planet, prosperity, peace, and partnerships. Sustainable development is development that is based on these five dimensions. One study also states that the 2012 UNESCO report entitled “The Future We Want” further suggests that the following themes could be included to secure renewed political and educational commitment to sustainable development [
33]:
The three pillars of sustainability are social, environmental, and economic; in addition, education could play an essential role in providing solutions to the issues above. Educational sustainability is fundamental because it provides students with an understanding of why the environment is essential and with real-world skills they can use to improve the planet [
35]. To act for sustainability now and in the future in one’s own life, community, and global scale, one must first instill the values and incentives necessary to do so in students, schools, and communities. This is what sustainable education aims to do. The 21st Conference of the Parties to the United Nations Framework Convention on Climate Change (COP21) deliberations in Paris in December 2015 stressed the importance of informed sustainability globally [
36,
37,
38]. One of the critical objectives and purposes of the United Nations Sustainable Development Goals for 2030 is education for sustainable development and sustainable lifestyles. Higher education institutions play a significant part in this effort [
39]. The same study highlights that, globally and locally, higher education may support sustainability in various social, technological, and environmental ways. Due to their large population of students, interdisciplinary teaching and learning methods, use of technology, and flexibility in the learning process, distance-learning universities are essential components of education for sustainable development.
The study mentioned before refers to interviews of Vice Chancellors or Rectors of four selected distance learning universities (OU, UAb, UNED, and FernU) about the sustainability of the DL model and the likely impact of current disruptions in the future, revealing that higher education distance learning is not viewed as a significant leadership organization in fostering a sustainable world. However, the requirement to meet the ambitions of the student body in a continuously changing world and the fight to master technological hurdles will undoubtedly cause questions of global sustainability to rise in the remote learning agenda. In general, sustainability is still primarily limited to the narrow sustainability of each institution’s life cycle of educational delivery. On the other hand, laboratory exercises are a crucial addition to many subjects of the curriculum. Virtual and remote laboratories (VRL) have become highly popular in assisting the learning process, both at formal and informal training, thanks to advancements in Information and Communication Technologies (ICT). Other studies report that there have been numerous attempts to create a remote and virtual laboratory, and most of them have been effective in doing so. However, most of these need to catch up in maintaining the laboratory after its first development period and making it viable. They conclude that sustainability and expendability may place more demands on user abilities and requirements [
40,
41,
42,
43]. Crucial components in extending the system are the use of ICT by teachers and students and the content offered by a VRL. VRLs will be popular in the future and will predominate in laboratory experiments in formal education from secondary schools to postgraduate degree programs since the new generations are accustomed to using ICT from very early on.
A couple of studies attempt a systematic review of the literature on recent studies on teacher educators’ perspectives and attitudes concerning ESD and their use, discovering that one could not assume that teacher educators thoroughly understand ESD. Possibly, those who do not concentrate on sustainability studies only have a broad grasp. To promote ESD, implementation in courses is crucial but needs to be improved. To encourage the adoption of ESD, further training is required, preferably subject-specific training, as well as institutional support (such as a goal statement for teaching) [
43,
44].
The findings of specific research suggest that higher education confronts a need to establish sustainability concerning distance education. University leadership regarding infrastructure and faculty support mechanisms were identified as crucial success factors. For continued success, professors should constantly question accepted teaching theories and modify their pedagogies to incorporate new technologies [
44]. Another research study tried to analyze how digital pedagogical models of three kinds (collaborative, social, and independent) influence the learning experience. The results showed that social and collaborative models foster effective learning and are crucial for sustainable education. They improve relationships between students, a sense of being a part of a group with similar interests, and feelings of togetherness.
In contrast to a more interactive model, an autonomous model may hinder students’ perceptions of their present knowledge and the development of collaboration skills [
44]. Another study investigated ways to advance in developing competencies aimed at Sustainable Development Goals (SDGs) in business administration education. Their study is based on the proposal presented by UNESCO (2017), “Education Goals for Sustainable Development: Learning Goals” and structured eight competencies and the specific learning goals for each SDG (see
Table 1) [
32]. According to the same study, the document created by UNESCO (2017) does not advance in operationalizing, developing, or evaluating competencies or goals, despite being based on prior acknowledged studies [
45,
46]. Despite the abundance of studies on the topic, there still needs to be proof of how these skills are acquired in students, how they might help the 2030 Agenda, or how they relate to the SDGs [
40,
45]. With rare exceptions, most of the suggestions are generic and aim to define the levels of competencies for undergraduate and graduate programs [
45]. It has already been challenged and highlighted as a barrier to further progress in the sector that identifying and describing competencies for sustainable development are so straightforward [
45].
According to UNESCO (2017) [
32], the skills mentioned earlier are necessary to help people solve current socio-environmental issues and provide them with a deeper understanding of the 2030 Agenda for Sustainable Development. Although the timeframe for attaining the 2030 Agenda targets is less than ten years away, it is underlined that there are obstacles to the insertion, development, and evaluation of these capabilities [
40,
45]. It is also underlined that there need to be more assessment tools on how initiatives affect people’s education. Another study focuses on the resistance of many educational institutions to developing effective strategies for sustainable development [
45]. Some studies clarify that the way various pedagogical approaches can be used to support the articulation of competencies for sustainable development needs to be addressed [
45].
1.7. Our Research Objective
Some studies refer to remote labs design based on sustainability principles [
47,
48,
49,
50]. However, remote lab effectiveness has yet to be associated with student performance. Besides, none of the studies we located in the relevant literature address students at risk in remote labs. Hence, there is space for scientific output in this field. This paper focuses on an NI-ELVIS remote lab designed upon sustainability principles to help students develop ESD competencies, such as critical thinking, problem-solving, and collaborative skills. The paper also demonstrates a prediction model based on students’ engagement that could be used to identify non-achievers (students who are about to fall through) in remote labs to ensure their sustainability.
Given that students’ engagement appears to affect students’ performance, our first research question is [
19,
20,
21,
22,
23,
24,
25]:
Which students’ engagement data critically affected their performance in our remote lab?
In parallel, given that our remote lab was designed upon ESD principles, the second research question is:
Were the risk factors associated with ESD competencies?
Therefore, our research is directed toward identifying factors that critically affected students’ performance in our remote lab and examining whether these factors are associated with ESD competencies. In parallel, our research attempts to contribute to the control of the risk of students’ failure in remote labs by presenting a way to generate a prediction model for students at risk.