Abstract
Learners’ independence and self-regulated learning (SRL) may be achieved by online teaching, promoting learners’ 21st century skills. Transitioning into online learning and teaching requires a variety of teachers’ knowledge types and competencies. Mapping these knowledge types with a dedicated tool can help improve teachers’ professional development processes to enhance the SRL of teachers and subsequently students. We aimed at investigating the types of knowledge chemistry teachers expressed in online assignments they had designed, which included guidelines to foster their students’ SRL, and reviewing their reflections upon implementing these assignments. The participants were 20 high school chemistry teachers, who took part in two professional development programs focusing on fostering students’ SRL skills in online assignments, the teachers had designed. Research tools included teachers’ online assignments and teachers’ written reflections. We analyzed the assignments using a special rubric, which consists of three types of teachers’ knowledge: technological pedagogical and content knowledge (TPACK), assessment knowledge (AK), and SRL. Findings show that the analysis rubric of online assignments, designed by teachers, enabled identifying various teacher knowledge types and their levels. The use of a validated rubric for mapping and assessing teachers’ knowledge types is a methodical contribution to research on chemistry teachers’ professional development.
1 Introduction
Online learning and teaching and the promotion of self-regulated learning (SRL) have become key factors in modern education, with goals co-aligned with learners’ 21st century skills. In parallel, teaching and assessment methods have been growing further apart from traditional ones (Peretz et al., 2023). Understanding teachers’ knowledge types needed for designing student online assignments while implementing them into their teaching practices, is a key to a successful transformation of online teaching and learning processes into self-regulated learning (SRL), enhancing learners’ independence (Dori et al., 2021).
1.1 Online learning and self-regulated learning
Traditional learning and teaching happen face-to-face in a classroom with students and their teacher. The transition into online learning occurred during the last decades along with the expanding use of the internet (Barak et al., 2016; Wei et al., 2022). Online learning passes the responsibility of learning regulation to the learners (Barnard-Brak et al., 2010; Hodges et al., 2020; Lynch & Dembo, 2004), but the integration of online assessment into online learning has become a challenge for teachers (Amasha et al., 2018; Eichler & Peeples, 2013). Yet, this challenge is accompanied by advantages, primarily the ability to provide personal feedback for the learners, which allows them to monitor their progress in learning (Dietrich et al., 2021; Dipietro, 2010; Vonderwell et al., 2007).
Self-regulation is needed for performing online assessment, which is a key aspect of students’ and teachers’ self-regulated learning (SRL). Teachers are required to plan and support such processes for themselves, as well as for their students (Kramarski & Heaysman, 2021; Kramarski & Michalsky, 2010). SRL is an active personal and constructivist process, in which the learner acquires learning skills, sets personal goals, chooses learning strategies, and monitors the efficiency of the paths taken (Zimmerman & Moylan, 2009). In this process, the learner participates in behavioral, metacognitive, and motivational ways.
Fostering of students’ SRL skills by teachers has been reviewed in the literature, relating to both explicit and implicit approaches (Cohen et al., 2021; de Bruijn-Smolders et al., 2016; Dignath & Büttner, 2008, 2018). The SRL skills in our study include time planning and management, goal setting, help-seeking, and reflection. The transformation of teachers from traditional teacher-centered, face-to-face practices into SRL mentors who promote their students’ SRL skills encompasses several domains of teachers’ knowledge types, discussed next.
1.2 Teachers’ knowledge types
In the process of professional development, teachers are expected to acquire large amounts of diverse knowledge. Shulman (1986) addressed different types of knowledge, primarily content knowledge (CK), pedagogical knowledge (PK), and pedagogical content knowledge (PCK). PCK was defined as an amalgam of content and pedagogy into a specific understanding of how domains and problems are organized, represented, and adapted to learners’ interests and capabilities and how they are aligned with teaching and learning (Shulman, 1987). Assessment knowledge (AK) has been proposed as a meaningful and unique type of teachers’ knowledge (Avargil et al., 2012; Dori & Avargil, 2015; Magnusson et al., 1999; Mertler, 2009; Tal et al., 2021). Studying the implementation of a new chemistry curriculum, researchers (Avargil et al., 2012; Mertler, 2009) found that AK is expressed in the teachers’ ability to develop high-level student-centered assignments. The expansion of technological knowledge, teachers are currently required to acquire, is expressed in their technological pedagogical and content knowledge (TPACK). TPACK was defined as the ability to include technology in a way best suited for teaching the subject matter at hand (Koehler et al., 2013; Voogt et al., 2013).
Methods for assessing teachers’ knowledge types in the literature can be divided into several approaches. The common approach is questionnaires that rely on teachers’ self-report and answers to close-ended questions regarding practice and perceptions (Willermark, 2018). Another approach is based on “paper and pencil” tests, which are tailored for a specific subject matter and usually focus on CK or PCK only (Gess-Newsome et al., 2019; Großschedl et al., 2019). A different approach is based on practice research and analysis of different representations of teachers’ practices, such as lesson videos, lesson reports, and student products (Canbazoglu et al., 2016; Harris et al., 2010). The advantage of the latter approach is that the teachers’ knowledge is attributed to their actual actions and performance rather than to their declarations.
Following this approach, analyzing teachers’ self-designed online assignments (Peretz et al., 2023) can reflect the depth and width of teachers’ knowledge in a broad range of skills and expose their perceptions. The underlying assumption here is that an assignment developed by the teacher can reveal their pedagogical, technological, and assessment competencies, as well as their perceptions, including self-efficacy and pedagogical considerations. Previous implementations of assignment analysis included probing TPACK of teachers (Koh, 2013; Oster-Levinz & Klieger, 2010) and probing “Assessment literacy” (Koh et al., 2018; Mertler, 2009), which is similar to AK.
TPACK is somewhat controversial, as it is described based on two approaches. The first one views TPACK as being comprised of three basic constructs: CK, PK, and TK. When these overlap, they give rise to PCK, TPK, and TCK, which, in turn, overlap to yield TPACK. Alternatively, TPACK is a construct (Brantley-Dias & Ertmer, 2013) that is integrative and transformative (Canbazoglu et al., 2016; Schmid et al.,2020). Rather than an accumulation of the three basic constructs (CK, PK, and TK) TPACK develops through teachers processing technological support and applying it into their PCK. Koehler et al. (2012) reviewed different TPACK measurement approaches to probe teachers’ beliefs, knowledge, skills, and self-efficacy. The current research assumes that teachers (especially experienced ones) come with an established PCK for their teaching, along with TK from their daily experience, upon which they develop their TPACK. Accordingly, we define TPACK as the average of their PCK and TK, in line with the transformative approach of Koehler et al. (2013).
A rubric for probing TPACK and AK (Herscovitz et al., 2023) served as a basis for this research and was expanded to assess the fostering of SRL skills as well. The goal of this research was to investigate chemistry teachers’ professional development (PD), focusing on the various levels of teachers’ knowledge types. To this end, we have analyzed the online assignments the teachers had designed, while they participated in a PD program and their reflections on the process they had gone through.
2 Methods
2.1 Research goal and questions
The research aimed at investigating the types of knowledge chemistry teachers leveraged in online assignments they had designed, which included guidelines to promote SRL. Since reflection is an important part of SRL, we also investigated the teachers’ reflections on developing and implementing the assignments in their classes. The research questions were: (1) What are the differences between various teachers’ knowledge types as reflected in the online assignments they had designed? (2) What are the characteristics of the teachers’ reflections on developing the online assignments to promote their students’SRL and the teachers’ declared intentions to integrate them into their classes?
2.2 Research participants and setting
The 20 high school chemistry teachers who participated in the research took part in two PD programs focused on fostering SRL. The chemistry teachers shared a common interest in broadening their knowledge and skills of teaching in online environments, assessment, and SRL. With this homogeneous content knowledge background, these teachers served also as a focus group. The research included two professional development (PD) programs: (1) a 10-h intervention as part of a professional learning community for leading chemistry teachers, and (2) a 30-h summer program. Both programs focused on five aspects:
Exposing the teachers to the significance of SRL and introducing the SRL cycle and phases: forethought, action, and reflection,
Experiencing SRL, i.e., teachers as self-regulated learners,
Presenting video clips as introductions[1] to assist students in acquiring SRL skills,
Designing new online assignments or including SRL-promoting guidelines in assignments that teachers had previously used, and
Reflecting on the process teachers went through during the program, which included discussing and planning the class implementation of the SRL assignments, as well as teaching that promotes students’ SRL.
The 20 teachers varied in their experience: about half were leading chemistry teachers, and 40 % had less than 10 years of experience. As the term “experience” is sometimes referred to as “seniority” (Feldman, 1983), we use experience as a proxy to seniority when the experience spans at least a decade.
2.3 Research tools and data analysis
The research tools included online assignments or assignments with an online component that the teachers had developed, as well as teachers’ reflections. After the teachers had adapted the online assignments for SRL by including SRL fostering guidelines, the assignments were collected and analyzed. Analysis of the assignments was performed using a designated rubric, based on previous research (Herscovitz et al., 2023), referring to PCK, TK, and AK types of teachers’ knowledge. The TPACK score was calculated as the average of PCK, and TK component scores as expressed in the online assignment. In the rubric, we extract PCK from the online assignments developed by the chemistry teachers. The TK criteria relate to the technological attributes that promote PCK. Thus, both dimensions of the rubric probe TPACK as the overlap of PCK and TK, in line with Koehler et al. (2013). Averaging the two components normalizes the score, so it is on par with the AK and SRL scores. AK, especially that related to online assignments, was included in the analysis due to its importance. A unique type of teachers’ knowledge in this research was their ability to promote SRL (labeled SRL knowledge). Table 1 shows the categories and criteria of the rubric. The maximal rubric score for an assignment is 30 points, referring to an online assignment optimally suited for SRL. The teachers’ knowledge type and a specific criterion, chosen as an example from the rubric in Table 1, are presented next to an excerpt or a description found in the assignment, along with the reasoning for the ranking of that criterion. Some of the criteria can be demonstrated by the inclusion of a specific guideline or feature in the assignment. Other criteria may only be analyzed by a holistic view of the assignment, preventing the ability to match a criterion to an excerpt from the assignment.
Rubric for analysis of online assignments: knowledge types, criteria.
| Knowledge type | Criteria | Maximal score | |
|---|---|---|---|
| TPACK | PCK | Conceptual understanding | 3 |
| Higher order thinking levels | 3 | ||
| Catering to student levels | 3 | ||
| Values & relevance | 1 | ||
| Total PCK* | 10 | ||
| TK | Online assignment design | 3 | |
| Variety of digital tools | 3 | ||
| SAMR modela level | 3 | ||
| SAMR – R level | 1 | ||
| Total TK | 10 | ||
| TPACK = average (PCK & TK) | 10 | ||
| AK | Self-assessment | 3 | |
| Assessment for learning | 3 | ||
| Assessment level of online assignment | 3 | ||
| Peer assessment | 1 | ||
| Total AK | 10 | ||
| SRL teacher’s ability to promote SRL skills | Time planning | 3 | |
| Learning strategies | 3 | ||
| Reflection | 3 | ||
| Additional SRL aspects | 1 | ||
| Total SRL | 10 | ||
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aSAMR, substitution, augmentation, modification; Redefinition – the level of technology implementation (Blundell et al., 2022; Puentedura, 2013).
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*Bold values mark the dimensions of the rubric – the knowledge types, and the total scores for each knowledge type.
Figure 1 presents a few snapshots and the analysis of specific attributes (criterion, score, and reasoning) from the assignment that is referred to in Table 2. Examples of the analysis method are shown in Table 2. A complete assignment is presented in Figure 2 (see Findings section) with ranking for each criterion of the rubric and its reasoning in Table 3.

“It’s raining moles” – a google-form based assignment (participant 2010122) – a partial example of the analysis.
Examples of the analysis method for the assignment “It’s raining moles” – a google-form based assignment (participant 2010122).
| Knowledge type & criterion | Score | Description or excerpt | Reasoning |
|---|---|---|---|
| PCK Higher order thinking levels |
2/3 | The assignment features different visual modes and multiple-choice questions, requiring no explanations. | Diverse representation modes exist, but higher-order thinking levels lack – some are missing. |
| PCK Catering to student levels |
3/3 | The assignment includes watching the clip “Africa” concerning rain, constructing an online jigsaw puzzle to discover the next assignment, using the structural formula to choose the right functional group in the Geosmin molecule. | The assignment parts are diverse, so students at all levels are motivated to respond. |
| TK Assignment design |
3/3 | The assignment was prepared on a colorfully designed google-form with linear progression, where one part leads to the next. | The form is visually attractive and easy to follow. |
| SRL Reflection |
1/3 | “How did I feel during the online assessment?” “Where the directions clear?” “Was the assignment too long?” |
A basic, descriptive level of reflection appears at the end of the assignment. |
| AK Self-assessment |
2/3 | “Once you solve the riddle, take up to 3 min to find the answer online. Feedback: The answer is one word: a name of a substance.” |
There are certain points along the assignment where the student is expected to respond and get feedback automatically. However, the feedback does not foster meaningful learning. |

The virtual “Chemical Escape Room” assignment (participant 2020122).
Detailed analysis of the “Virtual Escape Room” assignment using the rubric.
| Knowledge type | Criteria | Score | Reasoning | |
|---|---|---|---|---|
| TPACK | PCK | Conceptual understanding | 2/3 | The accuracy and depth of concepts can’t be guaranteed because the content of the product is the students’ responsibility, with directions to rely on matriculation test questions. |
| Higher order thinking levels | 2/3 | In the final product, there is no teachers’ control over the thinking levels, however, matriculation test questions ensure a variety of levels. | ||
| Catering to student levels | 3/3 | Directions are clear & support the students to complete the assignment. | ||
| Values & relevance | 1/1 | The “escape room” format appeals to students’ interests. | ||
| Total PCK | 8/10 | |||
| TK | Online assignment design | 2/3 | Appearance is poor, but the order and the web links are good design aspects. | |
| Variety of digital tools | 3/3 | There are directions for the proper digital tools needed to complete the assignment. | ||
| SAMR model level | 2/3 | According to the directions, there isn’t a significant difference between designing the escape room or a quiz – Augmentation level. | ||
| SAMR – R level | 0/1 | |||
| Total TK | 7/10 | |||
| TPACK = ½ (PCK + TK) 7.5/10 | ||||
| AK | Self-assessment | 2/3 | Attaching the rubric and directing the students to use it – allow self-assessment, but without specific feedback. | |
| Assessment for learning | 2/3 | Feedback in many aspects is possible, but there is no collection of assessment data | ||
| Assessment level of online assignment | 2/3 | A multi-dimensional rubric with a definition of performance levels exists, however, no criteria are set to define the domains of the rubric. | ||
| Peer assessment | 1/1 | Use of peer assessment | ||
| Total AK | 7/10 | |||
| SRL | Time planning | 2/3 | Time planning is referred to, but without the possibility of feedback from the teacher. | |
| Learning strategies | 3/3 | A variety of learning strategies are referred to | ||
| Reflection | 2/3 | Referral to reflection through directions & peer assessment, but without direction for future insights | ||
| Additional SRL aspects | 0/1 | None | ||
| Total SRL | 7/10 | |||
| Assignment | Total score | 21.5 | ||
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Bold values mark the dimensions of the rubric – the knowledge types, and the total scores for each knowledge type.
The participants were asked to conclude their learning process by reflecting on three aspects: (1) the strengths or advantages of implementing SRL-promoting assignments that they had developed, (2) the challenges or difficulties they anticipate in implementing the SRL-adapted assignment in their classroom, and (3) whether they intend to implement the assignments in their classrooms.
2.4 Ethics
This research was reviewed and approved by the Behavioral Sciences Research Ethics Committee of the Technion – Israel Institute of Technology (approval number 2020-092).
3 Findings
The main findings will be discussed in response to the two research questions.
3.1 Teachers’ knowledge types as demonstrated in their self-designed online assignments
Following is a detailed example of the analysis of the assignment The virtual “Chemical Escape Room” of participant 2020122, inspired by the special EREA room designed by Avargil and colleagues (2021). The translated assignment is presented in Figure 2. Table 3 presents the rubric, including the knowledge type domains, criteria, scores, and the reasoning behind them. The total scores of the assignment for each knowledge type are: PCK is 8, TK is 7, and the average of these two – TPACK is 7.5. AK and SRL scores are 7 for each one of them.
The analysis, presented in Table 3, demonstrates that the assignment designed by participant 2020122, complies with most of the criteria in the rubric at a high level. Thus, testifying of high teachers’ knowledge levels in all the examined domains.
Table 4 presents the results of the analysis of 20 assignments. The level of knowledge type: PCK, TK, AK, and SRL were extracted using the rubric presented in Table 1, and the TPACK was calculated as the average of the PCK and TK scores.
Assignments’ knowledge type analysis of all the participants.
| Participant | Assignment description | Knowledge type | ||
|---|---|---|---|---|
| PCK, TK => TPACKa | AK | SRL | ||
| 2020122 | Designing a virtual “Chemical Escape Room” – bonding & structure | 8, 7 => 7.5 | 7 | 7 |
| 2010122 | Online google form assignment – moles, bonding & structure | 8, 9 => 8.5 | 3 | 2 |
| 2030122 | Online exercise – ionic compounds | 6, 5 => 5.5 | 6 | 6 |
| 2040122 | Online assay question – “Biodiesel” | 10, 9 => 9.5 | 4 | 3 |
| 2060122 | Online assignment – chemical equilibrium, entropy | 9, 9 => 9 | 6 | 5 |
| 2070122 | Online google form assignment – Lewis structures, molecular shapes | 7, 5 => 6 | 6 | 6 |
| 2080122 | Online examination | 9, 5 => 7 | 5 | 3 |
| 2090122 | Online quiz – covalent bonding | 8, 6 => 7 | 4 | 1 |
| 2110122 | Online google form assignment – energy transfer | 7, 5 => 6 | 3 | 3 |
| 6060722 | Online google form assignment – states of matter, the particles model, and temperature scales | 8, 7 => 7.5 | 2 | 5 |
| 6050722 | Guidelines for self – online learning – introduction to entropy | 6, 7 => 6.5 | 4 | 5 |
| 6120722 | “Nearpod” based online assignment – “What is volume?” | 8, 7 => 7.5 | 5 | 6 |
| 6080722 | “Genially” based assignment – reviewing states of matter | 8, 8 => 8 | 4 | 9 |
| 6020722 | Self-learning unit – neutralization reactions | 10, 6 => 8 | 4 | 7 |
| 6010722 | Online google form assignment – states of matter | 9, 4 => 6.5 | 4 | 6 |
| 6090722 | “Google Classroom” based assignment – inquiring mixtures separation | 10, 8 => 9 | 4 | 5 |
| 6040722 | Exercise assignment – radioactivity | 5, 3 => 4 | 4 | 6 |
| 6100722 | “Lergo” based online assignment – chemical rates | 8, 8 => 8 | 6 | 8 |
| 6070722 | “Molview” and fat composition calculator based online exercises – fats and fatty acids | 9, 8 => 8.5 | 4 | 8 |
| 6110722 | Online bingo – chemical concepts introductory lesson | 5, 3 => 4 | 0 | 1 |
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aTPACK was calculated as the average of PCK & TK.
Table 4 showcases a variety of chemical subjects and assignments, as well as different technological solutions for designing online learning assignments. The results of the analysis demonstrate high PCK levels of most teachers as well as TPACK. It can be seen clearly by observing the results for the different participants, that there is a variance of combinations for TPACK, SRL, and AK scores. Some teachers exhibit (through their online assignment analysis) a balanced knowledge profile, in which the levels of teachers’ knowledge types are relatively similar. Other teachers show differences in the knowledge type levels, with usually the AK and SRL being lower.
4 Teachers’ knowledge types presented in their assignments – quantitative analysis
Descriptive statists of the teachers’ knowledge type levels, extracted from the assignments are presented in Table 5, including the average and standard deviation for each knowledge type. PCK (pedagogical-content-knowledge) and TK (technological knowledge), which were independent domains in the rubric were used to calculate TPACK (technological pedagogical and content knowledge), which was not assessed by the rubric explicitly. In this research, we focus on three factors: TPACK, SRL (fostering SRL skills), and AK (assessment knowledge). It is apparent that TPACK values are higher than SRL and AK, found for the chemistry teachers after they have participated in an intervention focused on self-regulated learning. ANOVA test for repeated measurements, using the Greenhouse-Geisser correction established a significant difference between the averages of the different teachers’ knowledge types F(1.772) = 19.715, p < 0.001. A Post-Hoc test with a Bonferroni correction for multiple measurements, indicates that the TPACK average (M = 7.18) is significantly higher (t(19) = 3.72, p < 0.001) than the SRL average (M = 5.10), as well as the AK average (M = 4.25) (t(19) = 6.96, p < 0.001). There was no significant difference found between the SRL and AK averages.
Descriptive statistics of knowledge types, determined by the assignment analysis.
| Knowledge type | (N = 20) | |
|---|---|---|
| M | SD | |
| PCK | 7.90 | 1.52 |
| TK | 6.45 | 1.91 |
| TPACKa | 7.18 | 1.53 |
| SRL | 5.10 | 2.29 |
| AK | 4.25 | 1.59 |
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aTPACK was calculated as the average of PCK & TK (not analyzed using the rubric).
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The values in bold represent the knowledge types which were in the focus of this research.
The PCK and TK (which represent TPACK), as well as SRL and AK levels, where also assigned by three levels of knowledge score – high, medium, and low. Figure 3 shows the distributions of each of the knowledge type by levels for the teachers’ sample.

Distribution of teachers’ knowledge types (N = 20).
The findings presented in Figure 3 reveal that the levels of PCK of 80 % of the participants are high (those participants designed an assignment ranking a high level of 7–10). The distribution of TK levels shows a different pattern: even though only 5 % of the participants are with low TK (like the PCK distribution), there are less high-level participants (55 %) and more medium level (40 %). The levels of SRL are lower: only 25 % were ranked as high level, whereas 30 % were ranked as low level, and the distribution of AK levels shows that 95 % of the teachers have AK of low or medium levels.
The analysis of the assignments according to our rubric allowed gaining a maximal total score of 30 points for TPACK, AK, and SRL, representing an optimal online assignment suited for SRL. For example, an online assignment developed by teacher-1 (participant 2020122) scored a total of 21.5 points, whereas the online assignment developed by teacher-2 (participant 2010122) scored 13.5 points. Besides computing the total score, the score for each knowledge type enables us to create a teacher’s profile, as shown in Figure 4, which shows the results of two teacher profiles. The outer triangle represents the maximum score of 10 points for all three knowledge types. The inner triangle (black line) in the diagram shows the teachers’ actual scores, representing their types of knowledge according to the analysis of the self-designed online assignments.

Two teacher profiles – based on their online assignment scores.
Figure 4 emphasizes the differences between the two chemistry teachers: Both show similar levels of TPACK—7.5 and 8.5—but there are remarkable differences in their levels of AK (7 and 3 respectively) and SRL (7 and 2 respectively). The first chemistry teacher shows relatively high levels of each of the three knowledge types, whereas the second teacher has much less balanced knowledge types.
The analysis of the online assignments allowed us to recognize patterns of teachers’ profiles. The profile of Teacher 1 shows a teacher with fully balanced knowledge types. The profile of Teacher 2 shows a TPACK-oriented teacher, but with much lower AK and SRL, as expressed in the analyzed assignments.
4.1 Teachers’ reflections
Content analysis revealed several categories for each aspect of the teachers’ reflections. Regarding strengths or advantages, most teachers pointed out the necessity to encourage students for independent learning and more responsibility for their learning. Some of the teachers found it advantageous that students’ responses to SRL guidelines could serve as feedback for them and allow improvements in their classroom teaching. Concerning the challenges, all the teachers mentioned their students’ difficulties to manage their learning and assume responsibility, especially when they must study an unfamiliar topic.
Most of the teachers stated that they would include guidelines to promote SRL in the assignments for their students. However, there were differences concerning the extent and mode of implementation, with some teachers stating they would integrate the SRL-promoting guidelines gradually. They intend to plan each online assignment to focus on guidelines for a specific SRL skill, such as time management and help-seeking. Other teachers noted that implementing assignments that promote SRL is suitable for lower high school classes, 9th and 10th grade, in which those skills are needed to allow students’ academic development. Other teachers thought that implementing SRL guidelines in students’ assignments in the 12th grade is favorable, and SRL could be integrated into the learning of the advanced topics of the curriculum, in which 21st century learning skills are more pronounced.
5 Conclusions
The ability to learn about teachers’ professional development by addressing the knowledge they have acquired, such as PCK, TPACK, and AK, was shown in various types of research in the past (Gess-Newsome et al., 2019; Großschedl et al., 2019; Willermark, 2018). The rubric presented in this research enabled the analysis of various online assignments based on teachers’ knowledge types. The assignments were different in both content (subjects and goals) and their underlying technological platforms. The analysis revealed various levels of knowledge types among the participants. The quantitative study showed that the teachers’’ TPACK level was significantly higher than their AK and SRL levels. These findings with respect TPACK and AK are in line with previous studies (Dori et al., 2021; Herscovitz et al., 2023).
Evaluation of teachers’ knowledge, by analyzing their practice and specifically assignments they designed has been suggested and performed by others (Koh, 2013; Koh, et al., 2018; Oster-Levinz & Klieger, 2010). However, the rubric presented in this research enables separate investigation of different knowledge types, including SRL. The criteria for each knowledge type in the rubric, shown in Table 5, enables capturing the essence of each one of the knowledge types while preserving uniformity across the goals of the online assignments. The rubric can therefore serve as an assessment tool for teachers’ professional development. It enables teachers and teacher mentors to assess one’s PD level and determine future goals accordingly.
The rubric focuses on the online assignments’ suitability for SRL, enabling the identification of a teacher profile, focusing on strengths and weakness of one’s knowledge types. The comparison of the assignments developed by two leading chemistry teachers exposed a notable difference in their AK and SRL. This finding, along with the significant difference between the TPACK and AK averages, is in line with previous studies, which revealed that a high teacher PCK is a prerequisite to the formation of AK (e.g., Avargil et al., 2012). Other researchers (Dori et al., 2021) showed differences between chemistry teachers’ knowledge types based on their teaching experience. These differences might be related to gaps in their knowledge, and they are being further investigated in a comparison study we are conducting with both pre- and in-service teachers. Since the teachers’ knowledge type levels are extracted from the analysis of their assignments, one could claim that besides the teachers’ experience, their willingness to implement changes should be considered. Shedding light on this question became possible using teachers’ reflections. In their reflections, most teachers viewed the process of adding guidelines to promote SRL as favorable and important. The teachers addressed the aspect of preparing their students for independent learning and for assuming responsibility for their own learning as most advantageous because this preparation meets challenges students face while attempting to control their learning.
Teachers attributed a meaningful contribution to the variety of guidelines for fostering and mentoring SRL skills. They expressed overall willingness to implement the SRL guidelines in their assignments, but their implementation modes were diverse.
In view of this research findings, we recommend implementation of learning assignments that include guidelines for the promotion of SRL as part of chemistry teachers’ PD programs. This implementation is expected to provide for strengthening students’ SRL skills, including time planning and management, goal setting, help-seeking, and reflection. Experience gained during the PD and the development of the assignments, as well as future implementation in the teachers’ classes is expected to enhance students’ SRL.
The low levels of AK revealed through the assignment analysis should be addressed. PD programs and teacher training can potentially reinforce teachers’ AK in both perceptions and competencies aspects. Integrating assessment into online assignments as part of such PD programs to promote SRL beside other knowledge types is highly recommended.
The methodological contribution of this research is the implementation of the rubric for analyzing the level of teacher knowledge types based on their self-designed online assignments. The rubric focuses on diverse types of teachers’ knowledge, allowing educators to focus on specific PD areas for both teachers as individuals s and for teachers as a group.
Additional studies on the effect of the promotion of self-regulated learning in other settings and cultures in both STEM and non-STEM subject areas are needed to better understand challenges teachers face during similar PD programs and how they can overcome these challenges.
Funding source: Chief Scientist, Israeli Ministry of Education – A project with collaboration with Dr. Anant Cohen, Tel Aviv University. https://edu.gov.il/sites/ChiefScientist/Pages/home.aspx
Award Identifier / Grant number: # 203-0313
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Informed consent: Informed consent was obtained from all subjects involved in the study.
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Author contributions: All authors have read and agreed to the published version of the manuscript.
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Conflict of interest statement: The authors declare no conflict of interest.
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Research funding: Chief Scientist, Israeli Ministry of Education # 203-0313 – A project in collaboratiom with Dr. Anant Cohen, Tel Aviv University.
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Data availability: The original contributions presented in the study are included in the article. Further inquiries to receive supplementary material can be sent to the corresponding author.
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Artikel in diesem Heft
- Frontmatter
- Special Issue Papers
- Frontiers of research in chemistry education for the benefit of chemistry teachers
- The context of science fiction in the pre-service teachers’ chemistry education
- The development of an instrument for measuring teachers’ and students’ beliefs about differentiated instruction and teaching in heterogeneous chemistry classrooms
- “Chemistry, climate and the skills in between”: mapping cognitive skills in an innovative program designed to empower future citizens to address global challenges
- Supporting first-year students in learning molecular orbital theory through a digital learning unit
- ChemDive – a classroom planning tool for infusing Universal Design for Learning
- Developing and evaluating a multiple-choice knowledge test about Brønsted-Lowry acid-base reactions for upper secondary school students
- Analysis of online assignments designed by chemistry teachers based on their knowledge and self-regulation
- Identifying self-regulated learning in chemistry classes – a good practice report
- Motivation to use digital educational content – differences between science and other STEM students in higher education
- Are you teaching “distillation” correctly in your chemistry classes? An educational reconstruction
- A new online resource for chemical safety and green chemistry in science education
Artikel in diesem Heft
- Frontmatter
- Special Issue Papers
- Frontiers of research in chemistry education for the benefit of chemistry teachers
- The context of science fiction in the pre-service teachers’ chemistry education
- The development of an instrument for measuring teachers’ and students’ beliefs about differentiated instruction and teaching in heterogeneous chemistry classrooms
- “Chemistry, climate and the skills in between”: mapping cognitive skills in an innovative program designed to empower future citizens to address global challenges
- Supporting first-year students in learning molecular orbital theory through a digital learning unit
- ChemDive – a classroom planning tool for infusing Universal Design for Learning
- Developing and evaluating a multiple-choice knowledge test about Brønsted-Lowry acid-base reactions for upper secondary school students
- Analysis of online assignments designed by chemistry teachers based on their knowledge and self-regulation
- Identifying self-regulated learning in chemistry classes – a good practice report
- Motivation to use digital educational content – differences between science and other STEM students in higher education
- Are you teaching “distillation” correctly in your chemistry classes? An educational reconstruction
- A new online resource for chemical safety and green chemistry in science education