Czech and Slovak intended curricula in science subjects and mathematics: a comparative study

ABSTRACT

A curriculum is generally regarded as an instructional plan that describes what, why and how students should learn. In this comparative study, we analysed the Czech and Slovak intended curricula of science subjects (physics, chemistry, biology, geography, and geology) and mathematics by comparing their national curriculum documents in terms of learning outcomes at the lower secondary level (ISCED level 2). Our analysis showed significant differences in the number of obligatory learning outcomes, which were much higher in the Slovak curriculum than in the Czech curriculum. The structure of these outcomes also differed across subjects and between countries. Nevertheless, the cognitive demands of the learning outcomes analysed using the revised Bloom’s taxonomy were similar in the two countries, but metacognitive knowledge and higher-level cognitive processes were rarely represented in either. Additionally, by inductive content analysis of the Slovak curriculum document, we identified two significant groups of cross-curricular requirements, namely outcomes related to scientific inquiry and outcomes requiring working with information. Overall, these learning outcomes are underrepresented in both analysed documents (particularly in the Czech document) even though the skills that these outcomes develop are in high demand in the current context.

KEYWORDS:

• Curriculum research
• comparative study
• learning outcomes

Introduction

All countries strive to develop an effective educational plan able to flexibly respond to changing conditions in society. Current trends in Czechia (Czech Republic) include, after many years of minority adjustments, a move towards major curriculum changes reflecting the challenges of our modern globalised world (e.g. greater emphasis on Information and Communications Technology (ICT) skills and critical thinking, among other priorities). Usually, in the education policy framework, countries look for inspiration in adjustments to educational programmes and specific teaching goals abroad. The objects of interest are either countries with good results in international comparative testing, countries geographically, culturally, historically, or otherwise close, or countries struggling with similar problems. In our study, we aimed at comparing two countries that meet the last two criteria – Czechia and Slovakia. We compared their curricula of science subjects (biology, chemistry, physics, geography, and geology) and mathematics based on obligatory learning outcomes stated in national curriculum documents. Although mathematics has distinct content, we include it for several reasons. First of all, with the growing global significance of STEM education, understanding the intersection and complementarity of mathematics and science is crucial. Moreover, by analysing mathematics and science subjects, we maintain consistency in our research approach which allows us to find connections across different subjects.

This study is a follow-up article comparing curricula of two countries, which furthers the findings published separately for each subject (Janoušková et al., 2019) and subsequently included in a joint interdisciplinary comparison (Kácovský et al., 2022).

Theoretical framework

Research context

Only a few countries in the world have mutually intelligible languages, similar cultures, and a shared history as do Czechia and Slovakia. This similarity provides us with interesting opportunities for educational comparisons in the area of curricula development. How are the current national curricula determined by the common educational traditions of both countries, and in which way are they affected by recent international trends in curriculum development?

Common history has bound together Czechs and Slovaks since the early mediaeval period. During the industrial age, both nations became a part of the Austro-Hungarian Empire and at the beginning of the twentieth century, they joined in the shared state called Czechoslovakia. This multinational country was democratic until the beginning of the Second World War, after which Czechoslovakia fell under Soviet Union influence. A few years after the fall of the communist regime in central Europe, in 1993, Czechoslovakia split into new national states, Czechia and Slovakia. The new countries have separately tackled the same challenges, such as political and economic transformation, and integration to the European Union.

The roots of the Czech and Slovak educational systems are based on the principles of Maria Theresa’s school reform in the eighteenth century. This reform led to the establishment of a public-school system throughout the whole Habsburg monarchy, and also mandatory schooling. This joint base was further consolidated during the First Czechoslovak Republic (1918–1938). Due to the lack of public secondary schools and teachers in the Slovak part, Czech teachers were asked to help (Kudláčová, 2020). This help has bound Czech and Slovak school conception together. After the Second World War, the educational system of Czechoslovakia was strongly affected by the Soviet educational model as in other Soviet vassals’ countries (satelites). Except for mother tongue school subjects, the Czech and Slovak curricula were similar.

After the fall of the communist regime in 1989, both curricula were modified only slightly – ideologically backgrounded learning content was removed, but the teaching methods remained unchanged. After their split in 1993, Czechia and Slovakia started to deal with their own curricula reforms.

The current Czech national curriculum was introduced in 2005 (has been in force in schools since 2007) and innovated with a small revision in 2021. The goal of this revision was to strengthen the role of ICT and to place the development of digital literacy at the level of key competences; as a side effect, expected outcomes in other educational fields were reduced. The Slovak curriculum was introduced in 2008 and innovated in 2015.

Curriculum comparisons

A curriculum is broadly perceived as a learning plan that describes what, why and how students should learn (Taba, 1962). The curriculum model has three parts: the intended, the implemented and the achieved curriculum (Kridel, 2010). The intended curriculum defines expectations and goals in terms of the knowledge, skills, values, and attitudes that students should achieve and develop during their school education and how the outcomes of the teaching process should be evaluated. The implemented curriculum is the curriculum that is put into practice in schools, and the achieved curriculum describes the knowledge and understanding that students acquire during their education.

Governments have become increasingly interested in international comparisons between educational systems. This interest stems from efforts to ‘develop policies to enhance individuals’ social and economic prospects, provide incentives for greater efficiency in schooling, and help to mobilise resources to meet rising demands’ (OECD, 2021).

There are more directions in which comparative education can be conducted. One segment includes studies on the intended curriculum, which is considered crucial for any national educational system (Wan & Lee, 2021). Pawilen and Sumida (2005) published a study on similarities and differences between the intended science curricula for elementary levels of the Philippines (with poor science performance in TIMSS) and Japan (with outstanding science performance), focusing on the aims, content, and organisation of the curricula. Lee et al. (2015) compared the Korean and Singapore intended primary science curricula, and in their subsequent and highly detailed study, Lee et al. (2017) provided further insights into primary science curriculum standards in mainland China, Hong Kong, Taiwan, Korea, Japan, and Singapore. Wei and Ou (2019), in turn, discussed the similarities and differences between the junior high school science curriculum standards of four Chinese regions.

To our best knowledge, however, only a few studies have compared Czech and Slovak curricula. In the field of science education, Poupová et al. (2019) compared the biology curriculum at ISCED level 2 in Czechia and in selected post-communist European countries. More focused on a specific topic of geography, Hanus and Marada (2013) addressed map skills.

In our research, we narrowed the study of intended curricula to an analysis of official national curriculum documents and frameworks, which should serve as a basic vision, a widely accepted direction for all stakeholders in the educational process. More specifically, we focused on what national curriculum documents expect students to manage by the end of their instruction – depending on the country, these requirements are known as educational goals, outcomes, or objectives. Henceforth, we will uniformly use the umbrella term learning outcomes to express all three concepts, in line with, e.g. Harden (2002).

Research questions

When analysing obligatory learning outcomes, we chose two aspects. Firstly, we used an analytical framework of cognitive, affective, and psychomotor knowledge domains (e.g. Wei & Ou, 2019) to compare intellectual demands of particular learning outcomes. In line with, e.g. Lee et al. (2015) or Elmas et al. (2020), we addressed the cognitive domain only as the relevant domain when assessing intellectual demands. Inspired by the methodology of these research groups, we chose Revised Bloom’s Taxonomy as the instrument to assess educational cognitive demands prescribed by learning outcomes.

Secondly, we used our in-depth knowledge gained from assessing intellectual demands of the learning outcomes to perform an inductive content analysis. The aim of this analysis was to identify groups of outcomes with similar or even identical content that occur across subjects and are not strictly related to subject-specific matter (such as experimenting, modelling, working on projects, among other tasks).

Thus, regarding mathematics and science subjects, we ask the following research questions:

1. How detailed in terms of the number of obligatory learning outcomes are the science subjects and mathematics curricula of Czechia and Slovakia?

2. What cognitive processes and what types of knowledge are most often required by learning outcomes defined in the national curricula of Czechia and Slovakia?

3. What cross-curricular requirements are included in learning outcomes and how do they differ in both countries?

Methodology

In this study, document analysis was used as a key research technique. Concerning the research question (1), we simply identified and counted obligatory learning outcomes in both national documents. To address research question (2), we applied deductive content analysis framed by Revised Bloom’s Taxonomy. To answer research question (3), i.e. to capture the cross-curricular requirements, we chose inductive content analysis. Both deductive and inductive approaches are described in detail below.

Deductive content analysis employing revised Bloom’s taxonomy

This famous classification of cognitive goals was introduced by Bloom et al. (1956) and consists of six levels defined by the following cognitive processes: Knowledge, Comprehension, Application, Analysis, Synthesis, and Evaluation. In Bloom’s taxonomy, mastery of any level is a prerequisite for moving to the next (higher) level, which has been challenged by some of Bloom’s followers (e.g. Ormell, 1974). Similarly, the unidimensionality of this taxonomy has been identified as a weakness because learning objectives typically include both content and action.

These shortcomings were addressed in a revision of the original taxonomy proposed by Anderson et al. (2001), known as the Revised Bloom’s Taxonomy (RBT). RBT also consists of six levels, namely Remember, Understand, Apply, Analyse, Evaluate, and Create; compared to the original taxonomy, the top two categories are ordered in reverse. Furthermore, RBT defines a second dimension that distinguishes four types of knowledge – Factual, Conceptual, Procedural and Metacognitive knowledge. Thus, two pieces of information can be associated with each learning outcome – the cognitive process required (expressed by a verb) and the type of knowledge developed (expressed by a noun). Thanks to its two-dimensionality (see Table 1), RBT provides researchers with more accurate characteristics of cognitive goals and challenges than the original taxonomy. The original research article by Anderson et al. (2001) helps researchers by providing both theoretical background and specific examples on the use of the taxonomy in educational research. Although some studies have indicated the weaknesses of RBT (Amer, 2006), the revised taxonomy has recently been applied in the analysis of curriculum documents in various regions (e.g. Elmas et al., 2020; Lee et al., 2017; Wei & Ou, 2019; Yaz & Kurnaz, 2020).

Table 1. The RBT matrix with illustrative examples of coded learning outcomes.

Inductive content analysis

Since RBT covers non-content characteristics of learning outcomes (types of knowledge and cognitive processes), we hypothesised that an inductive analysis of learning outcomes would help us to identify the specific requirements that permeate across subjects. Inductive content analysis is a methodological, text-driven approach, going from concrete to abstract, and from specific to general (Graneheim et al., 2017), which is commonly used when the data collection approach is open (Kyngäs, 2020) and the existing knowledge is fragmented (Elo & Kyngäs, 2008). Whilst carefully reading the text, the researcher focuses on searching for patterns in data and for similarities and differences therein (Graneheim et al., 2017). The points of interest are marked in the open coding process and grouped into categories, which are not determined in advance. Typically, the number of categories is gradually reduced by collapsing those that are similar or dissimilar into broader categories of higher order (Elo & Kyngäs, 2008). The resulting categories lead to concepts that help to describe the explored phenomenon, thereby generating new knowledge (Cavanagh, 1997). When interpreting the findings, Graneheim et al. (2017) warn of only superficial descriptions, while Kyngäs (2020) emphasises that content analysis ‘can be considered a discussion between the researchers and their data’.

Research sample: relevant curriculum documents

The Czech and Slovak primary and lower secondary systems are mostly single structured and termed basic education. Both countries use a two-level curriculum structure – state and school levels. The state level represents a national framework document, which defines the conception of education, its objectives and basic content, and general conditions for its implementation. The school level document provides a framework for implementing education in particular schools and is defined by schools in line with the national framework document.

In our research, we studied the latest available editions of national framework documents on lower secondary education (ISCED level 2) in their original languages. In Czechia, ISCED level 2 consists of 4 grades (grades 6–9), while in Slovakia it includes 5 grades (grades 5–9).¹

The national core curriculum for lower secondary education in Czechia (CZC) is found in The Framework Educational Programme for Basic Education (Ministry of Education, Youth and Sport of the Czech Republic, 2021). The New State Educational Programme for the Second Stage of the Primary School (National Institute for Education in Slovak Republic, 2015) represents the national curriculum at ISCED level 2 in Slovakia (SKC).

In both documents, the educational content is divided into educational areas; in this study, we address the areas Mathematics and Its Application (CZC), Mathematics and Working with Information (SKC) respectively, and Humans and Nature (both), which include the following educational fields: Physics, Chemistry, Geography and Biology. Both CZC and SKC place geology education in the subject of biology; this is a rare approach across Europe since geology often falls under geography.

For the entire lower secondary period, the total time allocation is strictly prescribed for each subject in the SKC and for mathematics in the CZC. The science subjects in the CZC, on the other hand, are allocated common time, providing schools with flexibility in prioritising certain subjects. Nevertheless, there tends to be a typical distribution of lessons across schools.

The educational content of specific educational areas comprises learning outcomes and subject matter. Learning outcomes are designated in CZC as expected outcomes, while SKC defines performance standards. In both documents, learning outcomes set criteria for the level at which students master knowledge, skills, and abilities. Besides the learning outcomes, both curriculum documents determine the volume of the required knowledge and skills. CZC designates it as ‘subject matter’, while SKC labels it as ‘content-based standards’. However, as the Czech ‘subject matter’ is not obligatory, this study does not address this aspect and solely focuses on assessing obligatory learning outcomes.

Research procedure

The first stage of this research was based on the methodology used in our previous research (for more details see Kácovský et al., 2022): Obligatory learning outcomes were extracted from CZC and SKC and analysed using RBT. Conative/operative aspects of the outcomes were analysed within the cognitive process dimension and the content aspects within the knowledge dimension. For Metacognitive knowledge, we felt the need to define this dimension more comprehensively in this study than in the original RBT, using other sources that describe typical features of metacognition (Anderson et al., 2001; Cambridge Assessment International Education, 2019; Flavell, 1979; Lokajíčková, 2014). The most common features of metacognition were outcomes aimed at developing interest beliefs, attitudes, judgements or values, and outcomes using different strategies in various situations (especially regarding working with information), individual experiences, awareness of cultural specificities and interdisciplinary approaches (except for the expected use of essential discrete competence, such as the use of mathematics in chemistry and geography, for example).

The learning outcomes were coded according to the type of knowledge (labelled F for Factual, C for Conceptual, P for Procedural and M for Metacognitive). For Factual, Conceptual and Procedural knowledge, we also investigated the cognitive processes (labelled RE for Remember, UN for Understand, AP for Apply, AN for Analyse, EV for Evaluate, and CR for Create) contained in an outcome. Due to these challenges in determining Metacognitive knowledge, we only decided whether the learning outcome was metacognitive or not, without determining the level of the cognitive process. Table 1 provides a few examples of learning outcomes from CZC and SKC, and their coding.

The codes tracked the entire context of the learning outcomes, not just individual active verbs. Learning outcomes consisting of multiple parts (i.e. usually containing two or more different active verbs) were referred to as complex and encoded according to the following rules:

1. If multiple parts of the complex learning outcome referred to the same type of knowledge, we only encoded the higher cognitive process of knowledge included in the outcome.

2. If the complex learning outcome contained several different types of knowledge, the cognitive process was specified for each of the knowledge separately.

These rules made it possible to assign multiple codes to complex learning outcomes.

In defining complex learning outcomes, we made an exception for mathematics, which included outcomes beginning with the phrases add and subtract or multiply and divide. Although such outcomes contain two different verbs, we did not consider them complex as they merely express two variants of a single cognitive requirement.

To ensure the validity of the research process, the research team was split into groups of three to four coders for each study subject. At first, the coders coded two dimensions of each outcome on their own; further, they entered their results into a shared table, checked mutual matching within the results of their group and corrected their decisions if needed; finally, outcomes with intra-group disagreements were discussed in regular meetings of the research group, that is, among all seven co-authors of this paper.

The second stage of this research followed the inductive approach and aimed at specific groups of requirements in learning outcomes of science subjects and mathematics. During RBT coding, we noticed that some phrases and requirements appeared repeatedly in the learning outcomes, in the same subject and across different subjects in the specific country. Most commonly, such recurring subject-nonspecific phrases referred to knowledge or skills universally applicable in STEM disciplines such as planning experiments, working with graphs or tables, and presenting conclusions of a project, among other. Therefore, we went through all learning outcomes once again in detail and marked those that contained more general scientific skills and concepts. Lastly, we grouped these marked outcomes according to their content and characterised the resulting groups; we present their classification in Results.

Results

Number of obligatory learning outcomes

To address the first research question, we compared the number of obligatory learning outcomes between the two countries under study. However, simply knowing this number may be misleading because some outcomes contain two or exceptionally more active verbs – readers should recall that we have labelled such outcomes as complex and that their proportion among all outcomes is also a relevant parameter in our comparison. Therefore, we outlined both the total number of required outcomes in each subject and the proportion of those that are complex in Table 2.

Table 2. Numbers of obligatory learning outcomes prescribed by national curricula.

	Complex learning outcomes/all learning outcomes (% of complex learning outcomes)
	Czechia (CZC)	Slovakia (SKC)
Biology	10/21 (48%)	6/101 (6%)
Chemistry	11/19 (58%)	2/58 (3%)
Geography	10/25 (40%)	16/153 (10%)
Geology	1/3 (33%)	0/21 (0%)
Mathematics	20/29 (69%)	43/305 (14%)
Physics	4/19 (21%)	20/92 (22%)
In total	56/116 (48%)	87/730 (12%)

The number of Slovak learning outcomes is more than six times higher than that of Czech outcomes (730 vs. 116). This difference is even more noticeable in geography and especially in mathematics. Let us consider an example from the topic of arithmetic operations: CZC contains 7 learning outcomes on arithmetic operations with integer and rational numbers, while SKC includes 97 such learning outcomes, most of which very simple and cognitively not demanding. Another obvious example is geology, with 21 learning outcomes in SKC and 3 in CZC. Most likely fundamental geological topics (Earth’s formation and structure, mineralogy and petrology, endogenous and exogenous geological processes, development of the Earth’s crust, evolution, environment formation and protection) cannot be covered with only the few learning outcomes formulated in CZC.

CZC often contains more outcomes with two or more active verbs. The total ratio of complex outcomes is 48% in CZC, in contrast to 12% in SKC. In reality, this difference is even more significant because some Slovak complex outcomes in geography, mathematics and physics, are repeated. For example, the complex outcome ‘create and present a project that makes creative use of the knowledge gained’ is, with small nuances, required four times by the physics curriculum in each year of study; similarly, in geography, the complex outcome ‘justify the inclusion of one of Africa’s sights on the UNESCO World Heritage List and show it on a map’ is similarly required for each other continent separately, totalling five times.

In Slovakia, within a complex learning outcome, all active verbs often refer to a single teaching activity and express an escalating requirement for a type of knowledge (e.g. ‘record the results of experiments in tables and interpret them’). Conversely, in CZC, different outcomes are commonly combined for both general and specific demands, which likely do not fall within the same teaching episode (e.g. ‘know the periodic table of elements, recognise selected metals and non-metals and estimate their possible properties’).

Remarkably, the typical time allocation of the subjects is, according to our findings, far from being proportional to the number of obligatory learning outcomes in both countries. For example, in CZC, mathematics does not have many more outcomes than geography despite being allocated significantly more time.

Cognitive processes and types of knowledge

To address our second research question, we determined the share of each learning outcome that require a given type of knowledge (Figure 1) and a given cognitive process (Figure 2) in each subject. In both cases, the sum of the shares exceeded 100% because complex outcomes often combine multiple types of knowledge and/or involve multiple cognitive processes. Our statistical analysis did not distinguish whether the same cognitive process was required once or multiple times within a single complex outcome – in both cases, such an outcome was counted just once. For example, a complex outcome requiring both Conceptual and Procedural knowledge at the Apply level was counted only once in terms of cognitive processes, under the category Apply. However, the numbers of learning outcomes significantly differed between the two documents of interest. Therefore, the percentages alone are not sufficiently indicative of the nature of the national curricula.

Figure 1. Distribution of knowledge types (LOs = learning outcomes).

Figure 2. Distribution of cognitive processes (LOs = learning outcomes); the cognitive processes that were not included in any LO were omitted in the graph.

Because CZC is brief, a single outcome in physics and chemistry, for example, accounts for more than 5% of all outcomes in these subjects. For this reason, the graphs in Figures 1 and 2 include not only the share of learning outcomes that require a given knowledge/process (always on the y-axis) but also their absolute numbers (always on the x-axis). We did not determine the level of the cognitive process for metacognitive learning outcomes, so these outcomes were not included in the statistics shown in Figure 2. For geology, we only show data from Slovakia because CZC contains only three obligatory outcomes in this area, precluding any quantitative analysis.

Figure 1 shows that in both countries, Conceptual knowledge is by far the most frequently included type of knowledge in science subjects, typically followed by Procedural knowledge. The exception is found in the Slovak physics curriculum, which is predominantly focused on Procedural knowledge, unlike the Czech counterpart; across subjects, this is the most remarkable difference between Czech and Slovak science curricula. In mathematics, Procedural knowledge is required by more than 80% of learning outcomes, while Factual and Metacognitive knowledge is the type of knowledge least often required by curriculum documents across subjects. Factual knowledge occurs rather sporadically, in less than 15% of learning outcomes in all subjects, except for chemistry, where this type of knowledge is represented more significantly in both countries. Metacognitive knowledge is the most frequently mentioned type of knowledge in only one subject, namely biology, and virtually absents from geography, mathematics and physics.

As shown in Figure 2, the prevalence of cognitive processes varies considerably. Unlike in the types of knowledge, no clear trend in cognitive processes is found across all science subjects. Some similarity can be traced in Czech and Slovak mathematics and physics curricula, wherein the Apply process prevails, followed by Understand and Analyse. Furthermore, the chemistry curricula of both countries are similar – most learning outcomes require simple memorisation (Remember), with Understand and Apply behind as the next most frequently required processes. In geography, the order of the most frequently prescribed cognitive processes in Czechia is virtually reversed from that in Slovakia. Another difference is observed in biology – Create, the highest level process of RBT, is the most frequently required in SKC but does not appear among Czech outcomes at all.

Requirements included in learning outcomes across different subjects

In the following section, we describe the results of the inductive content analysis used to address the third research question. Since CZC is very brief and rarely includes cross-curricular requirements, the resulting structure of the categories is almost exclusively based on SKC. Most learning outcomes marked with cross-curricular requirements were classified into one of the following two categories: (A) outcomes referring to observing, experimenting, modelling, and working on projects, and (B) outcomes requiring working with information. Since group (A) involved a higher number of outcomes, we defined its sub-categories associated with chronological phases of scientific inquiry, namely (A1) preparation, (A2) execution, (A3) evaluation and (A4) presentation. In group (B), we differentiated (B1) work with graphs, tables, schemes, and data resources from (B2) work with maps which is specific for geography. For all (sub)categories, Figure 3 shows examples of typical actions required, and Table 3 outlines the frequency of their occurrence in both CZC and SKC.

Figure 3. Categories and subcategories identified within the inductive content analysis.

Table 3. The frequency of cross-curricular requirements in CZC and SKC.

When we added up all cross-curricular requirements in category (A), we found that their relative frequency was not markedly different between CZC and SKC (except using maps in geography, which is more common in SKC); however, given the brevity of CZC, the difference in absolute frequency is considerable.

In SKC, cross-curricular requirements from group (A) are relatively more frequent in biology, geology, chemistry and especially in physics. Biology and physics are also the only subjects where the presentation of results and experimental findings is directly addressed in the expected outcomes. The requirements from category (B1) appear most often in biology, geology, mathematics, and physics. SKC also describes more specifically the required outcome of the learner’s work with information (graph, table, sketch, concept map, among others). In CZC, only a few requirements are considered, so comparing their occurrence across subjects would be nonsensical. More importantly, the teacher or the author of a school curriculum document may assume that such isolated outcomes appear in the curriculum accidentally rather than intentionally.

In addition to the cross-curricular requirements included in categories (A) and (B), we identified other cross-curricular features such as: first aid learning, workplace safety and health, outdoor safety, and learning in or about one’s surroundings – i.e. in real-life settings, close to home or school. However, there are too few learning outcomes covering these topics to be given a specific (sub)category.

Discussion

Extent and cognitive demands of the curricula

In answering the first research question, our study showed that SKC is much more extensive than CZC and contains a higher number of learning outcomes. The Czech national framework document is brief, clearly assuming that the derived school-level documents will be considerably longer and more detailed, according to the needs of each school. In contrast, the Slovak national framework document has high number of obligatory learning outcomes and does not need to be expanded at the school level.

Previous studies have suggested that the number of obligatory learning outcomes in the curriculum does matter and that finding a balance between school autonomy and prescribed curriculum remains a dilemma (Vrhel, 2021). Some researchers focus on providing schools, teachers and learners with substantial educational options and flexibility (van den Akker, 2006), thus preferring less strict national curriculum. According to Nieveen and Kuiper (2012), teachers tend not to prefer overly prescriptive frameworks because such rigid curricula discourage them from their professional activity. However, novice and less experienced teachers often need more detailed intended curricula as they look for guidance for their teaching (Lee et al., 2015). Additionally, national frameworks are crucial for creating a school curriculum document, and endowing schools with too much autonomy in this regard may widen the quality gap between them.

Considering the above, there is no clear consensus on the appropriate length and specific learning outcomes of national curricula. CZC and SKC exemplify two opposite approaches to the level of detail of a national framework document. On the one hand, SKC (especially in mathematics) contains even trivial or overly specific learning outcomes (for instance, ‘find a positive integer on the number line’, ‘find out whether a given number is divisible by 2, 3, 4, 5, 6, 9, 10, 100, according to the instructions provided’) which are implicitly covered by other outcomes and as such could be reduced. On the other hand, CZC displays excessive brevity, which has been even deepened after the recent curriculum revision of 2021.

In contrast to the CZC brevity, Czech school-level documents are traditionally quite detailed. As a result, usually only a small fraction of a particular school-level document is sufficient to meet the requirements of a minimalist CZC which raises the question of what justifies the rest of the school level document. What we have been observing in Czechia is that school documents often align with established textbooks or traditional practices (‘this and that have always been taught’). Thus, textbook authors and ‘educational habits’ may have potential to influence the development of school-level documents rather than current government’s educational objectives.

Czechia is currently facing a shortage of qualified STEM teachers, whose work often relies on teachers without a proper disciplinary background. When these teachers are required to design a school curriculum document (or to contribute to its development/revision), they do not receive effective support from the national curriculum. If the national framework lacks some key learning outcomes or methods (e.g. projects and presentations, among others), teachers may feel unmotivated to include these aspects in either school-level documents or their lessons.

The second research question referred to the cognitive demands of the intended curricula, which were measured by applying RBT on obligatory learning outcomes. RBT is rarely applied to both science subjects and mathematics together but often used to assess individual subjects. For instance, both our former (Kácovský et al., 2022) and present research results corroborate the findings of a recent Turkish study (Zorluoglu et al., 2021) showing that most learning outcomes in secondary school chemistry curricula focus on the Understand level in the Conceptual cognitive domain. According to Momsen et al. (2010), biology courses, in particular, are overloaded with memorisation, as found in CZC, which is closely associated with lower-order cognitive skills (Momsen et al., 2013). Even calculus-based physics sequences use lower-order skills (Apply), which are mostly represented by problem-solving tasks (Momsen et al., 2013). One of the few exceptions is the Chinese chemistry curriculum to which further intellectual demands (higher-order cognitive processes) have been added during curriculum reforms (Wei, 2020). Similar efforts are patent in SKC biology, where the highest cognitive process Create dominates the learning outcomes.

However, from a broader perspective, lower-level cognitive processes and lower levels of required knowledge still prevail in both countries. Such a conservative approach may be understandable at the primary school level but is questionable in secondary education, especially with older pupils; nevertheless, such a progression is not observed in either country. Yet, according to research by Kumpas-Lenk et al. (2018), students are more motivated and engaged when given tasks with high levels of cognitive demand.

Many recommendations also emphasise that metacognitive skills should be developed via learning outcomes (Braund, 2017; Hartman, 2001). Our findings show that metacognition is required rather sporadically, with a relatively higher representation only in biology in the context of human health topics and everyday life aspects (botany, zoology, and ecology, among other fields of study). However, a higher incidence of metacognition was expected also in other fields of natural sciences.

Lastly, we identified marked differences in the typical structure of learning outcomes, their complexity and in the active verbs between the two documents, all of which strongly depending on the specific subject. Presumably, different groups of experts formulated the requirements in each subject, which leads us to question the extent to which these teams collaborated in designing their national curriculum.

Inductively identified cross-curricular requirements

To answer the third research question, we performed a qualitative analysis, which identified two significant groups of cross-curricular requirements. The first group labelled (A) involves outcomes related to scientific inquiry and was subdivided into four subgroups (A1)–(A4) (preparation, execution, evaluation, presentation). The outcomes of SKC were crucial for defining these categories given the brevity of the CZC discussed above. Although these four categories arose inductively, they represent activities previously included in frameworks. Over time, these frameworks (particularly those focused on hands-on practical work in science) have been renamed as processes of science, scientific process skills, habits of mind, scientific inquiry abilities or even scientific method (Harlen, 1999; Millar, 1993; Padilla, 1990); however, these names usually referred to similar lists of fundamentally transferable abilities mimicking the work of scientists. Henceforth, we exclusively use the term science process skills (SPS) which is likely the most commonly used term, but we looked for these skills in mathematics as well.

Among typical SPS, Harlen (1999) lists ‘identifying investigable questions, designing investigations, obtaining evidence, interpreting evidence in terms of the question addressed in the inquiry, and communicating the investigation process’ (p. 129). Some authors (e.g. Brotherton & Preece, 1995; Padilla, 1990; Turiman et al., 2012) offer a two-level model that distinguishes basic SPS (observing, inferring, measuring, communicating, classifying, and predicting) from integrated SPS (controlling variables, defining operationally, formulating hypotheses, interpreting data, experimenting, and formulating models). The emphasis on SPS is a main feature of so called process science, which prioritises the development of students’ skills over content knowledge, which, however, has both proponents and opponents. For instance, Harlen (1999) stated that ‘the development of SPS has to be a major goal of science education’ (p. 130), and Turiman et al. (2012) added that SPS ‘should be utilised by teachers in the delivery of teaching the facts of science effectively’ (p. 114), but other authors have been more critical. According to them, the process approach underestimates the importance of ideas and theories (Millar, 1993), and student performance in some of the aforementioned skills has proved to be strongly context-dependent (Lock, 1990; Millar, 2010). Furthermore, as indicated by authors addressing the scientific method, the diverse work of scientists cannot be reduced to a simple list of discrete subsequent processes and rules (Cowles, 2020; Staddon, 2018).

As for incorporating elements of the SPS framework directly into curriculum documents, we are aware of the arguments of the proponents of the opposing views. Currently, scientific research does indeed differ from what the SPS framework simplifies and is certainly not limited to empirically, inductively acquired knowledge; hence, learning outcomes should not be primarily based on the spirit of science process skills. Nevertheless, SPS turns the attention to activities that should be developed in science subjects (and even mathematics), such as formulating hypotheses, predicting, collecting, and analysing data, and interpreting results, among others. After all, Germann and Aram (1996) showed that developing activities such as data analysing, drawing conclusions and providing evidence requires metacognitive skills, whose underdevelopment we highlighted above in this discussion.

For teachers to adopt these cross-curricular requirements in their teaching, complementary to a content-oriented approach, these skills must be explicitly stated in the intended curriculum. Both curricula analysed in this study focus on content (content approach, see Millar (2015)), while the scientific processes play a minor role (nearly disregarded in CZC). Conversely, SKC, and especially the physics and biology sections, contain learning outcomes that express the spirit of SPS. For instance, the physics curriculum includes several outcomes that explicitly mention the problem solving sequence formulating problem – formulating hypotheses – carrying out experiments and measurements – processing, assessment, and interpretation of results of experiments and measurements. Including these tasks in framework documents could be a way to permeate the current strongly content-oriented curriculum with process-oriented learning outcomes to balance the process- and content-oriented approaches.

The second group of cross-curricular requirements labelled (B) is related to work with information. This group also arose from SKC learning outcomes. Understanding, finding, evaluating, or using the wide variety of information to which we are increasingly exposed has become one of the essential elements in our personal lives (Nisha & Varghese, 2021). This set of abilities defines information literacy, which is common to all disciplines, learning environments and levels of education (Association of College and Research Libraries, 2000). In line with Nisha and Varghese (2021), who performed a meta-analysis of studies conducted on information literacy in higher education, this literacy training should be integrated into curricula, in our opinion even at lower secondary level. Current references on working with tables, graphs, schemes, literature, or data resources in both curriculum documents under study provide a good basis for this fostering information literacy. However, with the transition from the information age to the digital age, information literacy is evolving to include how to access information in digital formats (Welsh & Wright, 2010). In this regard, both CZC and SKC lack explicit mentions to learning outcomes on using digital tools to develop information literacy.

A specific way of working with information commonly found in both documents is to use maps in geography teaching. According to Marada et al. (2017), map skills are needed to acquire and improve spatial competences as a foundation of geographical thinking. However, simply mentioning map skills and including map use in geography instruction in curricular documents does not ensure the integration of map skills into geography teaching. Using maps can be an educational goal of geography, but it can also be reduced to a way of teaching geography. As such, using maps could represent different levels of Bloom’s taxonomy, ranging from simple reading and memorising facts from a map at one end of the spectrum to developing new maps for specific issues or processes at the other end (Kerski, 2020). Specifying these levels more clearly in national curricula should be a new challenge for school geography, especially whilst making revisions towards strengthening digital competencies.

Limitations

The limitations of this study derive from the nature of the curriculum documents themselves, from nuances in the structure of the corresponding school systems, and from the research methodology.

Firstly, this research focused on only a specific section of educational documents (obligatory learning outcomes), which is structured in both documents similarly and is therefore suitable for comparison. The study did not address other sections of the curriculum documents under analysis, such as introductory or general sections, which can provide valuable information about the overall ideological background and directions of the curricula. Furthermore, no analysis of intended curricula can provide a perspective of the implemented curriculum, on a real educational process.

Secondly, the Czech and Slovak educational systems differ in the number of years labelled as ISCED level 2, which must be considered when quantitatively evaluating the results.

Thirdly, we faced some methodological issues. When assessing learning outcomes using revised Bloom’s taxonomy, we struggled to reach a consensus among coders in a few instances, which may explain why the categorisation of these learning outcomes ultimately may not be one hundred percent clear. Furthermore, the system of categories and subcategories that arouse from the inductive content analysis was based on the compared documents, and especially on the Slovak curriculum; for other documents, such a system will necessarily look differently. However, we believe that the suggested framework (especially the structure of category (A)) could also help other researchers, who may further improve this framework.

Conclusions and implications

This study compared intended curricula of science and mathematics education in Czechia and Slovakia at ISCED level 2. The subject of this research was the obligatory learning outcomes prescribed by national curriculum documents. These learning outcomes were analysed in two ways – on the one hand, by deductive analysis, for identifying their level of detail and cognitive demands and, on the other hand, using an inductive content analysis for retrieving cross-curricular requirements highlighted across subjects.

Regarding the extent and detail of the learning outcomes, the two national documents are fundamentally different. CZC contains a very low number of learning outcomes that often contain several active verbs (i.e. several actions), while SKC prescribes a high number of simple learning outcomes. In contrast to SKC, the brevity of CZC provides teachers with a high degree of autonomy. However, based on our experience, it also means that it offers limited guidance on how to build a school-level document and on what to do in the classroom. This aspect may be particularly problematic for novice and unqualified teachers. At the national level, we suggest striking a compromise between the Czech and the Slovak approaches, considering the long-term educational trends specific to each country, such as the increasing lack of qualified teachers in Czechia, which calls for a more detailed and structured curriculum.

In both countries, the link between the number of learning outcomes in a particular subject and the typical time devoted to the subject remains weak; whether the curriculum is brief or detailed, these two variables should be approximately proportional.

Our results show that both SKC and CZC predominantly emphasise lower-level cognitive processes (Remember, Understand, and Apply) and rarely develop Metacognitive knowledge. For this reason, these documents should more frequently mention high-level cognitive processes and metacognition, which are appropriate guidelines for innovating framework documents of the twenty-first century; in the discussion, we presented arguments supporting this approach. To be fair, in SKC, the physics and biology curricula are designed in a cognitively more ambitious way and can serve as an inspiration in following the recommendation given in this paragraph.

An inductive content analysis uncovered two categories of learning outcomes that express cross-curricular requirements – outcomes related to scientific inquiry and outcomes requiring work with information. However, these learning outcomes were underrepresented in SKC and effectively absent from CZC although the skills that they develop are highly required currently. In the context of scientific inquiry, we propose balancing the content approach and the currently overlooked process approach. Efforts to achieve this balance are found in physics, in SKC, where specific science process skills repeatedly occur across particular physics topics, which is inspiring for other subjects. Science process skills should also be complemented with content-specific teaching ideas and projects, which the teachers may select for their lesson plans.

Overall, national curriculum designers must act together in a coordinated way and across subjects by sharing the aims and philosophy emphasised in the educational policy. Such coordination will prevent learning outcomes from significantly differing in terms of cognitive demands, complexity, and cross-curricular ambitions across subjects in each country. Across subjects, the learning outcomes should send an understandable message about the country’s educational priorities, which could be particularly appreciated by teachers who teach more than one subject. The requirements (cognitive, cross-curricular and others) expressed by learning outcomes should also be repeated in subsequent years with increasing demands as pupils become more advanced. For instance, pupils could first present the results of a project in front of the class and prepare a poster/presentation in the next year.

Our research highlights striking differences between two very close countries – Czechia and Slovakia – which share decades of recent history in a common state. Yet, in some educational respects, they have already taken different paths. Despite its local focus at first glance, we believe that our study will be beneficial for authors from other countries especially by suggesting an inductively derived framework for cross-curricular requirements in science subjects and mathematics. However, further future implementation is needed to assess the usefulness of this framework in curriculum-related studies. Furthermore, the graphical representation of our results (simultaneously depicting both the extent and the cognitive demands of learning outcomes) is widely usable in and transferable to other contexts.

Ethics statement

Presented research meets all the ethics requirements of Charles University at the time the data were collected. The research involves no human participants.

Acknowledgements

We would like to thank our dear colleagues Marián Kireš (Pavol Jozef Šafárik University in Košice) and Neal Martin for their help and valuable consultations. The authors also thank Carlos V. Melo for editing the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work has been supported by Charles University Research Centre No. UNCE/HUM/024 and Charles University Research Program Cooperatio SOC/SSED.

Notes

1 Henceforth in the text, the word “pupil” refers to children attending primary school and lower secondary school, while the word “student” is used in the context of upper secondary schools or universities and in a more universal sense where the level of education is not entirely clear or the statement refers to education in general.