Personalised Learning in Vocational Higher Education: A Model for Diverse Learners

doi:10.21203/rs.3.rs-3404282/v1

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Personalised Learning in Vocational Higher Education: A Model for Diverse Learners

https://doi.org/10.21203/rs.3.rs-3404282/v1

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Implementing blended learning in vocational higher education (VHE) is more effective considering the characteristics of the VHE curriculum, which is predominantly focused on practical learning and emphasises mastery of specific competencies. Due to the diversity of students in VHE, a 'one size fits all' blended learning design can be developed to be more adaptive and personalised. However, personalised blended learning (p-BL) design still needs to be improved, especially in VHE in Indonesia. Therefore, this paper offers a p-BL model that can be applied in VHE. Through an exploratory sequential mixed methods research design involving a literature review, seven interviews and a survey of 53 VHE lecturers and instructors, we identified the student model, knowledge model and personalisation model as the components of the p-BL model. The student model categorises students based on their knowledge levels and learning styles. The knowledge model annotates learning objects based on the level of difficulty and learning style dimensions. The personalisation model comprises learning objects and assessment recommendations; it also has a suggestion system that provides recommendations for assessment items for students who have failed, and it is recursive until the student passes the assessment. This suggestion system ensures that students master the competencies according to the characteristics of VHE learning in Indonesia. The personalisation process is integrated into blended learning using a station rotation and flexible model. The proposed p-BL model can be a reference for VHE institutions implementing personalised learning in their teaching and learning practices.

personalised learning

personalised blended learning

vocational

model blended learning

Vocational higher education (VHE) is higher education that prepares students for jobs with specific applied skills. It plays a strategic role in addressing the needs of the workforce and educating them to become skilled and professional workers ready to enter the job market (Haolader et al., 2017; Jabarullah & Hussain, 2019; Rintala & Nokelainen, 2020; Stapa & Mohammad, 2019). Vocational higher education is part of the higher education system implemented in many countries, including developing countries such as Indonesia, India and Korea and developed countries such as Germany, the USA, Australia and the Netherlands (Gelişli & Beisenbayeva, 2016).

In various countries in the Association of Southeast Asian Nations, as well as other nations, VHE is considered a strategy to enhance the country's economy by developing a skilled workforce (Reeve, 2016 in Stapa & Mohammad, 2019). The Indonesian government places significant attention on human resource development through higher education and vocational training in the anticipated demographic bonus era starting in 2030 (Indonesian Ministry of Economic Affairs, 2022). The government also emphasises that vocational education contributes to the mastery of specific applied skills, thus addressing the challenges of the current era and meeting the industry's demand for readily employable human resources. In Finland, VHE is expected to promote economic growth and social inclusion (Rintala & Nokelainen, 2020). In China, VHE is regarded as a part of modern education aimed at training 'technical, applied talents' (Shuo & Lei, 2019, p.83). In Malaysia, the government has prioritised skill-based development in human resource development in line with the country's vision of becoming an advanced nation (Stapa & Mohammad, 2019). One development is strengthening vocational education. Bangladesh, which had an aspiration to become a middle-income nation by 2021, recognised vocational education as a crucial tool for economic development (Haolader et al., 2017).

Compared with general or academic education, vocational education in Indonesia has several distinct features that differentiate it from academic education (Dewi et al., 2019; Indonesian Ministry of Research, Technology and Higher Education, 2016; Masrifah & Sudira, 2020), including (1) a graduate profile oriented towards the workforce and professions; (2) a focus on practical knowledge supported by relevant theory and emphasising skills more aligned with the needs of the job market; (3) a curriculum developed based on the occupational requirements of the workforce and designed in collaboration with businesses and industries; and (4) a sequential curriculum structure based on prerequisite subject and the order of competency and skill achievement. These features lead to a VHE curriculum having a more dominant composition of practical learning than theory. In contrast, the composition in academic education leans more towards theoretical rather than practical learning. These characteristics of VHE emphasise mastery of specific competencies, aiming to produce job-ready graduates for the workplace or industry.

The rapid progress of information and communication technology (ICT) has led to an increase in the use of e-Learning. With the outbreak of the COVID-19 pandemic in 2020, e-Learning became necessary for education when face-to-face learning was not feasible. The pandemic has catalysed the widespread adoption of e-Learning in education, including vocational education. The suitability of e-Learning for theoretical learning (Majumdar & Araiztegui, 2020; Margo et al., 2020; Yeap et al., 2021) poses a challenge for VHE, where practical learning plays a more dominant role than theory. However, some portions of practical learning can be conducted online. In several vocational programmes, most practical portions can be provided online, such as business, entrepreneurship, accounting, information technology (IT), communication, administration and management programmes (Commonwealth of Learning, 2020). Practical components that cannot be conducted online still require offline or face-to-face learning. Therefore, in vocational education, a blended learning approach can be applied and is considered to be more accommodating to its learning needs (Krismadinata et al., 2020; Nurhayanti, 2021).

Blended learning is an instructional design that optimises learning by combining various elements such as objectives, content, time and space, methods and learning media to maximise learning outcomes (Im, 2021). In various studies, blended learning is also defined as an instructional strategy that combines or ideally integrates face-to-face learning strategies with online learning strategies (using e-Learning media) to enhance learning effectiveness (Driscoll, 2002; Garrison & Kanuka, 2004; Wong et al., 2014).

One instructional model used in blended learning is the rotation model, widely employed in vocational education (Dewi et al., 2018; Krismadinata et al., 2020). Other blended learning models include flexible, self-blend and enriched virtual models. In the rotation model, students are divided into small groups, and each group is assigned a different instructional model during the learning process. For instance, in a single classroom, students may rotate between face-to-face instruction with a teacher, independent tasks and online learning through e-Learning media. The rotation schedule is arranged accordingly.

In his book, Bates (2022) states that one of the significant challenges faced in learning during the digital age is the diversity of students with different characteristics, levels of ability or prior knowledge. Therefore, lecturers and instructors must design learning experiences that accommodate this student diversity. Concerning VHE and blended learning, the use of rotation models is considered to be able to address the issues of diversity and differences in students' abilities (Dewi et al., 2019; Walne, 2012). Although rotation models can be optimised to address this diversity, instructional design in e-Learning for blended learning still tends to use one model for all students. However, with the mentioned diversity of students, one size does not fit all today (Bates, 2022). Another article states that personalisation is the optimal design for blended learning (Commonwealth of Learning, 2020; Patrick et al., 2013). Personalised learning (PL) is also referred to as a key success factor in implementing blended learning models (Brooke, 2021).

Personalised learning is a form of education that tailors the learning process and instructional approach to meet each student's unique needs (Xie et al. 2019). This involves customising the learning experience to align with the student's strengths, weaknesses, skills and interests (Akyuz, 2020; Patrick et al., 2013). Implementing the PL concept in blended learning brings personalisation to instructional methods and e-Learning design based on the diverse needs of the students. Our previous research showed that PL research could be expanded by incorporating hands-on learning/practice, allowing personalisation to encompass both cognitive and psychomotor domains. Educational contexts emphasising hands-on learning/practice, such as vocational education, can serve as valuable research contexts (Fariani et al., 2022).

One characteristic of vocational education that can be considered for personalisation is the emphasis on acquiring specific skills and competencies. Therefore, the curriculum is designed to prepare students to master particular skills and pass classes in the current semester before taking courses in the following semester. With the diversity among students, it is challenging to design learning activities tailored to fit their abilities in acquiring competencies in each course. Due to this specificity, it is important to research the implementation of the PL concept in VHE that adapts blended learning, thus forming personalised blended learning (p-BL).

From the explanation above, the research questions (RQs) in this study are:

RQ1

What are the components of the p-BL model in VHE?

RQ2

How can a p-BL model be constructed in VHE using these components?

Based on the RQs, the objectives of this study can be formulated as follows: (1) to determine the components that form the p-BL model in VHE, and (2) to create a p-BL model for VHE.

The remaining part of this paper is organised as follows: Section 2 explores some of the literature related to this study topic. Section 3 discusses the methodology used in this study. Section 4 presents the general results of the study. In Section 5, we discuss the findings to answer the RQs that have been defined. Finally, in Section 6, we present our conclusion and future work.

2.1 Vocational Higher Education

Vocational higher education is a formal learning approach that prepares students with the knowledge and skills necessary for the workforce. Therefore, its graduate profile is oriented towards specific professions and the workforce (Jabarullah & Hussain, 2019). Unlike general education, which often emphasises theoretical aspects, VHE prioritises practical, hands-on learning experiences. This focus helps ensure that VHE graduates have the necessary skills to integrate into the workforce and meet industry demands.

Given that VHE is oriented towards the workforce and industry, its curriculum is specialised towards specific skills and competencies (Choy & Yeung, 2022). Therefore, developing the VHE curriculum should involve the participation of industry (Masrifah & Sudira, 2020; Yoto, 2016). In developed nations like Germany, Austria and Denmark, VHE has a solid connection to the labour market: academic and vocational tracks are segregated, education is standardised, and businesses actively contribute to curricula and governance (Rintala & Nokelainen, 2020). In Indonesia, the structure of the VHE curriculum is sequential, based on prerequisite subject matter and a progression of competence and skill acquisition (Indonesian Ministry of Research, Technology and Higher Education, 2016).

Government laws regulate the provision of vocational education. For example, a VHE law was enacted in South Korea in 2000, known as the 'Law and Regulations on the Establishment of a New Vocational Education and Training System' (Shuo & Lei, 2019). In China, the Law on Vocational Education has been in place since 1996. The focus of vocational education in China has gradually shifted from secondary vocational education to higher education. Considerable research has been conducted on vocational and technical education, with established master's programmes in this field (Shuo & Lei, 2019). In Malaysia, the provision of vocational education is included in the Malaysia Education Blueprint 2013–2025 (Yeap et al., 2021). In Indonesia, the definition and governance of VHE are regulated by Law No. 12 of 2012 on higher education. The law stipulates that VHE provides various levels, including Diploma-I, Diploma-II, Diploma-III, Bachelor of Applied Science, Master of Applied Science and Doctor of Applied Science. Institutions that provide VHE by government regulations include universities, institutes, colleges, polytechnics and academies (Indonesian Ministry of Research, Technology and Higher Education, 2016). In Bangladesh most vocational education programmes are provided at the Diploma level (Haolader et al., 2017).

In summary, VHE has distinct features that distinguish it from academic education: (1) graduate profiles oriented towards the workforce and professions; (2) an emphasis on practical knowledge supported by relevant theory and skills more focused on the needs of the workforce; (3) a curriculum developed in collaboration with businesses and industries, based on the occupations required in the workforce; and (4) a sequential curriculum structure, based on prerequisite subject matter and the order of competence achievement.

2.2 Blended Learning

Blended learning is more than a combination of online and offline learning environments. The definition and scope of blended learning continue to evolve to incorporate successful system designs and strategies to enhance the learning process. Blended learning design optimises learning by integrating various elements such as objectives, content, time and space, methods and learning media (Im, 2021). In several studies, blended learning is defined as an instructional strategy that combines or ideally integrates face-to-face and online learning strategies to enhance learning effectiveness (Driscoll, 2002; Garrison & Kanuka, 2004; Wong et al., 2014). Simply put, blended learning utilises various methods and media in the learning process. As explained in Subsection 2.1, one of the characteristics of vocational education is the emphasis on practical aspects, making practicum components more dominant than theory in its implementation. Therefore, applying a fully online learning concept is automatically challenging in vocational education, especially in practical learning components. Hence, blended learning methods are considered more appropriate (Krismadinata et al., 2020; Nurhayanti, 2021). The online learning portion in blended learning methods can also be used for practicum, as an introduction to a practicum can be conducted through e-Learning. In some vocational programmes, a significant part of the practical portion can be delivered online, such as business, entrepreneurship, accounting, IT, communication, administration and management programmes (Commonwealth of Learning, 2020).

There are several instructional models in blended learning that are commonly implemented in education (Staker & Horn, 2012), including:

1. Rotation model

The rotation model involves students learning through a variety of different learning activities. Students can be divided into small groups, and each group is assigned a different instructional model during the learning process. One group may learn with direct instruction from the teacher, another group may work on individual/group tasks, and a third group may engage in online learning using e-Learning platforms. These learning activities are carried out rotating within one classroom, with the rotation schedule between student groups arranged accordingly.

The rotation model can also be implemented through flipped classrooms. In a flipped classroom approach, students individually access online learning materials the teacher instructs outside class time. They then engage in and prepare the learning materials before attending face-to-face class sessions. In-class activities are used to assess individual student understanding.

One challenge of using the rotation model is the need for a robust e-Learning system to assist each student by providing appropriate online content (Dewi et al., 2019; Walne, 2012).

2. Flexible model

The flexible model emphasises individualised learning. Most instructional activities are conducted online, and student–teacher interactions occur face-to-face and in small groups based on the student's needs. This model is designed to allow students to learn at their own pace in receiving instructional materials while still having opportunities for face-to-face interactions.

3. Self-blended model

The self-blended model presents a scenario where students can choose several online learning topics to complement their in-person lessons. The teacher is an online instructor and not physically present in the classroom/campus. Most learning activities are online, but students can still attend face-to-face classes.

4. Enriched-virtual model

In this model, students divide their time between attending face-to-face classes and learning through online content delivery and instructions. This model differs from the flipped classroom because, in the enriched-virtual model, students rarely attend face-to-face sessions every day. It also differs from the self-blended model as it applies to the entire subject in a course.

In their implementation, these four models are adjusted based on the technology used, time and instructional design of the learning process. The rotation model is widely used in vocational education, especially polytechnic education (Krismadinata, 2020).

There are several key success factors in using blended learning as an instructional model (Brooke, 2021), including (1) personalised and adaptive learning supported by appropriate technology – personalisation is important because one of the core aspects of blended learning is personalised instruction tailored to the specific needs of each student; (2) detailed tracking of student progress, which allows teachers to understand student learning progress accurately and enables them to take appropriate teaching actions or plans; (3) Availability of comprehensive teaching resources; and (4) availability of comprehensive planning, especially for teachers, regarding instructional strategies related to personalisation.

Personalisation in Blended Learning

The systematic literature review on the application of PL in higher education shows that the general approach to implementing PL in education is by analysing student diversity and classifying them. Then, the personalised learning and teaching aspect is determined as a learning strategy given to students according to their classification (Fariani et al., 2022). Therefore, applying the PL concept in blended learning begins with analysing student diversity and then determining personalisation in instruction, teaching materials and blended learning design according to the needs and diversity of students.

3.1. Research Design

In general, this study focuses on two main aspects in line with the RQs: (1) determining the components of the p-BL model in VHE and (2) developing the p-BL model in VHE. Therefore, there are two main research designs: one for determining the components of the p-BL model and another for creating the p-BL model. The method employed in both research designs is the exploratory sequential mixed method design. In this design, qualitative data is first gathered and analysed, followed by the collection and analysis of quantitative data to generalise the initial qualitative results (Schoonenboom & Johnson, 2017).

The qualitative phase was conducted through a literature review to determine the components of the p-BL model. The literature review sources are drawn from various sources such as journals, books and relevant documents. In this qualitative phase, an investigation was also carried out on practicum learning and using e-Learning in practicum learning in VHE, which aimed to identify personalised strategies that could be recommended to students. The practicum learning investigation involved interviews with the heads of study programmes from several VHE programmes in Indonesia. The main questions that guided the study were: (1) what are the forms of practicum learning in your institution? (2) do you use e-Learning in your teaching? (3) how do you use e-Learning for practicum learning? The data obtained from the qualitative phase was then used to develop a questionnaire for the survey in the quantitative phase. The survey targeted lecturers/instructors in VHE who were involved in practicum learning.

The next step was to combine the data from the literature review and interviews obtained in the qualitative phase with the data from the survey in the quantitative phase. This combination was done to create the p-BL model. An overview of the entire research design can be seen in Fig. 1, and the details of each phase are explained in the subsequent subsections.

3.2. Qualitative Phase

Literature Review

The literature review was conducted to study the learning environment in VHE compared with general higher education. The concept of PL in education was also examined, and various sources such as journals, papers, articles, books and government documents related to VHE were utilised. The findings from this phase were used to explain how the concept of PL can be applied in blended learning to create p-BL. Furthermore, it helped determine which components of p-BL would serve as the basis for designing the proposed p-BL model.

Interview

The interviews were conducted to understand the implementation of practical teaching and how e-Learning is used in VHE. Purposive sampling was employed to select programme coordinators from several departments in VHE as expert sources on curriculum and instructional practices in VHE. In-depth interviews were conducted with these programme coordinators (n = 7) to better understand the nature and implementation of practical teaching in VHE. The author analysed the interview data and identified common types of practical teaching activities in VHE and the role of e-Learning in supporting practicum learning.

3.4. Quantitative Phase

A questionnaire was developed based on the data obtained from the interviews conducted in the qualitative phase. The questionnaire was purposefully and explicitly designed for this study. It comprised two main sections: closed-ended questions about respondents' personal information and data on practical teaching and e-Learning usage. Experts in education and e-Learning then validated the questionnaire.

Before the questionnaire was distributed, validity and reliability tests were conducted. Firstly, two lecturers from VHE with over 10 years of experience teaching practical courses were asked to review the questionnaire and provide feedback on its clarity and comprehensiveness (Moorhouse & Wong, 2022). Secondly, two other lecturers were asked to complete the survey. The author analysed their responses and compared them to the survey objectives. Some items needed revision, but overall, the survey achieved its goals. This process helped ensure the reliability and validity of the collected data (Holden, 2010, as cited in Moorhouse & Wong, 2022). Some questions from the questionnaire are presented in Table 1.

Table 1

Some of the questions in the questionnaire for this study
No.	Question
Characteristics
Q1	What is your programme and field of study?
Q2	Have you ever taught practicum?
Q3	How long have you been practicum learning?
Practicum Learning and e-Learning Use
Q1	Do you use e-Learning in practicum learning?
Q2	What percentage of you use e-Learning in practicum learning compared to conventional methods?
Q3	What forms of practicum do you conduct when teaching?
Q4	When do you use e-Learning in practicum learning?
Q5	For what purposes do you use e-Learning in practicum learning?

The questionnaire was distributed online to VHE lecturers and instructors as part of a survey. A purposive random sampling technique was used, targeting lecturers or instructors who conducted practical teaching in laboratories or workshops at VHE institutions. A total of 89 lecturers completed the questionnaire, but only 53 lecturers from 18 VHE institutions in Indonesia were involved in practical teaching. The distribution of VHE institutions was fairly representative as they were from the main islands of Indonesia, namely Sumatra, Java, Sulawesi and Kalimantan. Similarly, the distribution of fields of study in VHE covered 11 disciplines, including engineering, IT, business administration, accounting, animation, public relations, multimedia, education, convention and event management, mechatronics engineering and food technology.

3.5. Data Analysis

Data analysis in the qualitative phase was conducted using thematic analysis. Thematic analysis is a method for methodically describing and interpreting the meaning of qualitative data by giving codes to the data, condensing them into themes and then analysing and presenting these themes. Thus, the thematic analysis combines a structured approach with the subjective interpretation of the researcher (Wæraas, 2022). As shown in Fig. 1, the qualitative phase involved two activities: literature review and interviews.

The literature review activity used thematic analysis to determine the stages needed to develop a PL model in education. The first step involved coding the relevant journal, books and articles related to personalised learning in higher education using an inductive approach. This was a data-driven coding process, with codes being developed during the analysis. The interview activity used thematic analysis to analyse the interview result, with coding performed on the transcripts.

Next, themes were generated from the codes. Themes were identified by grouping codes with similar meanings, types or semantic content (Wæraas, 2022). From the literature review activity, the identified themes were used as components of the p-BL model. In the interview activity, the identified themes served as the basis for creating the questionnaire items and answer options in the quantitative phase.

The quantitative survey results complemented the data obtained from the qualitative process, and the qualitative and quantitative data were then integrated. Integration in a mixed methods design can occur at multiple levels: design, method or interpretation (Fetter et al., 2013, as cited in Berman, 2017). This study used design-level integration with a sequential design, where the results obtained from the first phase (qualitative) were used to build the survey in the second stage (quantitative). This study also used interpretation-level integration since data from both qualitative and quantitative have emerged and been synthesised. The integrated data were presented using descriptive statistics to examine the landscape of practical teaching and the use of e-Learning. The integrated data also served as the basis for developing the p-BL model. All authors collaborated in the analysis and synthesis process, with the initial analysis conducted by Author 1 and verified by Authors 2 and 3.

From the thematic analysis, three steps were identified in the use of PL in education: (1) analysis of student diversity/differences; (2) analysis of aspects of learning and teaching that can be personalised; and (3) personalisation. In step (1), students were classified based on their characteristics, while step (2) determined the learning strategies. Step (3) is the personalisation process, which provides learning strategies to students according to their classification.

Some statistics from the survey conducted through the questionnaire will also be described. The lecturers and instructors participating in this survey taught practicum sessions, with teaching experience ranging from less than 3 years to over 15 years. The distribution of teaching experience can be seen in Fig. 2. As many as 73% of lecturers/instructors have more than five years of experience teaching practicum, with the most frequency of teaching experience being more than 15 years (38%). This statistic indicated that the participants had considerable experience in teaching practicum.

There are 11 fields of practicum learning involved in this survey: engineering, ICT/computer engineering, business administration, accountancy, animation, public relations, multimedia, education, management of events, mechatronics engineering and food technology. These 11 fields of study were grouped into three categories: engineering, IT and social science. The distribution of these three categories is presented in Fig. 3.

4.1. Practicum learning

In Subsection 2, it has been mentioned that one characteristic of VHE is a larger proportion of practicum learning compared to theoretical education. The forms of practicum in VHE can be categorised into several categories, as shown in Fig. 4.

Figure 4 shows that the practicum forms, such as hands-on activities, simulations using specific software and project development, are the three most common practicums in VHE.

4.2. e-Learning Use in Practicum Learning

In this study, e-Learning has been widely used in practicum learning, with a usage percentage of 60%. Although the rate of those who have not yet adopted e-Learning is relatively high (40%), 90% of non-adopters plan to use e-Learning in future practicum learning, while only 10% will not use e-Learning. Figure 5 illustrates the distribution of e-Learning usage in practicum learning.

The percentage of e-Learning usage in practical teaching varies, and this study divides it into five intervals: < 20%, 21–40%, 41–60%, 61–80% and > 81%. The distribution of these usage percentages can be seen in Fig. 6.

The figure shows that although e-Learning is common in practicum learning, the usage percentage is still relatively low (82% of participants incorporate e-Learning into less than 60% of their teaching).

In the practicum, e-Learning can be used as a teaching aid in various aspects, including practical teaching preparation/introduction (28%), exercise (26%), providing formative/summative assessment (20%), practical teaching implementation (20%), internalisation (5%) and assignment (2%). If mapped based on the field of study, the diagram shows that ICT uses e-Learning more as a teaching aid in practicum learning than in the social sciences and engineering fields (Fig. 7).

Not all types of practicum learning in vocational education can utilise e-Learning as a teaching aid. Some types of practicum learning can only partially use e-Learning, while others can fully utilise it. Therefore, it is interesting to map the spectrum of e-Learning usage across different types of practicum learning (see Fig. 8). Figure 8 shows that the usage of e-Learning is still minimal in practicum learning types that involve workshops or hands-on activities using tools. On the other hand, practicum learning types such as projects, design, simulations and internships have a higher proportion of e-Learning usage.

Next, we will discuss how to develop e-Learning in practicum learning by adapting the concept of PL and integrating it into a blended learning design. This will be further explained in the discussion section.

The discussion of the results of the qualitative and quantitative phases shows that e-Learning is widely used and can be applied to practicum learning in VHE. Therefore, it is interesting to model how personalised learning is implemented in developing blended learning design in the context of VHE. In order to align with the objectives of this study, this discussion section was guided by the RQs.

5.1. Addressing RQ1: What are the components of the p-BL model in VHE?

As explained earlier, from the analysis of the literature review conducted, it was found that, in general, a PL model comprises three major stages: (1) analysis of student diversity/differences; (2) analysis of aspects of learning and teaching that can be personalised; and (3) personalisation. Stage (1) results in a student model, while stage (2) involves modelling the knowledge related to learning, and stage (3) involves modelling the personalisation strategies to be applied to students. Therefore, in p-BL as a model that utilises the concept of PL, there are three main components: the student, knowledge and personalisation models. The student model describes the classification of students to represent the diversity among students; the knowledge model describes the processing of knowledge, while the personalisation model describes the personalisation strategies provided to students according to their classification.

The first component of the p-BL model, the student model, is essential to understanding student diversity, which helps construct student classification. This classification forms the basis for subsequent personalisation. Our preliminary study (Fariani et al., 2022) found that knowledge level and learning styles are the most commonly used personalisation parameters. Therefore, this study uses these two parameters in modelling the student.

Learning style is defined as the cognitive, affective and psychological behavioural characteristics that indicate how students perceive, interact and respond to their learning environment (Keefe, 1979, as cited in Felder et al., 2005). Chen and Wang (2020) found that the Felder–Silverman learning style model (FSLSM) is widely used in analysing student differences. This is supported by another review of PL research, which states that the FSLSM is the most commonly used learning style theory (Jando et al., 2018). Another study mentioned that FSLSM learning styles are widely used in computer-based learning, including adaptive and personalised learning, and are relatively easy to implement (Nafea et al., 2019). Based on these reasons, the learning style parameter used for personalisation in this study is the learning style according to FSLSM.

The FSLSM categorises learning styles by identifying students’ learning preferences across four dimensions (Felder & Silverman, 1988), namely:

Input dimension: This dimension determines how students prefer information to be presented. In the input dimension, students are differentiated into visual and verbal learners. Visual learners prefer visual information such as pictures, diagrams or flowcharts, while verbal learners prefer written or spoken explanations.

Understanding dimension: This dimension determines how students comprehend and acquire information. Based on this dimension, students are categorised as sequential or global learners. Sequential learners prefer learning in a step-by-step and linear manner, starting from easier concepts to more difficult ones. On the other hand, global learners think holistically and grasp the big picture.

Processing dimension: This dimension distinguishes how students prefer to process information. Students are categorised as either active or reflective learners. Active learners prefer learning by doing, collaborating in groups and engaging in hands-on activities. Reflective learners, on the other hand, prefer learning through reflection and individual study.

Perception dimension: This dimension determines how students perceive and approach information. Students are differentiated as sensing or intuitive learners. Sensing learners prefer practical, concrete, fact-oriented and procedural learning. Intuitive learners, on the other hand, prefer conceptual understanding, innovation and theoretical exploration.

With FSLSM, a student will have a dominant preference in each dimension. Therefore, a student's learning style can be described as a combination of these four dimensions. For example, a student may have a visual, sequential, active and sensing learning style.

The second component of the p-BL model is the knowledge model, which serves as a strategy for processing knowledge. This processing knowledge is done by classifying learning objects (LOs) based on their difficulty levels and aligning them with the dimensions of FSLSM according to the personalisation parameters used in the student model.

The third component of the p-BL model is the personalisation model. This model represents the learning aspects that will serve as the basis for the personalisation strategies to be provided to students. In determining these personalisation strategies, it is important to consider the implementation of e-Learning in VHE. As detailed in Subsection 4.2, our survey found that the top three e-Learning uses in VHE's practical teaching are practical teaching preparation/introduction, exercises and formative/summative assessment. Therefore, exercise and formative/summative assessment were the basis for the proposed personalisation in the p-BL model. This proposed personalisation enables implementing an assessment item suggestion system for items students have not successfully completed, offering students guidance on areas needing review and repeating assessments until passing criteria are met. This ensures that students meet the passing standards for each assessment before moving to the next competency, aligning with the nature of the sequential curriculum structure in vocational education. This also aligns with the characteristics of VHE, as explained in the introduction, which emphasises competency mastery. The suggestion system also allows for personalised pacing, where students with a good level of assessment completion can complete learning more quickly.

In addition to assessment and suggestion system personalisation, this research incorporated the personalisation of LOs, including learning materials and media. Learning materials can be presentation slides, digital books, papers, journals and other instructional materials. Learning media can include text-based and multimedia elements such as videos or animations.

From the explanations above, the components of the proposed p-BL model in VHE in this study are as follows:

Student model: This data structure defines a student at a specific time, storing information on the student's characteristics and providing the ability to track the student's learning progress. The information stored in the student model includes (1) general information, (2) the student's knowledge level and (3) the student's learning style.

Knowledge model: This model represents the processing of knowledge to determine the personalised strategies provided to students based on their classifications. Knowledge processing involves classifying LOs based on difficulty levels and the FSLSM dimensions.

Personalisation model: The proposed personalisation strategies include personalised assessment, exercise, a suggestion system and LOs.

The following subsection will explain how these three components formed the p-BL model and address the second RQ.

5.2. Addressing RQ2: How can a p-BL model be constructed in VHE using these components?

The necessary components for creating the p-BL model were identified in the previous discussion: the student, knowledge and personalisation models. The next step is creating a framework representing the p-BL model built from these model components. The proposed p-BL model can be seen in Fig. 9 below.

Student Model

The student model defines the data structure of a student at a specific time and is used to determine the characteristics of the student. The data used as student characteristics are the student's knowledge level and learning style. This study divides students' knowledge levels into basic, intermediate and advanced. Meanwhile, the learning styles are classified according to the FSLSM.

A pre-course test assesses students' knowledge levels, and the Index of Learning Styles (ILS) questionnaire, developed by Felder and Silverman (2012), discerns learning styles across FSLSM's four dimensions. This study determined the two most dominant learning styles for each student.

For example, after taking the pre-test, a student may be categorised as having a basic level of knowledge. Then, the ILS questionnaire identifies the student's learning style as visual, sequential, active and sensing, with high scores in visual and sequential. Therefore, the student model for this student is a student with a basic level of knowledge and a visual and sequential learning style.

Knowledge Model

The knowledge model represents the LOs for each competency of a specific course into several criteria. These criteria align with the personalised parameters used: knowledge level and FSLSM learning style. Therefore, the LOs were annotated using difficulty levels and categorised into eight categories based on the four dimensions of FSLSM.

To represent the LOs, the instructor determines a hierarchy of competencies and LOs at the beginning of the course. Each competency has several LOs. Then, the competencies and LOs are annotated with difficulty levels using the categories specified in the Learning Object Metadata (LOM) standard (IEEE Computer Society, 2020). The LOM standard is a proposed data model by the Learning Technology Standards Committee of IEEE, which is widely used as a metadata standard to describe LOs (Apoki, 2021). The difficulty levels specified in the LOM standard are easy, medium and difficult. Therefore, the LOs were annotated into easy, medium and difficult categories based on their difficulty levels.

In a course, there are several competencies. These competencies were grouped according to their difficulty levels. Then, each competency was represented by the associated LOs (see Fig. 10 for illustration).

Learning objects are also annotated with FSLSM learning styles. According to the LOM standard, LOs can include practice exercises, simulations, questionnaires, diagrams, images, maps/graphs, slides, narrative texts, exams, experiments, self-assessments and videos. Then, a mapping of FSLSM to LOs, both in terms of LO types and instructional design, can be seen in Table 2.

Table 2

Mapping learning objects to the Felder–Silverman Learning Style Model dimensions
Felder–Silverman Learning Style Model Dimension	Learning Objects/ Instructional Design
Visual	Graphics and visualisations, videos, PDFs, exercises, assessments, quiz
Verbal	References, PDFs, PPTs, textual materials, exercise, assessment, quiz
Active	Exercise, assessment, quiz Instructional design: discussion link
Reflective	References, PDFs, PPTs, textual materials, review questions, exercises, assessments, quiz
Sensing	Practical materials, hands-on activities, exercise, assessment, quiz Instructional design: forum discussion link (sharing experience)
Intuitive	Abstract description, references, PDFs, PPTs, textual materials, exercise, assessment, quiz
Sequential	Solved exercise after the content, chapter summary and navigation, exercise, assessment, quiz
Global	Solved exercise before the content, outline and overviews, exercise, assessment, quiz

For example, in the ‘Algorithms & C Programming’ course, the knowledge model can be illustrated in Fig. 11. In this course, there are several competencies categorised into easy difficulty (green blocks), medium difficulty (yellow blocks) and high difficulty (light red blocks). Each competency is associated with LOs of various types according to the dimensions of FSLSM.

Personalisation Model

It has been explained in the previous section that this research uses two personalisation parameters: knowledge level and learning style. Therefore, there are two types of personalisation: (1) personalisation regarding what the students will learn based on their knowledge level; and (2) personalisation of how students learn according to their learning style.

The first personalisation recommends LOs according to the student's level of knowledge. Students are categorised into three knowledge levels: beginner, intermediate and advanced. Learning objects are categorised into three difficulty levels: easy, medium and difficult. Beginner-level students were recommended LOs with an easy difficulty level. Intermediate-level students were recommended LOs with a medium difficulty level, and advanced-level students were recommended difficult LOs. Students with higher levels of knowledge can access LOs below their level. In comparison, students with lower levels of knowledge can only access LOs above their level if they have passed the assessment of competencies at their level.

The second personalisation determines the LO and instructional design recommended to students according to their learning style, as indicated in Table 2. For example, students with an active-visual learning style were recommended LO types such as graphs, videos and discussion forums. Although the system recommended LO types based on the student's learning style preference, it also allowed students to access other types of LOs and instructional designs.

In each competency, an assessment was conducted. The results of the student's assessment triggered a suggestion system. This suggestion system is triggered by the assessment items that the student did not pass successfully. The student received guidance on what needs to be relearned for the failed assessment. Subsequently, the assessment was repeated until the student achieved the passing standard for that assessment. In this way, the system ensures that students meet the graduation criteria before proceeding to learn another competency, aligning with the nature of the vocational education curriculum structure, which is sequential and focused on fulfilling specific competencies.

After determining the student, knowledge and personalisation models, the next step is to create the p-BL model by integrating these three model components into blended learning.

Integration into Blended Learning

It has been explained that there are several models for implementing blended learning. This research uses a combination of the station rotation and flexible model. The station rotation model utilises the knowledge levels as stations. Therefore, the proposed model has three stations: basic, intermediate and advanced knowledge levels, as shown in Fig. 9. In each station, teaching can be conducted using the flipped classroom approach, where students individually access online learning materials instructed by the teacher outside of class time (asynchronous). Students prepare the learning materials in advance before attending face-to-face class sessions (synchronous).

The flexible model allows students to switch stations according to their learning pace. For example, a student who finishes modules at the basic knowledge level station before their peers can proceed to the intermediate knowledge level station without waiting for the rest of the group.

The suggestion system provides recommendations regarding the specific LOs students have not successfully passed. Therefore, students may return to a lower knowledge level station, although only for particular modules.

For example, consider the course ‘Algorithms & C Programming’, which comprises three competencies with varying difficulty levels, as shown in Table 3. Competency C1 is categorised as easy, competency C2 as medium and competency C3 as difficult. C1 has LOs with various types and media, such as PDFs (LOC1 pdf), exercises (LOC1 exercise), videos (LOC1 video), assessments (LOC1 assessment), review questions (LOC1 review question) and suggestions (LOC1 suggestion). Similarly, C2 and C3 have related LOs, as shown in Table 3. The LOCX suggestion (X = 1, 2, 3) represents the LOs related to the assessment items for each competency.

Table 3

Illustration of the mapping between competencies and LOs in a course
Competencies	Difficulty	Learning Object
C1: Translation algorithm into C programming	Easy	LOC1 pdf, LOC1 exercise, LOC1 video, LOC1 assessment, LOC1 review Question, LOC1 suggestion
C2: Apply control using selection in C programming	Medium	LOC2 pdf, LOC2 exercise, LOC2 video, LOC2 assessment, LOC1 review Question, LOC2 suggestion
C3: Apply procedure and functions	Difficult	LOC3 pdf, LOC3 exercise, LOC3 video, LOC3 assessment, LOC3 review Question, LOC3 suggestion

Figure 12 illustrates an example of three students, A, B and C, enrolled in ‘Algorithms & C Programming’ with different characteristics and learning experiences. Student A is categorised as having a basic knowledge level and visual and active learning styles. In contrast, student B has a basic knowledge level and verbal and active learning styles. Student C is categorised as having an intermediate knowledge level and visual and reflective learning styles. Students A and B are in the same basic-level station in the blended learning design. The recommended LOs for students A and B are related to competency C1, with LO types tailored to their respective learning styles. Student C is in the intermediate-level station. In this example, student B is illustrated as having a different learning pace than student A because they completed the assessment for competency C1 without needing to repeat it. As a result, they can directly move to the intermediate level station to learn competency C2. Student C completed the assessment for competency C2, allowing them to move into the advanced level station to learn competency C3. On the other hand, student A failed the assessment for competency C1, and the system will provide a suggestion, in this case, the LOC1 suggestion. Student A will learn the LOC1 suggestion and retake the LOC1 assessment.

The system continues to provide suggestions to student A as long as they still need to complete the LOC1 assessment successfully. Student A moves into the intermediate level after completing the LOC1 assessment. Therefore, by the end of the course, all students are ensured to fulfil all the competencies designed in the course.

This study proposes a p-BL model in VHE to model personalised face-to-face and online learning. This study conducted a survey to examine the use of e-Learning in practicum learning in VHE. Based on the survey, it was found that the use of e-Learning in practicum learning was moderately high, with a usage percentage of 60%. Although the rate of non-users of e-Learning was relatively high (40%), 90% planned to use e-Learning in future practicum learning. It can be concluded that e-Learning can be promoted for practicum learning. The survey also revealed that e-Learning was mainly used for introducing laboratory sessions, providing exercises and conducting assessments. Therefore, in this research, the provision of exercises and assessments forms the basis of personalisation in the proposed p-BL model.

This p-BL model utilises three fundamental components: (1) student model; (2) knowledge model; and (3) personalisation model. In the student model, the knowledge level and FSLSM learning style are used as personalisation parameters to obtain a description of the student with a specific knowledge level and learning style. In the knowledge model, LOs related to the knowledge domain are annotated. The annotation is based on the difficulty level and the FSLSM dimensions, resulting in LOs with specific difficulty levels and types corresponding to the FSLSM dimensions. In the personalisation model, personalisation is carried out by providing LOs according to the student's characteristics and a suggestion system. This suggestion system provides recommendations to students regarding LOs that need further exploration. The suggestion system is triggered by assessment items that the students have not successfully completed and operates recursively, ensuring mastery of competencies before moving on to the next ones. This aligns with the nature of the vocational education curriculum structure, which is sequential and focused on fulfilling specific competencies. This personalisation process is implemented in the blended learning model by adapting the station and flexible models, with the knowledge level serving as the station.

There were some limitations in this study. Firstly, although the sample in the survey on using e-Learning in practicum learning at VHE represents various fields of study in VHE, it is context-specific to VHE in Indonesia. Secondly, the p-BL model presented in Fig. 9 has yet to be tested, which leads to our future work. In our following research, we will test the model by building a prototyping system p-BL and implementing it in several practical laboratory courses at VHE. Experiments will be conducted with a control group and an experimental group to assess the impact of using the p-BL model on learning outcomes and learning experiences..

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing interests

The authors declare that they have no competing interests.

Acknowledgments

Authors sincerely thank to Lembaga Pengelolaan Dana Pendidikan (LPDP), Republic of Indonesia for supporting this research through the Indonesian Education Scholarship Program.

Authors' contributions

Rida Indah Fariani contributed to collecting, analysing the data and wrote the initial manuscript. Kasiyah Junus supervised the research by directing, analysing, and reviewing the manuscript. Harry Budi Santoso contributed to reviewing the methodology and the manuscript. Each of the named authors made a significant contribution to the underlying research. The final manuscript was read and approved by all authors.

Availability of data and material

The data and material in the study are available from the author upon request.

Ethical Approval and consent to participate

Informed consent was obtained from all individual participants included in the study

Consent for publication

Not applicable.

Conflict of Interest

The authors declare no conflict of interest.

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Personalised Learning in Vocational Higher Education: A Model for Diverse Learners

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Abstract

Figures

Introduction

Relevant Literature

2.1 Vocational Higher Education

2.2 Blended Learning

Personalisation in Blended Learning

Methodology

3.1. Research Design

3.2. Qualitative Phase

Literature Review

Interview

3.4. Quantitative Phase

3.5. Data Analysis

Results

4.1. Practicum learning

4.2. e-Learning Use in Practicum Learning

Discussion

5.1. Addressing RQ1: What are the components of the p-BL model in VHE?

5.2. Addressing RQ2: How can a p-BL model be constructed in VHE using these components?

Student Model

Knowledge Model

Personalisation Model

Integration into Blended Learning

Conclusions, Limitations and Future Work

Declarations

References

Additional Declarations

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