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The Effects of Learning Design on Learning Activities Based on Higher Order Thinking Skills in Vocational High Schools

  • Dainita Rachmawati , Suharno Suharno EMAIL logo and Roemintoyo Roemintoyo
Published/Copyright: September 25, 2023
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Open Education Studies
From the journal Open Education Studies Volume 5 Issue 1

Abstract

The twenty-first century requires Vocational High School (VHS) graduates to have high-order thinking skills (HOTS). Although HOTS-based learning in VHS has been implemented in Indonesia, the graduates are less creative in complex work. Also, teachers have insufficient knowledge about this learning design. Therefore, the purpose of this study was to examine the relationship between learning implementation and planning, specifically focusing on the knowledge of teachers in planning lessons. To measure this knowledge, several aspects were established based on the concept of Anderson and Krathwohl’s Taxonomy 2001. The concept was used to measure knowledge, which was categorized into the factual, conceptual, procedural, and meta-cognitive dimensions. To analyze each dimension, critical thinking, creativity, collaboration, and communication were employed as key elements. A quantitative approach with a survey design and a random sample of productive subject teachers was used. Cross-sectional analysis and F-test were applied to the primary data using multiple linear regression. According to the F-test results, planning aspects simultaneously affect the implementation of HOTS-based learning in VHS. This is because the calculated F-value was greater than the table F-value. HOTS-based learning activities can be easily achieved in case the lesson plan has the same basis.

Keywords: HOTS; knowledge dimension; learning design; employability

1 Introduction

The ongoing processes of globalization, digitalization, and information changes are leading to a transformation of the job market, creating a pressing need for students to adapt their skill sets accordingly (García-Pérez, García-Garnica, & Olmedo-Moreno, 2021). Due to limited skills (González-rodríguez, Vieira, & Vidal, 2019), they might lose their jobs, resulting in a global unemployment problem (Jamaludin, Alias, DeWitt, & Ibrahim, 2020). Therefore, the Vocational High School (VHS) educational institution helps develop students’ intelligence, preparing them for work (Rujira, Nilsook, & Wannapiroon, 2020). This preparation involves developing skills to effectively solve problems (Harangus & Kátai, 2020). However, producing critical thinking skills requires changing teaching approach from low-order thinking skills (LOTS) to high-order thinking skills (HOTS) (Ramadhan, Suparman, Hairun, & Bani, 2020; Rosidin, Suyatna, & Abdurrahman, 2019; Suprapto, Fahrizal, Priyono, & Basri, 2017). This is because HOTS includes critical thinking and problem-solving skills, creativity, innovation, collaboration, and communication (4C) (Kurniawan, Santoso, & Utaminingsih, 2021; Lu, Yang, Shi, & Wang, 2021; Villalba, Castilla, & Redondo-Duarte, 2018).

HOTS would help to solve various challenges in a future career (Suprapto et al., 2017) and produces higher quality human resources (Misrom et al., 2020). In line with this, teacher skills and competencies affect knowledge and work skills among students (Mohamed, Puad, Rashid, & Jamaluddin, 2021). This implies that learning with 4C skills requires teachers to prepare themselves well (Haryani, Cobern, Pleasants, & Fetters, 2021) to change the learning approach from LOT to HOTS (Seman, Yusoff, & Embong, 2017). They need to develop appropriate HOTS-based learning methods (Sukatiman, Akhyar, Siswandari, & Roemintoyo, 2020) such as innovative learning models (Harangus & Kátai, 2020). Learning can be developed through Internet access, teacher development programs, collaboration, and curriculum guidelines to enhance the 4C skills (Haryani et al., 2021).

Learning in the twenty-first century requires students to develop their thinking skills (Komala, Lestari, & Ichsan, 2020). In this case, VHS teachers should adopt HOTS-oriented learning by developing media, learning materials, models, and strategies (Ichsan et al., 2019). It is vital to have 4C skills (Ramadhan et al., 2020; Rosidin et al., 2019), but VHS students have low HOTS levels (Deechai, Sovajassatakul, & Petsangsri, 2019; Ichsan et al., 2019). The low HOTS is attributed to teachers and students not being familiar with HOTS-based learning (Misseyanni, Marouli, & Papadopoulou, 2020; Sukatiman et al., 2020). Furthermore, the relationship between teachers and students influences the development of basic emotional, social, behavioural, and cognitive learning competencies (Darling-Hammond, Flook, Cook-Harvey, Barron, & Osher, 2020). Subsequently, teachers using less-effective learning strategies hinder the increase in HOTS (Misrom et al., 2020).

The state of education and student competencies contradict the needs of the twenty-first century (Elkababi, Atibi, Radid, Belaaouad, & Tayane, 2020) because their 4C abilities have not been maximized (Agussuryani, Sudarmin, Sumarni, Cahyono, & Ellianawati, 2022). For example, most VHS graduates are not competent because learning is not relevant to the industry (Suharno, Pambudi, & Harjanto, 2020). It is vital to examine the effect of teacher learning planning on implementing HOTS-based learning. This approach provides direction for vocational education institutions in preparing more efficient HOTS-based learning. Therefore, this study examined the direction and magnitude of the effect of learning planning dimensions F (Factual), C (Conceptual), P (Procedural), and M (Meta-cognitive) on the implementation of HOTS-based learning.

Recent research on HOTS has been conducted. Khaeruddin, Indarwati, Sukmawati, Hasriana, and Afifah, (2023) examined the effectiveness of the project-based learning model to improve the high thinking skills of junior high school students with the limited research subjects. This study compared pre-test and post-test scores. The results showed that project-based learning was able to improve HOTS characterized by higher post-test scores. This study only focused on analyzing student activity and did not test teacher readiness. Teacher readiness is important to test.

This study has a unique form and a specific focus. Therefore, this research is focused on analyzing the impact of HOTS learning planning in VHS, by exploring HOTS-related methods, media, learning outcomes, and innovative approaches. The main contribution of the field of learning finds results related to pedagogy for curriculum development. The findings of this study indicate teachers’ perceptions of the influence of HOTS lesson planning on VHS. The data used consisted of teachers’ self-reported perceptions, providing valuable insight into their viewpoints. In addition, the novelty in curriculum design is a new philosophical approach based on teachers’ perceptions of the influence of HOTS (Bloom’s taxonomy) lesson planning on VHS. Perception data can be used as material for performance evaluation, making regulations, and developing HOTS learning training. In addition, the resulting perceptions can be the basis for further research investigations. Practical learning performance is expected to be more developed and valuable.

2 Literature Reviews

2.1 Conceptual Framework

The use of VHS as an organizing framework for exploring the implementation of HOTS-based learning is based on the observation that VHS education primarily focuses on equipping students with practical work skills. Suharno et al. (2020) stated that the competencies of VHS graduates do not align with the needs of industries. Therefore, the development of students’ intelligence and their ability to effectively apply their skills in the workplace becomes a key responsibility of VHS (Rujira et al., 2020). To address this, attention must be given to the learning process at VHS, particularly in lesson planning, as the effectiveness of the learning process and the outcomes are influenced by the structure and design of planned lessons (Louws, Meirink, Van Veen, & Van Driel, 2017). The existing learning approaches in VHS have not been successful in enabling students to achieve HOTS (Deechai et al., 2019; Ichsan et al., 2019).

2.2 Twenty-First-Century Learning

Vocational learning requires critical and creative reasoning because most of the materials need detailed understanding and accurate analysis (Irmawan, Suharno, & Saputro, 2020). It is increasingly important to help students develop HOTS and a flexible knowledge base (Hmelo & Ferrari, 1997). Therefore, teachers need a learning plan that includes factual, conceptual, procedural, and meta-cognitive dimensions and pay attention to the learning process and evaluation (Souza, Greca, Silva, & Teixeira, 2019). Teaching experience and learning-planning models affect the process’s effectiveness and student learning outcomes (Louws et al., 2017).

Students’ thinking skills and high-level character could be improved with HOTS-based learning (Suhirman, Yusuf, Muliadi, & Prayogi, 2020). Active and experiential learning would be the most effective approach for this concept (Komala et al., 2020; Misseyanni et al., 2020) because it helps the development of students’ 4C skills (Sierra & Suárez-Collado, 2021). Furthermore, learning requires pedagogical transformation and innovation in the twenty-first-century schools (Carvalho & Santos, 2021). In this situation, teachers need to improve pedagogic competence to ensure appropriate HOTS methods (Haatainen & Aksela, 2021). Teachers need practical pedagogical training to significantly change roles based on communicative collaborative learning (Bernate, 2021; Urdin, Iglesias, Barandiaran, Ezkurra, & Juanikorena, 2021). Also, VHS teachers could develop participatory communication in their teaching (Kaiser, 2018).

The models that increase HOTS include student-centred learning (Ariyana, Pudjiastuti, Bestary, & Zamroni, 2018; Chandrasekaran, Anitha, & Thiruchadai Pandeeswari, 2021; Duran & Dökme, 2016; Wagh, Cook-Whitt, & Wilensky, 2017). Project-based learning (PBL) is an innovative learning approach that encourages students to be active in the process (Bell, 2010; Blumenfeld et al., 1991; Heaviside, Manley, & Hudson, 2018; Seibert, 2021). According to Nurdiyanto (2018), the use of the work-based learning model in VHS can increase student activeness in the learning process. A proactive personality is formed from student involvement in a learning centre (Caniëls, Semeijn, & Renders, 2018). Moreover, collaboration improves the quality of learning (Wallin, Hörberg, Harstäde, Elmqvist, & Bremer, 2020), while increased peer interaction and smart learning strategies would hone HOTS (Lu et al., 2021). Therefore, as facilitators of student development, teachers could build positive emotions towards vocational STEM (Lian, Tsang, & Zhang, 2021).

The learning planning aspect consists of the factual, conceptual, procedural, and meta-cognitive knowledge dimensions. The factual dimension questionnaire item contains the introduction, implementation, and the basis for the HOTS Lesson Plan, such as the cognitive taxonomy used. Conceptual (C) is the relationship between the basics in a larger structure that allows them to function together. Procedural (P) is a step in preparing a HOTS lesson plan, while meta-cognitive (M) is the awareness and knowledge of one’s cognition (Wilson, 2016).

2.3 HOTS

Teachers need special knowledge, motivation, and cognitive abilities (Roll & Ifenthaler, 2021). Their student’s analytical and predictive abilities could be developed through various cognitive learning methods such as analysis, synthesis, projection, and simulation (Masalimova et al., 2017). Bloom classified the cognitive process steps of analyzing (C4), evaluating (C5), and creating (C6) into high order (HOTS). Analysis is the breakdown and determination of the material’s relation with the purpose, while evaluating is considered something based on criteria or standards. Similarly, creating is arranging several elements to form a new coherent or functional unit (Krathwohl, 2002; Wilson, 2016).

The implementation of HOTS-based learning uses the twenty-first-century criteria, including critical thinking, creativity, communication, and collaboration (4C) (Duran & Dökme, 2016; Suprapto et al., 2017; Trilling & Fadel, 2009). Furthermore, communication skills are most valued for increasing pedagogical degrees (Urdin et al., 2021). This is because communicative experience in industry shapes technical skills such as interpersonal, verbal presentation, writing, and study (Jamaludin et al., 2020; Urdin et al., 2021). Therefore, the curriculum needs to integrate these skills by determining the type of communication required in the local industry (Jamaludin et al., 2020). Communication has an important role in collaboration by actively exchanging information, assuming responsibility, solving difficulties, and contributing to collective improvement and development to achieve common goals (Urdin et al., 2021). This also implies that the quality of learning could be improved through collaboration (Arinaitwe, 2021).

Creativity and innovation help students to have long-term employability (Jules & Sundberg, 2018), while teacher creativity affects the effectiveness and efficiency of learning (Ripki et al., 2020). Furthermore, creativity and innovation generate ideas and solve problems in new ways (Urdin et al., 2021). For instance, critical thinking helps to solve complex problems when working (Jang, 2016) through self-reflection, judging, and a skeptical view of knowledge. Also, problems are solved through knowledge building, careful reading, rationality, and engagement with knowledge (Bekbayeva, Galiyev, Albytova, Zhazykbayeva, & Mussatayeva, 2021). Assessments that improve HOTS include creativity, innovation, and problem-solving in real life (Sutarto & Jaedun, 2018) and skill development, which increases job quality (Jagannathan, Ra, & Maclean, 2019). Achieving this requires teachers to access the Internet, development programs, collaboration, and curriculum guidelines to improve 4Cs (Haryani et al., 2021). Moreover, they need to develop digital competencies (Rujira et al., 2020), which affect their performance in implementing the curriculum (Bereczki & Kárpáti, 2021). This is because lack of digital competence affects the application of online learning (Moreno-Guerrero, López-Belmonte, Pozo-Sánchez, & López-Núñez, 2021).

3 Methodology

3.1 Methods

This study used a quantitative approach with a survey method conducted in two schools in Surakarta. The sample was determined using a random sampling technique (Cresswell, 2014). The planning and implementation of HOTS-based learning are measured using a survey questionnaire that refers to the basic and higher-level thinking skills needed by teachers (Kurniawan et al., 2021; Lu et al., 2021; Turney et al., 1977). Also, multiple linear regression analysis was used to empirically test the influence of the F, C, P, and M planning dimensions on teachers’ learning implementation.

3.2 Population, Sample, and Instrument

The population in this study was 146 productive subject teachers from two State VHSs in Surakarta with Technology and Engineering expertise. Then, 107 productive subject teachers responded to the survey questionnaire, where the sampling technique was carried out randomly from the study population (Cresswell, 2014). The 107 samples were determined based on rounding off the Slovin formula.

n = N 1 + ( N × e 2 ) ,

n = 146 1 + ( 146 × 0.05 2 ) ,

n = 106.959707 .

Furthermore, an online questionnaire was used because of the pandemic, saving costs, and facilitating data collection and tabulation. Before being used, the questionnaire was tested for validity and reliability. The test was conducted at another school randomly. The preparation of each item is based on various aspects and dimensions of learning (Turney et al., 1977). The questionnaire with 24 questions consisted of two aspects, each divided into four dimensions. The learning planning aspect comprised ten questions, including three on F, four on C, one on P, and two on M. The aspects of implementing HOTS VHS-based learning consisted of 14 questions, including four on critical thinking dimensions, two on collaboration, five on creativity, and three on communication. In addition, the questionnaire used a four-point Likert scale (1 for never (N), 2 for rarely (R), 3 for often (O), and 4 for always (A)), as well as (1 for a very wrong answer, 2 for an incorrect answer, 3 for a correct answer, and 4 for a very correct answer). In the appendix, Tables A1 and A2 show the questionnaires used.

3.3 Data Analysis

This study used multiple linear regression analysis (Janie, 2012; Nihayah, 2019). This tests the contribution of the independent variables, specifically factual (F), conceptual (C), procedural (P), and meta-cognitive (M) on learning (Y) as the dependent variable. The measurement of the variables uses the average value of each item.

4 Result

The distributed questionnaire data were processed using the SPSS program. Multiple linear regression analysis has a classic assumption test, which consists of a normality, heteroscedasticity, multicollinearity, and autocorrelation test (if using time series data) (Janie, 2012; Nihayah, 2019). The data passed the classical assumption test. The normality test was conducted using graphical and statistical methods.

Figure 1 shows normally distributed data as evidenced by the close-to-straight-line distribution of the points. The interpretation of the image method was supported by statistical methods (Janie, 2012). The statistical method for the normality test was conducted through the Kolmogorov–Smirnov test. One-sample Kolmogorov–Smirnov test with nonparametric test procedure: one-sample KS (Iman & Conover, 1980). Selection of the Kolmogorov Smirnov test is based on a sample size of 106 (Chakravarti, Laha, & Roy, 1967).

Figure 1 Normal P–P plot of standardized residual.
Figure 1

Normal P–P plot of standardized residual.

The normality test used the one-sample Kolmogorov–Smirnov test by reading the exact sig two-tailed value. Table A3 shows two-tailed value of the exact significance of 0.093 > 0.05 means the data are normally distributed (Mehta & Patel, 2007).

The second classic assumption test is heteroscedasticity (having the same variance), conducted using graphical and statistical methods (Figure 2).

Figure 2 Scatterplots.
Figure 2

Scatterplots.

The distribution of the dots does not form a pattern, and therefore, there is no heteroscedasticity in the data. The statistical method that supports this statement is the Glejser test, conducted by regressing the absolute residual value (AbsUi) against other independent variables. Indicating β is significant, indicating that there is heteroscedasticity in the model (Janie, 2012).

Table A4 shows the SPSS output results with significance values of 0.529, 0.714, 0.191, and 0.449 for F, C, P, and M, respectively (Glejser test). Therefore, the model has no heteroscedasticity because all the significance values of the independent variables are more than 0.05, denoting they are homogeneous or have the same distribution of variance (Janie, 2012).

From Table A5, the condition index values between 10 and 30 indicate moderate to strong multicollinearity, while values more than 30 indicate very strong multicollinearity. These parameters indicate that three dimensions have condition index values between 10 and 30, implying moderate multicollinearity. Furthermore, one dimension has a value of more than 30, showing very strong multicollinearity (Janie, 2012).

Table A6 shows that parameter tolerance values are more than 0.10, while VIF values are less than or equal to 10, indicating no multicollinearity (Janie, 2012).

In the appendix, Table A7 shows the Durbin–Watson value of 2.048, which was obtained from the autocorrelation test.

du < dw < ( 4 − du ) ,

1.7631 < 2.048 < ( 4 − 1.7631 ) ,

1.7631 < 2.048 < 2.2369 .

The formula requirements of the regression model indicate no autocorrelation problem (Janie, 2012; Nihayah, 2019), which rarely occurs for cross-sectional data (Janie, 2012).

In the appendix, Table A8 shows the results of data processing, specifically in the calculation of R 2. The obtained R 2 value of 0.262 indicates that approximately 26.2% of the variation in the learning variables can be explained by the four independent variables, namely, F, C, P, and M. Therefore, the remaining 73.8% of the variation is attributed to factors or reasons outside the scope of these variables. Also, the R 2 of 0.2 or 0.3 is good enough for cross-sectional primary data (Nihayah, 2019; Raharjo, 2019).

Table A9 shows that the values of significance 0.000 < 0.05 and F count 9.076 > F table (2.46) (Junaidi, 2010) imply that the regression coefficient is not equal to zero. Also, the independent variable affects the dependent variable of learning (Janie, 2012).

This study uses an alpha of 0.05 (5%). In the appendix, Table A10 shows that out of the four independent variables in the model, F was significant at 5% (Janie, 2012), shown by the significance value of less than 0.05. Therefore, the learning variable is influenced by the variables F, C, P, and M with the following mathematical equation:

Y = 2.453 + 0.336 F − 0.003 C + 0.103 P − 0.165 M .

The positive constant coefficient indicates that learning increases, assuming the absence of variables F, C, P, and M. Similarly, the positive regression coefficient F shows that it increases learning with the absence of other independent variables. In contrast, the negative regression coefficient C indicates that its increase reduces learning without the other independent variables. Similarly, the positive regression coefficient P means that it increases the learning activity. On the contrary, the negative regression coefficient M indicates that its increase reduces learning.

5 Discussion

To produce quality graduates, schools should provide students with a solid foundation of knowledge in their respective majors, foster opportunities for communities of practice, and employ professional teachers (Loeis, Hubeis, Suroso, & Dirdjosuparto, 2023). In addition, teachers who possess an understanding of employability can significantly enhance the variability of their graduates’ career outcomes (Leadbeatter, Nanayakkara, Zhou & Gao, 2023).

The global job termination problem (Jamaludin et al., 2020) is attributed to the limited skills possessed (González-rodríguez et al., 2019). The changes in this century have necessitated upgrading workers’ skills (García-Pérez et al., 2021). This upgrade is indispensable for preparing VHS students for work (Rujira et al., 2020). VHS as a skilled developer (Rujira et al., 2020) requires HOTS for students to survive (Harangus & Kátai, 2020; Ramadhan et al., 2020; Rosidin et al., 2019; Suprapto et al., 2017).

Although not all students may initially see the value of HOTS, there are significant benefits to be gained. Meanwhile, VHS in traditional and applied arts typically prioritize the mastery of craftsmanship techniques, and some traditional artists and workers remain resistant to incorporating HOTS into their work processes, believing that these skills are unnecessary to preserve their authenticity. However, as the world becomes increasingly complex, even traditional workers are beginning to recognize the importance of HOTS. Conversely, students in technology-focused VHS are generally more aware of the HOTS value in their future careers.

The education obtained in VHS determines individual performance development (Marnisah et al., 2021). The skills and competencies of teachers in education affect learning success (Mohamed et al., 2021). Teaching experience and the form of lesson plan affect the effectiveness of the process and student learning outcomes (Louws et al., 2017). Students’ cognitive limitations result from conventional contextual learning (Jakaitis & Krugelis, 2018).

The standard of learning outcomes can be changed gradually by increasing the level of cognitive processing (Bloom’s taxonomy one level higher, minimum C4). Changes in learning outcomes standards need to pay attention to the competency standard matrix so that they remain relevant. The competency standard matrix can be changed by adjusting the Indonesian National Work Competency Standards. An in-depth analysis of the industry is needed so that the student’s HOTS level is relevant to their field. The Government of Indonesia has an institution tasked with managing the employment sector. The Manpower Office cooperates with various industries and educational institutions to develop competency standards according to educational levels.

This study conducted a series of multiple linear regression analyses to ensure that the requirements, including normality, autocorrelation, homogeneity, and multicollinearity, are met. Normality testing uses the one-sample Kolmogorov–Smirnov test with an exact approach (Mehta & Patel, 2007). Generally, cross-sectional data (Ary, Jacobs, Sorensen, & Razavieh, 2010) rarely have autocorrelation problems (Janie, 2012). In this study, the autocorrelation test is still conducted to ascertain the absence of problems. Since the Durbin–Watson test produces numbers that meet the regression model formula requirements, there are no autocorrelation problems (Janie, 2012; Nihayah, 2019). The significance value in the Glejser test shows that heteroscedasticity does not occur. Multicollinearity detection uses condition index, tolerance, and VIF values. The tolerance and VIF values show that multicollinearity does not occur. This is different from the condition index value, which shows strong multicollinearity. The multicollinearity is proven again by the variance proportions value in that one line. In case the number shows more than 0.9 in that line less than twice (Regorz, 2020), there is no multicollinearity. This is indicated by the value of the variance proportion in Section 4, where each row has a number less than 0.9.

Since the results showed the value of significance = 0.000, the null hypothesis is rejected, while the alternative hypothesis is accepted. Therefore, factual, conceptual, procedural, and metacognitive dimensions affect learning (Y). R 2 value of 0.262 indicates that the learning variable can be explained by four independent variables (F, C, P, and M) of 26.2%, while other things explain 73.8%.

Y = 2.453 + 0.336 F – 0.003 C + 0.103 P – 0.165 M .

The value 2.453 shows that the coefficient of the constant is positive. Therefore, learning tends to increase without the variables F, C, P, and M. The regression coefficient F is 0.336, indicating that without other independent variables, the increase in F positively affects learning, specifically by 0.336. This aspect consists of the teacher’s basic knowledge (Krathwohl, 2002) about planning HOTS lessons, in learning, assessment, and the cognitive taxonomy used. Previous studies showed that teachers’ understanding of HOTS planning and assessment is low (Sutarto & Jaedun, 2018).

The regression coefficient of C is –0.003, implying that without other independent variables, the increase in the C leads to a decline in learning. Specifically, it includes understanding the relationship between basic knowledge (Krathwohl, 2002), such as planning in the learning model, the form of questions, and other aspects. Other studies show that dimension C can increase HOTS (Kurniawan et al., 2021; Lu et al., 2021; Villalba et al., 2018).

The regression coefficient P is 0.103, indicating that the increase in dimension P improves learning without other independent variables. It affects learning by 0.103 and relates to the flow and method (Krathwohl, 2002) of planning HOTS lessons correctly. The ability of teachers to plan and implement HOTS is still low (Sutarto & Jaedun, 2018).

The regression coefficient M is −0.165 (negative), indicating that without other independent variables, the increase in M negatively affects learning, specifically by –0.165. It is an in-depth knowledge (Krathwohl, 2002) related to HOTS learning planning, such as deepening to overcome problems and shortcomings. The teacher can solve problems that occur in class.

The assertion that independent variables affect learning in this study is confirmed theoretically and empirically. The independent variables are F and P, with a positive and significant effect on learning. The F count is 9.076, greater than the F table of 2.46, and a significance of 0.000, smaller than 0.005. These results are in line with other previous studies that established that planning contributes to the learning process (Hadriana, Mahdum, Isjoni, Futra, & Primahardani, 2021).

Teachers can improve the 4C dimensions of planning by accessing various resources (Haryani et al., 2021). The change in learning to HOT (Seman et al., 2017) should be carefully planned (Haryani et al., 2021). Immature HOTS lesson planning makes learning ineffective (Misrom et al., 2020). In case teachers are unfamiliar with this learning base, HOTS remains low (Misseyanni et al., 2020; Sukatiman et al., 2020). Competence positively and significantly affects performance (Marnisah et al., 2021). Therefore, teachers need to significantly improve pedagogic competence (Haatainen & Aksela, 2021) with pedagogical training (Bernate, 2021) to enhance their confidence when conducting HOTS-based learning (Wang, 2022).

These results show that F and P positively relate to learning. The topics that affect HOTS learning include planning on basic knowledge and correct implementation of HOTS learning (Krathwohl, 2002). Planning should focus on these two topics to improve teachers’ pedagogic competence toward HOTS learning (Sutarto & Jaedun, 2018).

The success of HOTS learning depends on the teacher, collaboration, and awareness from various parties. This study shows that only 26.2% can be explained by the lesson plans. Meanwhile, the remaining 73.8% is determined by various parties (Haider & Sundin, 2022).

We have limitations in conducting research. We were unable to perform alternative measurements, so we were unable to compare them with the present study. However, if it is possible to make alternative measurements, the nature of the measurements will tend to complement the data because the measurements will be performed differently. We also cannot plot the results of the knowledge measurement in this study together with the results of alternative measurements because we do not have alternative measurement data.

6 Conclusion

The perceptions of teachers regarding the HOTS-based learning can be explained through the 26.2% lesson plan aspect. It is worth noting that while the factual (0.336) and procedural (0.103) aspects positively affect learning. Meanwhile, the conceptual (−0.003) and metacognitive (−0.165) aspects have a negative effect. Therefore, education providers should prioritize planning for basic knowledge while incorporating appropriate HOTS learning strategies. Teachers can also integrate HOTS’s cognitive taxonomy in planning and learning activities to further enhance student outcomes. However, it is important to acknowledge that this study only focused on teacher efforts in preparing for HOTS-based learning. Therefore, further studies are needed to explore the effects of training on the application of HOTS learning for students.

  1. Funding information: This research was funded by Sebelas Maret University. (Excellent Research/PU-UNS, Leading Field: Human Development and National Competitiveness).

  2. Author contributions: SS and RR designed the experiments. DR carried them out and prepared the manuscript with contributions from all co-authors.

  3. Conflict of interest: The authors state no conflict of interest.

  4. Informed consent: Informed consent has been obtained from all individuals included in this study.

  5. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the author upon reasonable request.

Appendix

Table A1

Questionnaire aspects of learning planning

  • A. Factual

  • 1. Have you ever heard of HOTS-based learning?

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 2. Have you implemented HOTS?

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 3. Is your lesson plan based on HOTS?

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • B. Conceptual

  • 1. What do you think about HOTS-based learning?

    1. Learning that develops critical thinking skills, creativity, communication, and collaboration (4C)

    2. Learning is oriented towards higher-order thinking skills (HOTS)

    3. The process of interaction of students with educators and learning resources in a learning environment

    4. Do not know

  • 2. Operational verbs that include HOTS are…

    1. Create and analyze

    2. Identify and create

    3. Identify and understand

    4. Do not know

  • 3. The learning model that includes HOTS is…

    1. Project based learning and direct learning (DL, direct learning)

    2. Disclosure/inquiry learning model (discovery/inquiry learning)

    3. All the statements above are true

    4. Nothing

  • 4. Examples of questions based on HOTS are…

    1. Mention the material that has been studied today!

    2. Compare what we learned today and yesterday!

    3. Make a summary of the material we learned today!

    4. Summarize what you have learned today!

  • C. Procedural

  • 1. The steps for preparing a HOTS-based lesson plan are:…

    1. Review the syllabus and develop learning activities (determine objectives, time allocation, and learning resources)

    2. Identify learning materials and describe the types of assessment

    3. All of the above statements are true

    4. All of the above statements are false

  • D. Metacognitive

  • 1. If in learning the student's response shows a passive attitude (for example: not paying attention), what should the teacher do?

    1. Let

    2. Submitting to Counseling Guidance (BP)

    3. Punish

    4. Create a more interactive classroom atmosphere

  • 2. All education experts state that in the twenty-first-century learning should be based on HOTS. What impact will be experienced by students if the teacher does not apply HOTS-based learning?

    1. Students are not critical and creative

    2. Students are not critical and creative

    3. Learning is lagging behind

    4. Do not know
Table A2

Questionnaire aspects of implementation of learning

  • A. Critical Thinking

  • 1. I include arguments/reasons in every explanation I give

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 2. In learning, I convey material based on problem solving

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 3. I convey the conclusion at the end of each lesson

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 4. The questions I gave to students were based on problem solving

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • B. Collaborative

  • 1. In learning, I make groups of students (small/medium/large) to discuss.

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 2. I give students the opportunity to solve problems through discussions with their friends

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • C. Creative

  • 1. In learning, I ask questions about the material to students.

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 2. I give a variety of questions so that students can develop the subject matter

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 3. I create challenging questions for students to work on

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 4. If students cannot answer independently, I provoke ideas to students

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 5. I think about the benefits obtained from materials for everyday life.

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • D. Communication

  • 1. I provide feedback on student answers

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 2. In learning, I give certain signs (colours/lines) for important materials

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).

  • 3. I am actively involved when students work on assignments in groups

    1. never (N),

    2. rarely (R),

    3. often (O),

    4. always (A).
Table A3

Normality test (exact significance (two tailed)). One-sample Kolmogorov–Smirnov test

One-Sample Kolmogorov–Smirnov Test
Unstandardized Residual
N 107
Normal Parametersa,b Mean 0.0000000
Std. deviation 0.30182632
Most extreme differences Absolute 0.118
Positive 0.118
Negative −0.102
Test statistic 0.118
Asymp. Sig. (two-tailed) 0.001c
Exact Sig. (two-tailed) 0.093
Point probability 0.000

aTest distribution is normal, bCalculated from data, cLilliefors significance correction.

Table A4

Glejser test coefficients

Unstandardized coefficients Standardized coefficients t Sig.
Model B Std. error Beta
1 (Constant) 0.217 0.205 1.058 0.292
F 0.025 0.040 0.065 0.632 0.529
C −0.017 0.045 −0.038 −0.368 0.714
P −0.047 0.036 −0.181 −1.318 0.191
M 0.041 0.061 0.093 0.679 0.499
Table A5

Multicollinearity detection (condition index) collinearity diagnostics

Model Dimension Eigenvalue Condition index Variance proportions
(Constant) F C P M
1 1 4.926 1.000 0.00 0.00 0.00 0.00 0.00
2 0.039 11.309 0.01 0.14 0.07 0.35 0.01
3 0.019 15.904 0.02 0.82 0.31 0.03 0.01
4 0.011 21.057 0.27 0.03 0.58 0.26 0.13
5 0.005 32.529 0.70 0.02 0.03 0.36 0.86
Table A6

Multicollinearity test (tolerance value and VIF) coefficients

Collinearity statistics
Model Tolerance VIF
1 (Constant)
F 0.907 1.102
C 0.915 1.092
P 0.511 1.959
M 0.506 1.975
Table A7

Autocorrelation test (Durbin–Watson) model summary

Model R R 2 Adjusted R 2 Durbin–Watson
1 0.512a 0.262 0.234 2.048

aPredictors: (Constant), M, C, P, F.

Table A8

Coefficient of determination model summary

Model R R 2 Adjusted R 2 Std. error of the estimate
1 0.512a 0.262 0.234 0.30769

aPredictors: (Constant), M, C, P, F.

Table A9

Simultaneous significance test (F statistics test) ANOVAa

Model Sum of squares df Mean square F Sig.
1 Regression 3.437 4 0.859 9.076 0.000b
Residual 9.657 102 0.095
Total 13.094 106

aDependent variable: learning.

Table A10

Multiple linear regression coefficients

Unstandardized Coefficients Standardized Coefficients t Sig.
Model B Std. error Beta
1 (Constant) 2.453 0.319 7.691 0.000
F 0.336 0.062 0.485 5.428 0.000
C −0.003 0.071 −0.004 −0.044 0.965
P 0.103 0.055 0.221 1.857 0.066
M −0.165 0.095 −0.208 −1.738 0.085

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Received: 2022-07-04
Revised: 2023-08-19
Accepted: 2023-08-23
Published Online: 2023-09-25

© 2023 the author(s), published by De Gruyter

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Articles in the same Issue

  1. Special Issue: Transforming Education in the COVID-19 Era
  2. Digital Learning Ecosystem: Current State, Prospects, and Hurdles
  3. Special Issue: Building Bridges in STEAM Education in the 21st Century - Part I
  4. STEMbach Experiences at Higher Education
  5. Poly-Universe Resource for Solving Geometric Tasks by Portuguese Basic Education Students
  6. Automatic Exercise Generation for Exploring Connections between Mathematics and Music
  7. “Literally I Grew Up” Secondary–Tertiary Transition in Mathematics for Engineering Students beyond the Purely Cognitive Aspects
  8. Narrative Didactics in Mathematics Education: Results from a University Geometry Course
  9. Solving Authentic Problems through Engineering Design
  10. Using STEAM and Bio-Inspired Design to Teach the Entrepreneurial Mindset to Engineers
  11. Escape Rooms for Secondary Mathematics Education: Design and Experiments
  12. Towards a Pedagogical Model Applying Commedia dell’Arte and Art Workshops in Higher Education Design Studies
  13. A Pilot Study on Investigating Primary School Students’ Eye Movements While Solving Compare Word Problems
  14. Utilising a STEAM-based Approach to Support Calculus Students’ Positive Attitudes Towards Mathematics and Enhance their Learning Outcomes
  15. Regular Articles
  16. Motivators for University of Professional Studies Accra Students to Adopt a Learning Management System in Ghana
  17. Self-Confidence and STEM Career Propensity: Lessons from an All-Girls Secondary School
  18. “Tis Early Practice only Makes the Master”: Nature and Nurture in Economic Thinking During School Time – A Research Note on Economics Education
  19. Commuter Students and Psychological Sense of Community: How Ties to Home Relate to Academic Success
  20. International Students’ Experience of Remote Teaching and Learning in Portugal
  21. Exploring the Validity of a Single-Item Instrument for Assessing Pre-Service Primary School Teachers’ Sense of Belonging to Science
  22. Barriers to Basic School Teachers’ Implementation of Formative Assessment in the Cape Coast Metropolis of Ghana
  23. The Impact of Organizational Climate on Teacher Enthusiasm: A Two-Staged Structural Equation Modelling–Artificial Neural Network Approach
  24. Estimation of GPA at Undergraduate Level using MLR and ANN at Arab International University During the Syrian Crisis: A Case Study
  25. Research is for Hunters, Teaching for Farmers. Investigating Solutions to Lecturer-Related Problems of the Teaching–Research Mission of Swiss Universities of Applied Sciences
  26. Strategic Performance Management Using the Balanced Scorecard in Educational Institutions
  27. Reciprocal Teaching as a Cognitive and Metacognitive Strategy in Promoting Saudi University Students’ Reading Comprehension
  28. The Effects of Learning Design on Learning Activities Based on Higher Order Thinking Skills in Vocational High Schools
  29. Estimating the Returns to Education Using a Machine Learning Approach – Evidence for Different Regions
  30. Conceptualizing and Reimagining the Future of Inclusive Education in the UAE
  31. Transformative Assessment Practices in Mathematics Classes: Lesson from Schools in Jimma, Ethiopia
  32. Teacher’s Constraints and Challenges in Implementing Student Attitude Assessment in Junior High School
  33. Pedagogical Design as a Tool to Increase Students’ Learning Motivation During Distance Learning
  34. The Effectiveness of Online Problem-Based Learning Tasks on Riyadh’s Secondary School Students’ Problem-Solving Ability and Programming Skills
  35. Review Articles
  36. Underlying Educational Inequalities in the Global and Fijian Context
  37. Challenges and Emerging Perspectives of Quality Assurance and Teacher Education in Nigerian Universities: A Literature Review
https://doi.org/10.1515/edu-2022-0202
Keywords for this article
HOTS; knowledge dimension; learning design; employability
Creative Commons
BY 4.0

Articles in the same Issue

  1. Special Issue: Transforming Education in the COVID-19 Era
  2. Digital Learning Ecosystem: Current State, Prospects, and Hurdles
  3. Special Issue: Building Bridges in STEAM Education in the 21st Century - Part I
  4. STEMbach Experiences at Higher Education
  5. Poly-Universe Resource for Solving Geometric Tasks by Portuguese Basic Education Students
  6. Automatic Exercise Generation for Exploring Connections between Mathematics and Music
  7. “Literally I Grew Up” Secondary–Tertiary Transition in Mathematics for Engineering Students beyond the Purely Cognitive Aspects
  8. Narrative Didactics in Mathematics Education: Results from a University Geometry Course
  9. Solving Authentic Problems through Engineering Design
  10. Using STEAM and Bio-Inspired Design to Teach the Entrepreneurial Mindset to Engineers
  11. Escape Rooms for Secondary Mathematics Education: Design and Experiments
  12. Towards a Pedagogical Model Applying Commedia dell’Arte and Art Workshops in Higher Education Design Studies
  13. A Pilot Study on Investigating Primary School Students’ Eye Movements While Solving Compare Word Problems
  14. Utilising a STEAM-based Approach to Support Calculus Students’ Positive Attitudes Towards Mathematics and Enhance their Learning Outcomes
  15. Regular Articles
  16. Motivators for University of Professional Studies Accra Students to Adopt a Learning Management System in Ghana
  17. Self-Confidence and STEM Career Propensity: Lessons from an All-Girls Secondary School
  18. “Tis Early Practice only Makes the Master”: Nature and Nurture in Economic Thinking During School Time – A Research Note on Economics Education
  19. Commuter Students and Psychological Sense of Community: How Ties to Home Relate to Academic Success
  20. International Students’ Experience of Remote Teaching and Learning in Portugal
  21. Exploring the Validity of a Single-Item Instrument for Assessing Pre-Service Primary School Teachers’ Sense of Belonging to Science
  22. Barriers to Basic School Teachers’ Implementation of Formative Assessment in the Cape Coast Metropolis of Ghana
  23. The Impact of Organizational Climate on Teacher Enthusiasm: A Two-Staged Structural Equation Modelling–Artificial Neural Network Approach
  24. Estimation of GPA at Undergraduate Level using MLR and ANN at Arab International University During the Syrian Crisis: A Case Study
  25. Research is for Hunters, Teaching for Farmers. Investigating Solutions to Lecturer-Related Problems of the Teaching–Research Mission of Swiss Universities of Applied Sciences
  26. Strategic Performance Management Using the Balanced Scorecard in Educational Institutions
  27. Reciprocal Teaching as a Cognitive and Metacognitive Strategy in Promoting Saudi University Students’ Reading Comprehension
  28. The Effects of Learning Design on Learning Activities Based on Higher Order Thinking Skills in Vocational High Schools
  29. Estimating the Returns to Education Using a Machine Learning Approach – Evidence for Different Regions
  30. Conceptualizing and Reimagining the Future of Inclusive Education in the UAE
  31. Transformative Assessment Practices in Mathematics Classes: Lesson from Schools in Jimma, Ethiopia
  32. Teacher’s Constraints and Challenges in Implementing Student Attitude Assessment in Junior High School
  33. Pedagogical Design as a Tool to Increase Students’ Learning Motivation During Distance Learning
  34. The Effectiveness of Online Problem-Based Learning Tasks on Riyadh’s Secondary School Students’ Problem-Solving Ability and Programming Skills
  35. Review Articles
  36. Underlying Educational Inequalities in the Global and Fijian Context
  37. Challenges and Emerging Perspectives of Quality Assurance and Teacher Education in Nigerian Universities: A Literature Review
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