The era of the Industrial revolution 4.0 is disrupting various sectors to respond and adapt to the various changes and demands (Rhenald, 2017). Coupled with the plague of the COVID-19 pandemic, which has further accelerated digitization in various aspects of life (Iivari et al., 2020)(Awaludin et al., 2023), including in the field of Education. The education curriculum is expected to adapt flexibly in line with the disruption of digital technology needs. This rapid change is ultimately the background for forming the independent learning program. It encourages every university to organize Education more autonomously and flexibly according to the needs of students (Fuadi et al., 2021).

The world of Education should implement the need to understand the digital system in this era, including higher Education (U.N.E.S.C.O. et al., 2018), in a comprehensive-adaptive curriculum (Dutt et al., 2020). The curriculum must contain literacy aspects needed in thedigital era 4.0, namely data literacy, technological literacy, and human literacy (U.N.E.S.C.O. et al., 2018). The implementation of curriculum development is intended to guide the achievement of the profile of higher education graduates with future-oriented competencies and ensure suitability for present and future challenges (Falloon, 2020).

Digital competence is the latest concept that describes digital technology skills (Cabero-Almenara et al., 2020), such as skills in utilizing information technology, information and digital literacy, and 21st-century skills (Ghomi & Redecker, 2019). According to the European Union Commission, this digital competence is essential and is recognized as one of the eight main competencies for life and activities (Kuzminska et al., 2018). Digital competence is more comprehensive than just digital skills. Digital competence includes cognitive abilities related to knowledge and Education (content knowledge) as well as technical aspects related to the ability to manage hardware and software (Voogt et al., 2013). In the light of post Covid-19 pandemic, the need for digital competence in the world of Education has become increasingly urgent. The world of Education significantly impacts the need for competent teaching staff in digital skills and the community's need for teachers who understand the character of the digital native generation (alpha generation) in the 21st century (Fadlurrohim et al., 2019). Therefore, simultaneous steps are needed to explore, observe, map, and take concrete steps in preparing digitally competent preservice teachers. This study is a preliminary step (pilot study) to observing and mapping the digital competencies of student-teacher candidates in Indonesia before developing a framework and curriculum to implement digital competency learning.

Educational technology is developing very rapidly. The competence and quality of teachers in the 21st century and the technology used during the Covid-19 pandemic affect human behavior and affect how to teach, adapt, and get student engagement (Awaludin et al., 2023). Educating more qualified or digitally competent teachers is very necessary.

The requirements for digitally proficient instructors have grown along with the growing digitalization of society, requiring new approaches or methods in terms of technology integration in Education, especially during this pandemic. Teacher education is considered the place to start this integration of nature and what teachers need. However, recent studies show an apparent gap between the digital needs of qualified teachers and their qualifications (Amhag et al., 2019). The research shows that teachers need access to many things, such as equipment access, a job, and a good attitude toward technology, to achieve technology integration and have digital proficiency (Kay, 2006).

Today, information and communication technology come together and impact our daily lives. Competence in information and communication technologies or digital skills is an important prerequisite for teachers to engage in active social engagement (Gudmundsdottir & Hatlevik, 2018). The development of technology and more excellent ICT resources, especially during the Covid-19 pandemic, has also influenced and will continue to change the traditional ways of learning and working. The government has implemented many policy measures over the previous decade according to the times to address the problems resulting from one transition to the next. For example, steps have been put in place to make ICT materials and resources accessible, to help instructors integrate ICT into their schools, and to promote that these digital competencies are essential as part of a later national school curriculum (Tondeur et al., 2017). Table 1 elaborate the general description of DigCom areas.

Research related to educating teachers to be digitally competent has been conducted by Elen J. (Instefjord & Munthe, 2017). The research's primary goal is to integrate digital professional knowledge into early or basic teacher education programs (Instefjord & Munthe, 2017). A survey was conducted, and data from three national surveys were analyzed: teacher teachers, mentor teachers, and preservice teachers in Norway. This study reveals a weak positive relationship between positive management, development support for management, and teacher educator digital competence but a stronger positive correlation between self-reported effectiveness and teacher educator digital competence. The results of the teacher education function in qualifications in the digital classroom are examined.

The study has limitations, especially in the variation of responses from higher education institutions (HEIs). The research carried out an analysis related to multiple responses from the incoming responses, including investigating the influence of workplace supporting variables on the learning technology used. Strengths This research addresses the multidimensional concept of professional digital competence, which can be used as a reference in the future (Instefjord & Munthe, 2017). In addition, the table presented reflects the expected relationship between the variables, which indicates construct validity. This study also highlights the need to examine how workplace support for HEIs can affect the integration of digital competencies among teacher educators. There is a need to look more closely and detail how and where digital competencies for preservice teachers are developed and can also be explored (Instefjord & Munthe, 2017). The validation scheme and development flow of the instrument is shown in the Figure 1.

It is of tremendous importance to develop accurate instruments for digital testing skills. That is why Steffen's research has reassessed the measurement quality of the evaluation tool D21-Digital-Index. The D21-DigitalIndex is a two-year, prominent German research project. The DigComp is the theoretical basis for the instrument used in the D21 surveys (Wild & Schulze Heuling, 2021). Data from 1142 participants in the research analyzed from vocational and higher education establishments were utilized to estimate item parameters and the quality of the Item Response theory. Since selecting a suitable Item-Response- Theory (IRT-model) is critical for instrument assessment, two models were calculated and compared. The IRT model is a reference for measuring digital capabilities in the D21 survey (Wild & Schulze Heuling, 2021).

This research uses several IRT modeling methodologies to review the D21 Digital Index evaluation tool (Wild & Schulze Heuling, 2021). The analysis produces robust results with a sample size of more than 1,000 participants and shows fine measurement quality: Local independence was virtually always supported (Q1). The 2 PL-Birnbaum models have also shown that it fits better with the data than the 1 PL-Rasch model (Q2 and Q4). The level of discrimination and difficulty (Q3) often gave good results (Wild & Schulze Heuling, 2021). The data-gathering approach involves in-depth interviews with 30 in-service high school science teachers. This study showed the instructors' perception of their position and involvement in the digital revolution in three major categories: 1) non-observers, 2) careful participants, 3) conscientious participants. The research presents an extensive examination by examining the findings from structural and content analysis of the worldview of secondary school science instructors on the digital revolution. In this survey, 19 participants' opinions on the digital world are cautious people, eight are awareness-raising people, and only three have an external observation category. These findings imply that digital teachers analyze and rebuild their ideas about the world (Tsybulsky & Levin, 2019).

Overall, the findings of this study propose an instrument for validation, aimed at measuring the digital competency of preservice teachers in Indonesia. Factors such as information literacy, collaboration skills, security awareness, creative content creation, and the ability to evaluate digital skills are pertinent to examining these competencies. Consequently, the goal of this study is to develop a valid instrument that can be used to map the digital skills of the study's subjects. Additionally, the psychometric properties of the developed items will be assessed using the Rasch model to enhance the instrument's reliability. Figure 2 illustrates the theoretical framework of this study.

Based on the literature review that has been carried out, the following are the research objectives of this study, namely a). Measuring the psychometric properties of digital competency measurement instruments for pre-service Biology teachers; b). Mapping the digital competency difficulties area according to pre-service Biology teachers' perception using the Rasch analysis model.

This study's measuring model utilized Rasch model analysis. The approach is appropriate for measuring latent characteristics for evaluating human opinions, perceptions, and attitudes (Rusland et al., 2020). The Rasch analysis provides several psychometric components: descriptive analysis, Chi-square (χ2), person and item reliability, and Cronbach Alpha.

The descriptive analysis of the Rasch model exposes participants' judgments of knowledge and practice. Chi-square (X2) determines the level of significance among DFBQ questionnaire statements. The unidimensionality rating scale was utilized to evaluate the capability and measurability of the instrument under development. The person reliability index (PRI) reveals the consistency of an individual's responses, whereas the item reliability index (IRI) indicates whether or not the instrument adequately defines the latent variable. Finally, All of the analysis are described in Table 2.

Cronbach's Alpha, used as a reliability measure (reflecting the association between individuals and items), is 0.95, signifying that the instrument's quality is 'excellent,' with respondents providing consistent responses. The person reliability index (PRI), as shown in Table 4, is also 0.95, which indicates that the consistency of the respondents' answers is 'very good,' and that the scale effectively distinguishes between individuals. The same interpretative logic applies to the Item Reliability measurement of 0.99, which is also categorized as 'very good.' This suggests a high likelihood that respondents consistently answered the questions. A high estimate of item reliability indicates that the items are effective in defining the underlying construct (Bond et al., 2007).

The DFBQ can be considered a reliable instrument across various respondent groups. Table 3 further shows a high Cronbach's Alpha coefficient of 0.95, illustrating the interaction between the 261 respondents and the 36 items. According to instrument quality standards, a reliability score of 0.98 is classified as 'Excellent' (Sumintono & Widhiarso, 2014), indicating a strong interaction between respondents and items. An instrument with high internal consistency is regarded as highly dependable.

Table 3 shows the Person Separation Index (PSI), which indicates how effectively the DFBQ distinguishes between 'person abilities' in the latent trait. A higher separation index suggests a greater likelihood that respondents will answer the questions accurately. Conversely, the Item Separation Index (ISI) represents how well the items cover a range of simple to difficult concepts (Boone, 2016). The broader the range, the better the fit. In this study, the Person Separation Index is 4.29, and the Item Separation Index is 7.84, as shown in Table 3, demonstrating that the DFBQ has a robust distribution among respondents and items. These metrics establish the DFBQ as a suitable and reliable instrument for assessing undergraduate pre-service students' knowledge perceptions related to the digital competency frame work.The Person Separation Index (PSI) is used to assess the instrument's ability to distinguish between different levels of ability or competence among respondents (in this case, pre-service teachers). A higher PSI indicates that the instrument is effective in separating individuals into distinct groups based on their digital competency levels. Essentially, a high PSI suggests that the instrument can reliably identify who has higher or lower levels of the measured trait (e.g., specific digital competency).

Meanwhile, the Item Separation Index (ISI) measures the ability of the instrument to distinguish between the difficulty levels of the items themselves. It assesses how well the items are spread along the difficulty continuum, helping to identify whether the instrument includes items that range from easy to difficult, as appropriate for the trait being measured.

For this pilot study on digital competency mapping, a strong PSI would indicate that the instrument effectively categorizes pre-service teachers into different competency levels, which is crucial for understanding where they stand in relation to digital skills. Similarly, a robust ISI would show that the instrument's items are appropriately distributed across difficulty levels, making it possible to assess both lower and higher levels of digital competency. Together, these indices provide critical insight into the instrument’s readiness for broader application and its potential utility in shaping the teacher education curriculum.

In the case of the digital competency, the Logit Value of an Item (LVI) refers to the location of an item on the latent trait continuum. The logit value is a measure of the difficulty of an item, and it is derived from the relationship between the probability of a respondent answering an item correctly (or agreeing to it in the case of Likert scales) and the person’s ability level.

According to the map (Figure 4), items are arranged vertically according to their logit values (item difficulty), with more difficult items positioned higher (closer to positive values) and easier items lower (closer to negative values). For example, items C5 and C6 have higher LVI (logit values), meaning they are more difficult for the majority of respondents. Conversely, items D1 and D3, located at the lower end of the scale, are easier.

This mapping helps ensure that the test can effectively differentiate between individuals with varying levels of the measured ability or trait (digital competency, in this case). The Rasch model ensures that both the person abilities and item difficulties are measured on the same logit scale. The items are divided into four difficulty levels by segmenting the distribution of item logit scores according to their mean and standard deviation values.

As detailed in Table 4, Nine items (25%) fall into the "very difficult" category, meaning respondents found them challenging to agree with (LVI > 0.56 logits). In the second category, labeled "difficult" (+0.56 ≥ LVI ≥ 0.00), there are 12 items (30%). Additionally, 11 items (30%) are categorized as "easy" (0.00 ≥ LVI ≥ 0.56), while 5 items (22%) are classified as "very easy" for respondents to agree with (LVI < 0.64 logits). Table 4 provides a detailed description of the item difficulty classification according to the LVI analysis, offering further insights into the item difficulty levels of the DCFBQ by area and category:

To address the research objectives of this study—namely, evaluating the psychometric properties of digital competency measurement instruments for pre-service teachers—the data from each Digicom item aligned with the Rasch model's assumptions. All 36 items were retained, showing strong performance in terms of suitability, polarity, and local independence. Rasch analysis confirms that Digicom is an effective tool for assessing students' digital competency skills in everyday life, particularly within higher education and for future digital citizens.

The second objective was to map areas of digital competency difficulty based on pre-service teachers' perceptions using the Rasch analysis model. The study identifies critical gaps in the digital competencies of preservice biology teachers, particularly in content creation, problem-solving, and intellectual property comprehension. To address these deficiencies, curriculum design should prioritize practical workshops on multimedia content development, training in intellectual property rights, and advanced techniques for digital information retrieval. Furthermore, the inclusion of cybersecurity education and the enhancement of digital communication and collaboration skills are essential, given the increasing need for educators to manage data privacy and engage professionally in online spaces. Continuous formative assessments, such as digital portfolios, peer evaluations, and reflective exercises, will enable students to monitor their progress and adapt to emerging digital technologies and pedagogical methods.

A scaffolded and differentiated instructional approach is recommended to progressively build students’ digital competencies, providing additional support for those struggling in specific areas. The integration of practical, real-world activities—such as troubleshooting simulations and collaborative digital projects—can enhance their capacity for problem-solving and effective digital communication. Incorporating reflective practices and professional development planning will further cultivate a commitment to ongoing digital skill enhancement, ensuring that preservice teachers are equipped to utilize digital tools effectively within contemporary educational environments.

This study makes a significant contribution to the development of digital competency test instruments specifically tailored for preservice teachers, particularly in a region as diverse as Indonesia. Indonesia, with its vast demographic diversity and high internet penetration (185.3 million users), presents unique challenges and opportunities for digital education. The creation of this digital competency instrument is expected to support the development of a curriculum focused on enhancing the digital skills of preservice teachers. As future educators, these individuals will be responsible for teaching Generation Alpha—a cohort characterized by their unique relationship with technology and digital environments.

The instrument will provide a foundation for measuring and improving key competencies in areas such as digital literacy, content creation, cybersecurity, and online collaboration, which are essential in modern classrooms. Given Indonesia's socio-cultural and technological context, this tool can help ensure that teachers are adequately prepared to navigate the complexities of educating a generation Alpha that has been immersed in digital technologies from birth. Furthermore, the development of such competency assessments could inform broader educational policies and practices across the country, aligning teacher training programs with the needs of both students and the rapidly evolving digital landscape. Future research should consider expanding the range and number of sample respondents and also the variance of departments from other clusters (vocational, language, social sciences, and engineering).

Additionally, it is recommended to study the Logit Value of Person (LVP) to provide a more thorough discussion on digital competency mapping, leveraging the Rasch analysis model's capability to explore relationships between items and respondents' answers in research contexts.