The Scientific Graphic Organizer (SGO) is a pre-made schematic labjournal which simultaneously functions as a worksheet for practical work, see figure X. The SGO has many assumed merits above worksheets or labjournals in secondary school science education. Due to the standard format, for the teacher it is easy to see what students are doing. It is also easy to grade as it is only a two-pager with specific format. Moreover, it allows the teacher to open up the practical just by leaving cells open for the students to fill. What is known, is that it requires some trainings before students are aquainted with the SGO. Some questions worthy investigating:

Collaborative Data Collection (CDC) is an approach to practical work which allows to reduce the time to collect data. Rather than doing the same practical by each individual, students collect only a subset of the data. However, the entire dataset is available to them as it the data are shared on the Interactive White Board. This approach ensures that students conduct the experiment, understand the equipment and how to manipulate it. However, as less time is spend on data collection more time is available to discuss the results and draw and discuss potential conclusions. As the discussion is lead by the teacher, it is likely that students come to understand the essentials of the practical. Interesting questions that are worthy to investigate are:

• What are the merits and trade offs of the CDC approach?
• What are essential features of the CDC approach that contribute to conceptual development?

Why should students even bother to put effort in a practical, especially when the teacher already knows the answers they are looking for? One way to have them engage better in a practical is to create the awareness that only the best available answer to the research question is good enough in science. Why? Because otherwise we can not trust the outcomes, we can't build bridges, or more importantly, walk them if the engineers were doing a lousy job. But, what do we understand by the best availble answer and how do we prove that we did our utmost? That, given all practical limitations, we came up with the best answer. This requires students to engage in argumentation: Defend every decision they make during the investigation. Moreover, it requires a better understanding of what makes trustworthy data, and when we can regard these data as evidence in support of a claim. Pols et al (2022) developed various so-called Understandings of Evidence - insights of researchers when constructing a scientifically cogent argument for a claim. In science education we want students to acquire several of these insights. Interesting questions which can be investigated are:

• What ways help to foster the idea that in science only the best answer to the research question suffices?
• What are interventions that develop specific Understandings of Evidence?
• How does the use of peer review enhance the critial approach to practical work?

Jupyter Notebooks provide the opportunity to make a lab worksheet using text, embed videos and simultaenously store and analyze data using Python. This online notebook can be saved and subsequently shared with the teacher. There are multiple reasons why one could prefer these Notebooks over worksheets. For one, all materials, including the data-analysis are found in one document. Moreover, students can directly see the graph (without struggling with excel). However, some basic programming skills are required and more advanced data-analysis techniques should be developed. At the moment there is no literature on the use of Jupyter Notebooks as instruments to guide labwork at secondary school level. Interesting research questions are therefore:

• What basic knowledge of Jupyter Notebooks and Python is required before students can make use of these as worksheets in lab activities and how can students acquire that knowledge effectively?
• How do teachers experience working with Notebooks as replacement for worksheet?
• How are students supported in doing lab work in physics when working with Jupyter Notebooks?

In movies the laws of physics are often violated, especially in cartoons. Debunking stunts or movie scenes can be highly entertaining. Especially topics related to kinematic are suitable.

Practical are often used to acquire content knowledge. The approach frequently used is to let students conduct the experiment and 'discover' the concepts. However, literature reports that this approach is hardly effective. An approach worthy investigating is one where students predict the outcomes of the experiment: make the graph that is supposed to show up. They then conduct the experiment and see whether their results fit the theory. Predicting what should be observed helps as well in understanding what range is useful when measuring. For instance, in studying lenses one could derive the function: b = v*f/(f-v). Using this function with a 10dpt lens, for instance in Geogebra or Python, results in the following graph: It is clear that one should use an object distance between ... and ... cm. Anecdotal evidence shows that this increases students' appreciation of the practical and increases the understanding of the content under investigation. However, we can question:

• How effective is this approach compared to traditional approaches?
• What aspects of this approach contributes especially to students' enhanced understanding?
• What practicals are suitable for this approach and how can we transform traditional practicals effectively?