Learners’ challenges in understanding and performing experiments: a systematic review of the literature

Figures & data

Figure 1. Flowchart of the search procedure and data analysis used for the systematic literature review, following PRISMA guidelines (Page et al., 2021) .

Figure 2. Number of challenges during the inquiry phases and number of studies reporting each challenge (studies can report more than one challenge).

Table 1. Overview of empirical studies reporting about learners’ challenges when stating research questions during experimentation and selected properties.

Stating research questions
Challenge		Reference(s)	Review-source^a	Sample size(s)	Grade	Target group(s)^b	Assessment(s)^c	Context domain(s)^d	Covered challenge(s)^e
Q1	Not stating causal research questions	Cuccio-Schirripa and Steiner (2000)	CR	181	7	S	PP	B, ES, P	Q1
		Hofstein et al. (2005)	CR	111	12	S	PP	C	Q1
		Neber and Anton (2008a)	CR	58	10	S	PP	C	Q1
		Neber and Anton (2008b)	CR	53	10	S	PP	C	Q1
Q2*	Stating questions that are qualitative in nature	Hofstein et al. (2004)	CR	25 (groups)	11, 12	S	LP, OB	C	Q2

Note. Learners’ specific approaches are marked with an asterisk, as they are not necessarily comprising the inquiry phase or an experiment in general, but need to be reflected in the further learning process. ^aReview source: ‘CR’ current review; ^btarget group(s): ‘S’ secondary school students; ^cassessment(s): ‘LP’ analysis of laboratory reports produced during experimentation, ‘OB’ observation of learners in experimental settings; ‘PP’ paper-pencil-test; ^dcontext domain(s): ‘B’ biology (including life sciences), ‘C’ chemistry (including natural and processed materials), ‘ES’ earth and space science (including geography), ‘P’ physics (including energy and change); ^dcovered challenge(s): overview of all challenges reported by the study.

Table 2. Overview of empirical studies reporting about learners’ challenges generating hypotheses during experimentation and selected properties.

Generating hypotheses
	Challenge	Reference(s)	Review-source^a	Sample size(s)	Grade^b	Target group(s)^c	Assessment(s)^d	Context domain(s)^e	Covered challenge(s)
H1	Not knowing what a hypothesis is	Darus and Saat (2014)	CR	6	3	E	INTV, LP, OB	P	H1, H2, M2
		Njoo and de Jong (1993)	JJ	17 (study 1), 91 (study 2)	-*	U	A, CS, LP, OB (study 1), CS, LP, PP (study 2)	ME	H1, PC7
		Zhai et al. (2014)	CR	161	4	E	INTV, OB, PP, V	NS	H1
H2*	Working without a hypothesis	Darus and Saat (2014)	CR	6	3	E	INTV, LP, OB	P	H1, H2, M2
		Park (2006)	CR	6	-*	U	INTV	P	H2, H3, DC9
		Valanides et al. (2013)	CR	498	4, 6	E	INTV, LP	L	H2, H5, H7, PC3, PC7, PC12, DC6, DC7, DC14, M2
H3	Operating abductive	Kwon et al. (2006)	CR	290 (study 1), 34 (study 2)	5	E	PP	P	H3
		García-Carmona et al. (2017)	CR	66	-*	U	PP	P	H3, PC7, PC9, PC10, DC1, DC7, DC14, M2
		Park (2006)	CR	6	-*	U	INTV	P	H2, H3, DC9
H4*	Selecting only hypotheses that predict an influence of the independent variable on the dependent variable	Kanari and Millar (2004)	Z	60	n/a	n/a	INTV, V	P	H4, PC10, DC3, DC6, DC7, M1, M6
		Klahr et al. (2007)	Z	56	7, 8	S	PP	EL	H4
		Schauble (1990)	Z	22	5, 6	n/a	CS, INTV, LP	n/a	H4, H6, PC6, PC7, DC8, DC13, M9
		Schauble (1996)	Z	20	5, 6	A, S	INTV, LP	P	H4, H6, PC3, DC7, DC9, DC12, DC13, M1
H5*	Generating hypotheses that include more than one independent variable	Baur (2018)	CR	18	5, 6	S	LP, V	B	H5
		Valanides et al. (2013)	CR	498	4, 6	E	INTV, LP	L	H2, H5, H7, PC3, PC7, PC12, DC6, DC7, DC14, M2
H6*	Only generating hypotheses that are plausible and predict expected data (fear of rejection)	Echevarria (2003)	Z	44–48	7	S	A, CS, PP, V	B	H6
		Klahr et al. (1993)	JJ; Z	64	3, 6	n/a, U	CS, LP	ES	H6, H7, PC7, DC8, DC13
		Penner and Klahr (1996b)	Z	120 (study 1), 59 (study 2)	-*	U	PP	L	H6, DC7
		Schauble (1990)	Z	22	5, 6	n/a	CS, INTV, LP	EL	H4, H6, PC6, PC7, DC8, DC13, M9
		Schauble (1996)	Z	20	5, 6	A, S	INTV, LP	P	H4, H6, PC3, DC7, DC9, DC12, DC13, M1
		Zimmerman et al. (2003)	Z	14	6	S	INTV	P	H6, DC6
H7*	Not aware that more than one hypothesis is possible	Klahr et al. (1993)	Z	64	3, 6	n/a, U	CS, LP	ES	H6, H7, PC7, DC8, DC13
		Valanides et al. (2013)	CR	498	4, 6	E	INTV, LP	L	H2, H5, H7, PC3, PC7, PC12, DC6, DC7, DC14, M2

Note. Learners’ specific approaches are marked with an asterisk, as they are not necessarily comprising the inquiry phase or an experiment in general, but need to be reflected in the further learning process. ^aReview source: ‘CR’ current review, ‘JJ’ De Jong and van Joolingen (1998), ‘Z’ Zimmerman (2007); ^bgrade: if marked with a -* no grade level can be depicted, because the study did not take place at school level; ^ctarget group(s): ‘E’ elementary school students, ‘S’ secondary school students, ‘U’ university students, ‘A’ adults, ‘n/a’ students target group not further specified; ^dassessment(s): ‘A’ audio recording of laboratory session(s); ‘CS’ computer simulation; ‘INTV’ interviews; ‘LP’ analysis of laboratory reports produced during experimentation; ‘OB’ observation of learners in experimental settings; ‘PP’ paper-pencil-test, ‘V’ video recording of laboratory session(s); ^econtext domain(s): ‘B’ biology (including life sciences), ‘C’ chemistry (including natural and processed materials), ‘EL’ everyday life”, ‘ES’ earth and space science (including geography), ‘L’ logic (including rule discovery), ‘ME’ mechanical engineering, ‘NS’ natural sciences (no specification), ‘P’ physics (including energy and change), ‘n/a’ context domain not clearly given; ^dcovered challenge(s): overview of all challenges reported by the study.

Table 3. Overview of empirical studies reporting about learners’ challenges planning and conducting an experiment and selected properties.

Planning and conducting an experiment
	Challenge	Reference(s)	Review-source^a	Sample size(s)	Grade^b	Target group(s)^c	Assessment(s)^d	Context domain(s)^e	Covered challenge(s)
PC1	Changing conditions throughout the ongoing experiment	Baur (2018)	CR	18	5, 6	S	LP, V	B	PC1, PC5, PC8, PC12, DC10, M3, M4
PC2	Limiting the experiment on just a few independent variables	Hammann et al. (2010)	CR	1006	5, 6	S	PP	B	PC2, PC5, PC7, DC7, M4
		Wu & Wu (2011)	CR	68	5	E	INTV, LP, PP, V	P	PC2, PC3, PC10, PC11, DC1, DC3, DC7, DC9 M6
PC3	Not controlling variables	Arnold et al. (2014)	CR	96 (study 1), 12 (study 2)	12 (study 1), 11 (study 2)	n/a	OB, PP	B	PC3, PC9, PC10
		Chen and Klahr (1999)	CR	87	2, 4	E	INTV, PP, V	P	PC3
		Dasgupta et al. (2016)	CR	4	-*	U	INTV, PP	B	PC3, PC5, PC10, DC4, DC10, M2
		Erdosne Toth et al. (2000)	CR	87 (study 1), 77 (study 2)	2, 3, 4	E	INTV, LP, PP	P	PC3
		Garcia-Mila et al. (2011)	CR	34	6	E	INTV, LP	B	PC3, DC11, M5
		Van Joolingen and de Jong (1991)	JJ	31	-*	U	CS, PP	C	PC3
		Klahr and Nigam (2004)	Z	112	3, 4	E	INTV, PA	P	PC3
		Kuhn (2007)	CR	30	4	E	CS	ES	PC3
		Kuhn and Dean (2005)	CR	30	6	S	CS	ES	PC3, DC10
		Reimann (1991)	JJ	8	-*	U	A, CS	P	PC3
		Schauble (1996)	Z	20	5, 6	A, S	INTV, LP	P	H4, H6, PC3, DC7, DC9, DC12, DC13, M1
		Schauble et al. (1991a)	JJ	22	-*	U	A, CS, OB, LP	P	PC3
		Shute and Glaser (1990)	JJ	30 (study 1), 530 (study 2)	-*	A, U	PP, CS	ES	PC3
		Siegler and Liebert (1975)	Z	60	5, 8	n/a	PA, PP	L	PC3, M5
		Tairab (2015)	CR	128	8, 10, 12	n/a	PP	NS	PC3
		Valanides et al. (2013)	CR	498	4, 6	E	INTV, LP	L	H2, H5, H7, PC3, PC7, PC12, DC6, DC7, DC14, M2
		Wu & Wu (2011)	CR	68	5	E	INTV, LP, PP, V	P	PC2, PC3, PC10, PC11, DC1, DC3, DC7, DC9 M6
PC4	When testing positive and negative hypotheses different strategies are used	Croker and Buchanan (2011)	CR	144	n/a	E	INTV, PP	B	PC4
PC5	Working without a control condition	Baur (2018)	CR	18	5, 6	S	LP, V	B	PC1, PC5, PC8, PC12, DC10, M3, M4
		Dasgupta et al. (2016)	CR	4	-*	U	INTV, PP	B	PC3, PC5, PC10, DC4, DC10, M2
		Hammann et al. (2010)	CR	1006	5, 6	S	PP	B	PC2, PC5, PC7, DC7, M4
PC6	Producing desired experimental results	Kuhn et al. (1995)	Z	32	4	n/a, U	INTV	P, S	PC6, DC13
		Kuhn et al. (1992)	Z	12 (study 1), 20 (study 2)	4, (study 1), 5, 6 (study 2)	E (study 1), n/a (study 2)	INTV, LP	EL	PC6, PC11, DC8, DC13
		Schauble (1990)	Z	22	5, 6	n/a	CS, INTV, LP	EL	H4, H6, PC6, PC7, DC8, DC13, M9
PC7	Engineering approach	Carey et al. (1989)	Z	27	7	E/S	INTV	NS	PC7, M5
		García-Carmona et al. (2017)	CR	66	-*	U	PP	P	H3, PC7, PC9, PC10, DC1, DC7, DC14, M2
		Hammann et al. (2010)	CR	66	-*	U	PP	P	H3, PC7, PC9, PC10, DC1, DC7, DC14, M2
		Klahr et al. (1993)	Z	64	3, 6	n/a, U	CS, LP	ES	H6, H7, PC7, DC8, DC13
		Njoo and de Jong (1993)	JJ	17 (study 1), 91 (study 2)	-*	U	A, CS, LP, OB (study 1), CS, LP, PP (study 2)	ME	H1, PC7
		Schauble (1990)	Z	22	5, 6	n/a	CS, INTV, LP	EL	H4, H6, PC6, PC7, DC8, DC13, M9
		Schauble et al. (1991b)	JJ, Z	16	5, 6	S	A, INTV, LP	P	PC7
		Schauble et al. (1995)	JJ	21 (study 1); 35 (study 2)	6	S	INTV	ES, P	PC7
		Tschirgi (1980)	Z	48	2, 4, 6	n/a, U	PP	EL	PC7, M9
		Valanides et al. (2013)	CR	498	4, 6	E	INTV, LP	L	H2, H5, H7, PC3, PC7, PC12, DC6, DC7, DC14, M2
		White (1993)	JJ	79, 41, 45 (study 1), n/a (study 2)	6, 11 (study 1), 12 (study 2)	S	A, CS, INTV, OB, PP (study 1), CS, PP (study 2)	P (study 1), NS (study 2)	PC7
PC8	Handling material and substances indiscriminately	Baur (2018)	CR	18	5, 6	S	LP, V	B	PC1, PC5, PC8, PC12, DC10, M3, M4
PC9	Lack of determination of test times	Arnold et al. (2014)	CR	96 (study 1), 12 (study 2)	12 (study 1), 11 (study 2)	n/a	OB, PP	B	PC3, PC9, PC10
		Duggan and Gott (2000)	CR	179	-*	U	INTV, PP	NS	PC9
		García-Carmona et al. (2017)	CR	66	-*	U	PP	P	H3, PC7, PC9, PC10, DC1, DC7, DC14, M2
PC10	Lack of repetition of measurement	Arnold et al. (2014)	CR	96 (study 1), 12 (study 2)	12 (study 1), 11 (study 2)	n/a	OB, PP	B	PC3, PC9, PC10
		Dasgupta et al. (2016)	CR	4	-*	U	INTV, PP	B	PC3, PC5, PC10, DC4, DC10, M2
		García-Carmona et al. (2017)	CR	66	-*	U	PP	P	H3, PC7, PC9, PC10, DC1, DC7, DC14, M2
		Kanari and Millar (2004)	Z	60	n/a	n/a	INTV, V	NS	PC10, DC3, DC6, DC7, M1, M6
		Wu & Wu (2011)	CR	68	5	E	INTV, LP, PP, V	P	PC2, PC3, PC10, PC11, DC1, DC3, DC7, DC9 M6
PC11	Planning and conducting the same trials several times (not with the aim of repeated measurements)	Kuhn et al. (1992)	JJ	12 (study 1), 20 (study 2)	4 (study 1), 5, 6 (study 2)	E (study 1), n/a (study 2)	INTV, LP	EL	PC6, PC11, DC8, DC13
		Schrempp and Sodian (1999)	CR	44	2	E, U	INTV	EL	PC11
		Wu & Wu (2011)	CR	68	5	E	INTV, LP, PP, V	P	PC2, PC3, PC10, PC11, DC1, DC3, DC7, DC9 M6
PC12	Do not involve all test trials while conducting their experiment	Baur (2018)	CR	18	5, 6	S	LP, V	B	PC1, PC5, PC8, PC12, DC10, M3, M4
		Valanides et al. (2013)	CR	498	4, 6	E	INTV, LP	L	H2, H5, H7, PC3, PC7, PC12, DC6, DC7, DC14, M2

Note. ^aReview source: ‘CR’ current review, ‘JJ’ De Jong and van Joolingen (1998), ‘Z’ Zimmerman (2007); ^bgrade: if marked with a -* no grade level can be depicted, because the study did not take place at school level; ^ctarget group(s): ‘E’ elementary school students, ‘S’ secondary school students, ‘U’ university students, ‘A’ adults, ‘n/a’ students target group not further specified; ^dassessment(s): ‘A’ audio recordings of laboratory session(s), ‘CS’ computer simulation, ‘INTV’ interviews, ‘LP’ analysis of laboratory reports produced during experimentation, ‘OB’ observation of learners in experimental settings, ‘PA’ performance assessment test; ‘PP’ paper-pencil-test, ‘V’ video recordings of laboratory session(s); ^econtext domain(s): ‘B’ biology (including life sciences), ‘C’ chemistry (including natural and processed materials), ‘EL’ everyday life”, ‘ES’ earth and space science (including geography), ‘ME’ mechanical engineering, ‘NS’ natural sciences (no specification), ‘P’ physics (including energy and change); ‘S’ social sciences, ^dcovered challenge(s): overview of all challenges reported by the study.

Table 4. Overview of empirical studies reporting about learners’ challenges analysing and drawing conclusions during experimentation and selected properties.

Data analysis and drawing conclusions
	Challenge	Reference(s)	Review-source^a	Sample size(s)		Grade^b		Target group(s)^c			Assessment(s)^d		Context domain(s)^e		Covered challenge(s)
DC1	Confusing between observation and interpretation of data	Boaventura et al. (2013)	CR	135		4		E			A, OB, PP, V		B		DC1, DC10
		García-Carmona et al. (2017)	CR	66		-*		U			PP		P		H3, PC7, PC9, PC10, DC1, DC7, DC14, M2
		Wu & Wu (20112)	CR	68		5		E			INTV, LP, PP, V		P		PC2, PC3, PC10, PC11, DC1, DC3, DC7, DC9 M6
DC2*	Variability in measured data leads to uncertainty	Masnick & Morris (2008)	CR	133 (study 1), 101 (study 2)		3, 6 (study 1), 6 (study 2)		n/a, U (study 1), n/a, U (study 2)			INTV, PP		EL		DC2
DC3	Not calculating the arithmetic mean	Kanari and Millar (2004)	Z	60		n/a		n/a			INTV, V		P		H4, PC10, DC3, DC6, DC7, M1, M6
		Masnick and Klahr (2003)	CR	49		2, 4		E			INTV, PP, V		P		DC3
		Wu & Wu (2011)	CR	68		5		E			INTV, LP, PP, V		P		PC2, PC3, PC10, PC11, DC1, DC3, DC7, DC9 M6
DC4	Diagrams that are not suitable	Croner (2003)	CR	60		7		S			INTV, OB, LP		P		DC4, M7
		Dasgupta et al. (2016)	CR	4		-*		U			INTV, PP		B		PC3, PC5, PC10, DC4, DC10, M2
		Durmaz and Mutlu (2017)	CR	43		7		E			LP, PP		NS		DC4
DC5	Difficulties when reading graphs	Mokros and Tinker (1987)	JJ	125		7, 8		S			CS, INTV, OB, PP		NS		DC5
DC6	Drawing conclusions when the result shows no covariation is difficult	Aoki (1991)	Z	152		4, 5, 6		n/a, U			PP		P		DC6
		Kanari and Millar (2004)	Z	60		n/a		n/a			INTV, V		P		H4, PC10, DC3, DC6, DC7, M1, M6
		Valanides et al. (2013)	CR	498		4, 6		E			INTV, LP		L		H2, H5, H7, PC3, PC7, PC12, DC6, DC7, DC14, M2
		Zimmerman et al. (2003)	Z	14		6		S			INTV		P		H6, DC6
DC7	Confirmation bias	Dunbar (1993)	JJ	40		_*		U			A, CS		B		DC7, DC14
		Hammann et al. (2010)	CR	1006		5, 6		S			PP		B		PC2, PC5, PC7, DC7, M4
		García-Carmona et al. (2017)	CR	66		-*		U			PP		P		H3, PC7, PC9, PC10, DC1, DC7, DC14, M2
		Kanari and Millar (2004)	Z	60	n/a		n/a				INTV, V		P		H4, PC10, DC3, DC6, DC7, M1, M6
		Penner and Klahr (1996b)	Z	120 (study 1), 59 (study 2)		-*	U					PP		L	H6, DC7
		Quinn and Alessi (1994)	JJ	179		n/a	n/a					CS		B	DC7
		Schauble (1996)	Z	20		5, 6	A, S					INTV, LP		P	H4, H6, PC3, DC7, DC9, DC12, DC13, M1
		Valanides et al. (2013)	CR	498		4, 6	E					INTV, LP		L	H2, H5, H7, PC3, PC7, PC12, DC6, DC7, DC14, M2
		Wason (1960)	JJ	29		-*	U					PP		L	DC7
		Wu & Wu (2011)	CR	68		5		E			INT, LP, PP, V		P		PC2, PC3, PC10, PC11, DC1, DC3, DC7, DC9 M6
DC8	Ignoring anomalous data	Amsel and Brock (1996)	Z	236		2, 3, 6, 7		A, E, U	INTV				B		DC8
		Greenhoot et al. (2004)	CR	211		-*		U	PP				EL, P		DC8
		Klahr et al. (1993)	JJ	64		3, 6		n/a, U	CS, LP				ES		H6, H7, PC7, DC8, DC13
		Kuhn et al. (1992)	JJ	12 (study 1), 20 (study 2)		4 (study 1), 5, 6 (study 2)		E (study 1), n/a (study 2)	INTV, LP				EL		PC6, PC11, DC8, DC13
		Wahser and Sumfleth (2008)	CR	172		7		S	PP, V				C		DC8, M4
		Schauble (1990)	Z	22		5, 6		n/a	CS, INTV, LP				EL		H4, H6, PC6, PC7, DC8, DC13, M9
DC9*	Anomalous data leads to the assumption that there are flaws in the experimental procedure	Park (2006)	CR	6		-*		U	INTV				P		H2, H3, DC9
		Schauble (1996)	Z	20		5, 6		A, S	INTV, LP				P		H4, H6, PC3, DC7, DC9, DC12, DC13, M1
		Wu & Wu (2011)	CR	68		5		E		INTV, LP, PP, V			P		PC2, PC3, PC10, PC11, DC1, DC3, DC7, DC9 M6
DC10	Drawing incorrect inferences	Baur (2018)	CR	18		5, 6		S	LP, V				B		PC1, PC5, PC8, PC12, DC10, M3, M4
		Boaventura et al. (2013)	CR	135		4		E	A, OB, PP, V				B		DC1, DC10
		Dasgupta et al. (2016)	CR	4		-*		U	INTV, PP				B		PC3, PC5, PC10, DC4, DC10, M2
		Kuhn and Dean (2005)	CR	30		6		S	CS				ES		PC3, DC10
DC11	Neglecting the protocol leads to incorrect inferences	García-Mila et al. (2011)	CR	34		6		E	INTV, LP				B		PC3, DC11, M5
DC12	Learners make no exclusion inferences	Schauble (1996)	Z	20		5, 6		A, S	INTV, LP				P		H4, H6, PC3, DC7, DC9, DC12, DC13, M1
DC13	Learners do not give up less advanced strategies when interpreting results	Klahr et al. (1993)	Z	64		3, 6		n/a, U	CS, LP				ES		H6, H7, PC7, DC8, DC13
		Kuhn et al. (1995)	Z	32		4		n/a, U	INTV				P, S		PC6, DC13
		Kuhn et al. (1992)	Z	12 (study 1), 20 (study 2)		4 (study 1), 5, 6 (study 2)		E (study 1), n/a (study 2)	INTV, LP				EL		PC6, PC11, DC8, DC13
		Schauble (1990)	Z	22		5, 6		n/a	CS, INTV, LP				EL		H4, H6, PC6, PC7, DC8, DC13, M9
		Schauble (1996)	Z	20		5, 6		A, S	INTV, LP				P		H4, H6, PC3, DC7, DC9, DC12, DC13, M1
DC14*	Not connecting hypotheses to previous results	Dunbar (1993)	JJ	40		-*		U	A, CS				B		DC7, DC14
		García-Carmona et al. (2017)	CR	66		-*		U	PP				P		H3, PC7, PC9, PC10, DC1, DC7, DC14, M2
		Klahr and Dunbar (1988)	JJ	20 (study 1), 10 (study 2)		-*		U	A, CS, V				L		DC14
		Valanides et al. (2013)	CR	498		4, 6		E	INTV, LP				L		H2, H5, H7, PC3, PC7, PC12, DC6, DC7, DC14, M2

Note. Learners’ specific approaches are marked with an asterisk, as they are not necessarily comprising the inquiry phase or an experiment in general, but need to be reflected in the further learning process. ^aReview source: ‘CR’ current review, ‘JJ’ De Jong and van Joolingen (1998), ‘Z’ Zimmerman (2007); ^bgrade: if marked with a -* no grade level can be depicted, because the study did not take place at school level; ^ctarget group(s): ‘E’ elementary school students, ‘S’ secondary school students, ‘U’ university students, ‘A’ adults, ‘n/a’ students target group not further specified; ^dassessment(s): ‘A’ audio recordings of laboratory session(s), ‘CS’ computer simulation, ‘INTV’ interviews, ‘LP’ analysis of laboratory reports produced during experimentation, ‘OB’ observation of learners in experimental settings, ‘PP’ paper-pencil-test, ‘V’ video recordings of laboratory session(s); ^econtext domain(s): ‘B’ biology (including life sciences), ‘C’ chemistry (including natural and processed materials), ‘EL’ everyday life”, ‘ES’ earth and space science (including geography), ‘L’ logic (including rule discovery), ‘MT’ maths, ‘NS’ natural sciences (no specification), ‘P’ physics (including energy and change), ‘S’ social sciences; ^dcovered challenge(s): overview of all challenges reported by the study.

Table 5. Overview of empirical studies reporting about learners’ challenges related to multiple inquiry phases and selected properties.

Multiple inquiry phases
	Challenge	Reference(s)	Review-source^a	Sample size(s)	Grade^b	Target group(s)^c	Assessment(s)^d	Context domain(s)^e	Covered challenge(s)
M1	Not investigating rival hypotheses	Kanari and Millar (2004)	Z	60	n/a	n/a	INTV, V	P	H4, PC10, DC3, DC6, DC7, M1, M6
		Penner and Klahr (1996a)	Z	30	n/a	n/a	INTV, V	P	M1
		Schauble (1996)	Z	20	5, 6	A, S	INTV, LP	P	H4, H6, PC3, DC7, DC9, DC12, DC13, M1
M2	Not being able to identify variables	Darus and Saat (2014)	CR	6	3	E	INTV, OB, LP	P	H1, H2, M2
		Dasgupta et al. (2016)	CR	4	-*	U	INTV, PP	B	PC3, PC5, PC10, DC4, DC10, M2
		García-Carmona et al. (2017)	CR	66	-*	U	PP	P	H3, PC7, PC9, PC10, DC1, DC7, DC14, M2
		Ramnarain (2012)	CR	42	9	S	INTV, OB, V	P	M2
		Valanides et al. (2013)	CR	498	4, 6	E	A, INTV, LP	L	H2, H5, H7, PC3, PC7, PC12, DC6, DC7, DC14, M2
M3*	Changing hypotheses throughout the experiment	Baur (2018)	CR	18	5, 6	S	LP, V	B	PC1, PC5, PC8, PC12, DC10, M3, M4
M4	Trying out	Baur (2018)	CR	18	5, 6	S	LP, V	B	PC1, PC5, PC8, PC12, DC10, M3, M4
		Hammann et al. (2010)	CR	1006	5, 6	S	PP	B	PC2, PC5, PC7, DC7, M4
		Wahser and Sumfleth (2008)	CR	172	7	S	PP, V	C	DC8, M4
M5	Lack of documentation	Carey et al. (1989)	Z	27	7	E/S	INTV	NS	PC7, M5
		Garcia-Mila and Andersen (2007)	Z	31	4	n/a, U	INTV, LP	P, S	M5
		Garcia-Mila et al. (2011)	CR	34	6	E	INTV, LP	B	PC3, DC11, M5
		Siegler and Liebert (1975)	Z	60	5, 8	n/a	PA, PP	L	PC3, M5
M6	No awareness of measurement error	Kanari and Millar (2004)	Z	60	n/a	n/a	INTV, V	P	H4, PC10, DC3, DC6, DC7, M1, M6
		Wu & Wu (2011)	CR	68	5	E	INTV, LP, PP, V	P	PC2, PC3, PC10, PC11, DC1, DC3, DC7, DC9 M6
M7*	Not constructing tables	Croner (2003)	CR	60	7	S	INTV, OB, LP	P	DC4, M7
M8	Value of the results determines the seeking for evidence	Schauble (1990)	Z	22	5, 6	n/a	CS, INTV, LP	EL	H4, H6, PC6, PC7, DC8, DC13, M9
		Tschirgi (1980)	Z	48	2, 4, 6	n/a, U	PP	EL	PC7, M9

Note. Learners’ specific approaches are marked with an asterisk, as they are not necessarily comprising the inquiry phase or an experiment in general, but need to be reflected in the further learning process. ^aReview source: ‘CR’ current review, ‘Z’ Zimmerman (2007); ^bgrade: if marked with a -* no grade level can be depicted, because the study did not take place at school level; ^ctarget group(s): ‘E’ elementary school students, ‘S’ secondary school students, ‘U’ university students, ‘A’ adults, ‘n/a’ students target group not further specified; ^dassessment(s): ‘CS’ computer simulation; ‘INTV’ interviews; ‘LP’ analysis of laboratory reports produced during experimentation; ‘OB’ observation of learners in experimental settings, ‘PA’ performance assessment test, ‘PP’ paper-pencil-test, ‘V’ video recordings of laboratory session(s); ^econtext domain(s): ‘B’ biology (including life sciences), ‘EL’ everyday life”, ‘L’ logic (including rule discovery, ‘NS’ natural sciences (no specification), ‘P’ physics (including energy and change), ‘S’ social sciences; ^dcovered challenge(s): overview of all challenges reported by the study.

Table

Challenge		Description	Expert ratings	Frequency of assignment (%)	Mean answers per rater	Krippendorff’s alpha (nominal)
Stating research questions
Q1	Not stating causal research questions	Learners only ask factual type questions that cannot be investigated by means of an experiment. Example of a factual type question: How is a heart constructed? What is in this lotion?	16	100	1.0	1.0
Q2*	Stating questions that are qualitative in nature	Learners state questions that are not quantitative type questions. Example of a qualitative type question: Does a plant produce oxygen with the help of sunlight? Example of a quantitative type question: Which effect has the intensity of sunlight on the production of oxygen in a plant? (Which factors are necessary for photosynthesis?) Which effect has the intensity of sunlight on the production of oxygen in a plant? (Which factors are necessary for photosynthesis?)	17	100	1.0	1.0
Generating hypotheses
H1	Not knowing what a hypothesis is	Learners do not have a scientific correct concept about what a hypothesis is.	17	100	1.0	1.0
H2*	Working without a hypothesis	Learners do not generate hypotheses during an experiment.	17	100	1.1	1.0
H3	Operating abductive	Learners do not include their existing content knowledge or knowledge about theories to generate a hypothesis.	16	100	1.0	1.0
H4*	Selecting only desired hypotheses that predict an influence of the independent variable on the dependent variable	Learners do not formulate hypotheses that predict no effect. Hence, they select hypotheses that predict an influence of the independent variable on the dependent variable. For example: ‘salt has an influence on yeast fermentation’ rather than ‘salt has no influence on yeast fermentation.’	17	94.1	1.1	0.80
H5*	Generating hypotheses that include more than one independent variable	Learners state hypotheses that include more than one independent variable. Example of a multivariate hypothesis: Yeast needs salt and flour for fermentation.	16	100	1.1	0.93
H6*	Only generating hypotheses that are plausible and predict expected data (fear of rejection)	Learners just generate hypotheses that are plausible for them and avoid hypotheses with a high chance of being rejected. Existing preconceptions or beliefs have a strong influence on what kind of hypotheses are formulated.	17	100	1.0	1.0
H7*	Not aware that more than one hypothesis is possible	Learners do not generate more than one hypothesis.	17	100	1.0	1.0
Planning and conducting an experiment
PC1	Changing conditions throughout the ongoing experiment	Learners intervene and change experimental conditions throughout the ongoing investigation. Example for changes: add substances in a mixture after the experiment has already started; rearrange the position of an animal in the middle of a behavioural experiment.	17	100	1.0	1.0
PC2	Limiting the experiment on just a few independent variables	When learners are required to investigate multiple independent variables in an experiment, they limit the investigation on just a few independent variables. For example, when asked to investigate ten types of substrates for germination they only test two of them.	17	100	1.0	1.0
PC3	Not controlling variables	Learners vary many variables at the same time and do not control potential confounding variables across experimental conditions (= confounded experiment, no valid inferences possible).	17	100	1.1	0.87
PC4	When testing positive and negative hypotheses different strategies are used	When testing a hypothesis, that predicts a positive outcome (e.g., honey leads to a ‘good cake’), learners use the less valid HOTAT strategy (Hold One Thing At a Time): baking another cake using the same sweetener (i.e., honey), but changing the fat and flour. When testing a hypothesis, that predicts a negative outcome (e.g., honey leads to a ‘bad cake’), learners use the more valid VOTAT strategy (Vary One Thing At a Time; or CVS): using a different sweetener (i.e., sugar), but the same fat and flour.	16	87.5	1.1	0.70
PC5	Working without a control condition	Learners are just working with a single test condition and without a control (= results cannot be compared).	17	100	1.1	0.93
PC6	Producing desired experimental results	Learners design confounded experiments, as a mean to produce a desired outcome.	17	94.1	1.1	0.79
PC7	Engineering approach	Learners create an effect as an outcome (‘engineering approach’) or just conduct experiments that are fun.	16	93.8	1.2	0.66
PC8	Handling material and substances indiscriminately	Learners do not consider the comparability of substance amounts or laboratory material (e.g., using test tubes with different volume) between different conditions.	17	100	1.0	1.0
PC9	Lack of determination of test times	Learners do not determine test times, as well as start- and endpoints and/or periods or intervals.	17	100	1.0	1.0
PC10	Lack of repetition of measurement	Learners do not repeat trials of an experiment. They are more likely to repeat measurements when the independent variable does not have an influence on the dependent variable.	17	100	1.1	0.93
PC11	Planning and conducting the same trials several times (not with the aim of repeated measurements)	Learners design and conduct the same trials (unintended) several times.	16	100	1.0	1.0
PC12	Do not involve all test trials while conducting their experiment	Learners do not carry out all necessary test-trials.	16	100	1.0	1.0
Data analysis and drawing conclusions
DC1	Confusing between observation and interpretation of data	When learners are asked to describe their observations, they interpret the results and when they are asked to interpret the results, they describe their observations.	17	94.1	1.2	0.70
DC2*	Variability in measured data leads to uncertainty	Large sample sizes coming from repeated measurements lead to uncertainty in drawing conclusions.	15	93.3	1	0.83
DC3	Not calculating the arithmetic mean	In repeated measurements, learners choose the first and the last measured value or a measured value that is in between the highest and the lowest measured value.	17	100	1.1	0.87
DC4	Diagrams that are not suitable	When learners are required to create diagrams, they do not create them at all or the diagrams are not suitable for the interpretation of data.	17	94.1	1.0	0.85
DC5	Difficulties when reading graphs	Learners have difficulties comparing and interpreting graphs.	15	100	1.0	1.0
DC6	Drawing conclusions when the result shows no covariation is difficult	Learners struggle with drawing conclusions in the situation, when the independent variable has no influence on the dependent variable. They distort or reinterpret the data, or state that non-covariation results are inconclusive.	17	100	1.0	1.0
DC7	Confirmation bias	Learners seek for information confirming their hypothesis instead of trying to disconfirm their hypothesis.	17	88.2	1.2	0.65
DC8	Ignoring anomalous data	Learners ignore data that is inconsistent with their current belief and therefore make judgements consistent with prior beliefs, even when the evidence does not support those beliefs. Therefore, the hypothesis directs the interpretation of data.	17	94.1	1.0	0.85
DC9*	Anomalous data leads to the assumption that there are flaws in the experimental procedure	Anomalous data leads to the assumption that there are flaws in the experimental procedure. When in doubt, different outcomes on a duplicate trial are interpreted as consistent with an effect if that effect was expected, but interpreted as measurement error if an effect was not expected.	17	94.1	1.0	0.85
DC10	Drawing incorrect inferences	Learners draw incorrect inferences from cohesive experiments.	17	100	1.0	1.0
DC11	Neglecting the protocol leads to incorrect inferences	Learners who not review their notes make invalid inferences.	17	82.4	1.0	0.60
DC12	Learners make no exclusion inferences	Learners struggle to make exclusion inferences and inferences of indeterminacy. Example of an exclusion: Salt has no influence on fermentation. Example of an inference of indeterminacy: It is not sure that salt has an influence on fermentation.	17	82.4	1.0	0.60
DC13	Learners do not give up less advanced strategies when interpreting results	Learners make inferences that are consistent with prior beliefs, based on a single instance of covariation (or non-covariation), or based on one level of the causal factor.	16	100	1.1	0.94
DC14*	Not connecting hypotheses to previous results	Learners do not state hypotheses on the basis of data gathered and do not correct or adapt their hypothesis even if data contradicts it.	17	94.1	1.1	0.74
Multiple inquiry phases
M1	Not investigating rival hypotheses	Learners demonstrate the correctness of one hypothesis rather than setting up experiments to decide between rival hypotheses.	17	58.8 [3]	1.1	0.24
M2	Not being able to identify variables	Learners have difficulties understanding the concept of a variable. They cannot name the types of variables, state the number of variables or determine the specific variable in an experiment.	15	60.0 [3]	1.3	0.25
M3*	Changing hypotheses throughout the experiment	Learners spontaneously change the hypothesis throughout their experiment without changing the experimental setup.	17	70.6 [3]	1.1	0.37
M4	Trying out	Learners are not following a systematic approach when experimenting and therefore are not proceeding in a scientific way.	17	88.2 [3]	1.4	0.44
M5	Lack of documentation	Learners do not take notes about the experiment. If they write protocols, they are incomplete.	17	58.8 [3]	1.1	0.24
M6	No awareness of measurement error	Learners are not aware of the idea of measurement error.	17	50.0 [3, 4]	1.2	0.43
M7*	Not constructing tables	Learners do not organise their data in tables.	17	70.6 [4]	1.0	0.45
M8	Value of the results determines the seeking for evidence	The value of the outcome determines whether learners seek either disconfirming or confirming evidence. Example: When learners are faced with a negative experimental result, they will systematically search for the one thing they can change in order to change the negative result into a positive one.	16	68.8 [4]	1.1	0.38

Page, M. J., Moher, D., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., & McKenzie, J. E. (2021). PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. BMJ, 372(n160). https://doi.org/10.1136/bmj.n160

 Web of Science ®Google Scholar

Cuccio-Schirripa, S., & Steiner, H. E. (2000). Enhancement and analysis of science question level for middle school students. Journal of Research in Science Teaching, 37(2), 210–224. http://dx.doi.org/10.1002/(SICI)1098-2736(200002)37:2%3C210::AID-TEA7%3E3.0.CO;2-I

 Web of Science ®Google Scholar

Hofstein, A., Navon, O., Kipnis, M., & Mamlok-Naaman, R. (2005). Developing students’ ability to ask more and better questions resulting from inquiry-type chemistry laboratories. Journal of Research in Science Teaching, 42(7), 791–806. https://doi.org/10.1002/tea.20072

 Web of Science ®Google Scholar

Neber, H., & Anton, M. A. (2008a). Förderung präexperimenteller epistemischer Aktivitäten im Chemieunterricht. [Promoting pre-experimental epistemic activities in chemistry education. Zeitschrift für Pädagogische Psychologie, 22(2), 143–150. https://doi.org/10.1024/1010-0652.22.2.143

 Web of Science ®Google Scholar

Neber, H., & Anton, M. A. (2008b). Promoting Pre-experimental activities in high‐school chemistry: Focusing on the role of students’ epistemic questions. International Journal of Science Education, 30(13), 1801–1821. https://doi.org/10.1080/09500690701579546

 Web of Science ®Google Scholar

Hofstein, A., Shore, R., & Kipnis, M. (2004). Providing high school chemistry students with opportunities to develop learning skills in an inquiry-type laboratory: A case study. International Journal of Science Education, 26(1), 47–62. https://doi.org/10.1080/0950069032000070342

 Web of Science ®Google Scholar

Darus, F. B., & Saat, R. M. (2014). How Do Primary School Students Acquire the Skill of Making Hypothesis? The Malaysian Online Journal of Educational Science, 2(2), 20–26. https://files.eric.ed.gov/fulltext/EJ1086198.pdf

 Google Scholar

Njoo, M., & de Jong, T. (1993). Exploratory learning with a computer simulation for control theory: Learning processes and instructional support. Journal of Research in Science Teaching, 30, 821–844. https://doi.org/10.1002/tea.3660300803

 Web of Science ®Google Scholar

Zhai, J., Jocz, J. A., & Tan, A.-L. (2014). ‘Am I Like a scientist?’: Primary children’s images of doing science in school. International Journal of Science Education, 36(4), 553–574. https://doi.org/10.1080/09500693.2013.791958

 Web of Science ®Google Scholar

Park, J. (2006). Modelling analysis of students’ processes of generating scientific explanatory hypotheses. International Journal of Science Education, 28(5), 469–489. https://doi.org/10.1080/09500690500404540

 Web of Science ®Google Scholar

Valanides, N., Papageorgiou, M., & Angeli, C. (2013). Scientific investigations of elementary school children. Journal of Science Education and Technology, 23(1), 26–36. https://doi.org/10.1007/s10956-013-9448-6

 Web of Science ®Google Scholar

Kwon, Y.-J., Jeong, J.-S., & Park, Y.-B. (2006). Roles of abductive reasoning and prior belief in children’s generation of hypotheses about pendulum motion. Science & Education, 15(6), 643–656. https://doi.org/10.1007/s11191-004-6407-x

 Google Scholar

García-Carmona, A., Criado, A. M., & Cruz-Guzmán, M. (2017). Primary pre-service teachers’ skills in planning a guided scientific inquiry. Research in Science Education, 47(5), 989–1010. https://doi.org/10.1007/s11165-016-9536-8

 Web of Science ®Google Scholar

Kanari, Z., & Millar, R. (2004). Reasoning from data: How students collect and interpret data in science investigations. Journal of Research in Science Teaching, 41, 748–769. https://doi.org/10.1002/tea.20020

 Web of Science ®Google Scholar

Klahr, D., Triona, L. M., & Williams, C. (2007). Hands on what? The relative effectiveness of physical vs. virtual materials in an engineering design project by middle school students. Journal of Research in Science Teaching, 44, 183–203. https://doi.org/10.1002/tea.20152

 Web of Science ®Google Scholar

Schauble, L. (1990). Belief revision in children: The role of prior knowledge and strategies for generating evidence. Journal of Experimental Child Psychology, 49, 31–57. https://doi.org/10.1016/0022-0965(90)90048-D

 PubMed Web of Science ®Google Scholar

Schauble, L. (1996). The development of scientific reasoning in knowledge-rich contexts. Developmental Psychology, 32, 102–119. https://doi.org/10.1037/0012-1649.32.1.102

 Web of Science ®Google Scholar

Baur, A. (2018). Fehler, Fehlkonzepte und spezifische Vorgehensweisen von Schülerinnen und Schülern beim Experimentieren [Students' errors, misconceptions and specific approaches during experimentation. 24, 115–129. https://doi.org/10.1007/s40573-018-0078-7

 Google Scholar

Echevarria, M. (2003). Anomalies as a catalyst for middle school students’ knowledge construction and scientific reasoning during science inquiry. Journal of Educational Psychology, 95(2), 357–374. https://doi.org/10.1037/0022-0663.95.2.357

 Web of Science ®Google Scholar

Klahr, D., Fay, A., & Dunbar, K. (1993). Heuristics for scientific experimentation: A developmental study. Cognitive Psychology, 25, 111–146. https://doi.org/10.1006/cogp.1993.1003

 PubMed Web of Science ®Google Scholar

Penner, D. E., & Klahr, D. (1996b). When to trust the data: Further investigations of system error in a scientific reasoning task. Memory & Cognition, 24, 655–668. https://doi.org/10.3758/BF03201090

 PubMed Web of Science ®Google Scholar

Zimmerman, C., Raghavan, K., & Sartoris, M. L. (2003). The impact of the Mars curriculum on students’ ability to coordinate theory and evidence. International Journal of Science Education, 25, 1247–1271. https://doi.org/10.1080/0950069022000038303

 Web of Science ®Google Scholar

de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179–201. https://doi.org/10.3102/00346543068002179

 Web of Science ®Google Scholar

Zimmerman, C. (2007). The development of scientific thinking skills in elementary and middle school. Developmental Review, 27(2), 172–223. https://doi.org/10.1016/j.dr.2006.12.001

 Web of Science ®Google Scholar

Hammann, M., Phan, T. T. H., Ehmer, M., & Grimm, T. (2010). Assessing pupils’ skills in experimentation. Journal of Biological Education, 42(2), 66–72. https://doi.org/10.1080/00219266.2008.9656113

 Web of Science ®Google Scholar

Wu, H.-K., & Wu, C.-L. (2011). Exploring the development of fifth graders’ practical epistemologies and explanation skills in inquiry-based learning classrooms. Research in Science Education, 41(3), 319–340. https://doi.org/10.1007/s11165-010-9167-4

 Web of Science ®Google Scholar

Arnold, J., Kremer, K., & Mayer, J. (2014). Understanding students’ experiments—What kind of support do they need in inquiry tasks? International Journal of Science Education, 36(16), 2719–2749. https://doi.org/10.1080/09500693.2014.930209

 Web of Science ®Google Scholar

Chen, Z., & Klahr, D. (1999). All other things being equal: Acquisition and transfer of control of variables strategy. Child Development, 70(5), 1098–1120. https://doi.org/10.1111/1467-8624.00081

 PubMed Web of Science ®Google Scholar

Dasgupta, A. P., Anderson, T. R., & Pelaez, N. J. (2016). Development of the neuron assessment for measuring biology students’ use of experimental design concepts and representations. CBE Life Science Education, 15(2), 1–21. https://doi.org/10.1187/cbe.15-03-0077

 Web of Science ®Google Scholar

Erdosne Toth, E., Klahr, D., & Chen, Z. (2000). Bridging research and practice: A cognitively based classroom intervention for teaching experimentation skills to elementary school children. Cognition and Instruction, 18(4), 423–459. http://dx.doi.org/10.1207/S1532690XCI1804_1

 Web of Science ®Google Scholar

Garcia-Mila, M., Andersen, C., & Rojo, N. E. (2011). Elementary students’ laboratory record keeping during scientific inquiry. International Journal of Science Education, 33(7), 915–942. https://doi.org/10.1080/09500693.2010.48098

 Web of Science ®Google Scholar

van Joolingen, W. R., & de Jong, T. (1991). Supporting hypothesis generation by learners exploring an interactive computer simulation. Instructional Science, 20, 389–404. https://doi.org/10.1007/BF00116355

 Web of Science ®Google Scholar

Klahr, D., & Nigam, M. (2004). The equivalence of learning. Path in early science instruction. Psychological Science, 15(10), 661–667. https://doi.org/10.1002/tea.20152

 PubMed Web of Science ®Google Scholar

Kuhn, D. (2007). Reasoning about multiple variables: Control of variables is not the only challenge. Science Education, 91(5), 710–726. https://doi.org/10.1111/j.1467-9280.2005.01628

 Web of Science ®Google Scholar

Kuhn, D., & Dean, D. Jr. (2005). Is developing scientific thinking all about learning to control variables? Psychological Science, 16, 866–870. https://doi.org/10.1111/j.1467-9280.2005.01628

 PubMed Web of Science ®Google Scholar

Reimann, P. (1991). Detecting functional relations in a computerized discovery environment. Learning and Instruction, 1, 45–65. https://doi.org/10.1016/0959-4752(91)90018-4

 Google Scholar

Schauble, L., Glaser, R., Raghavan, K., & Reiner, M. (1991a). Causal models and experimentation strategies in scientific reasoning. The Journal of Learning Sciences, 1(2), 201–238. https://doi.org/10.1207/s15327809jls0102_3

 Google Scholar

Shute, V. J., & Glaser, R. (1990). A large-scale evaluation of an intelligent discovery world: Smithtown. Interactive Learning Environments, 1, 51–77. https://doi.org/10.1080/1049482900010104

 Google Scholar

Siegler, R. S., & Liebert, R. M. (1975). Acquisition of formal scientific reasoning by 10- and 13-year-olds: Designing a factorial experiment. Developmental Psychology, 11, 401–402. https://doi.org/10.1037/h0076579

 Web of Science ®Google Scholar

Tairab, H. H. (2015). Assessing students’ understanding of control of variables across three grade levels and gender. International Education Studies, 9(1), 44–54. https://doi.org/10.5539/IES.V9N1P44

 Google Scholar

Croker, S., & Buchanan, H. (2011). Scientific reasoning in a real-world context: The effect of prior belief and outcome on children’s hypothesis-testing strategies. The British Journal of Developmental Psychology, 29(3), 409–424. https://doi.org/10.1348/026151010X496906

 PubMed Web of Science ®Google Scholar

Kuhn, D., Garcia-Mila, M., Zohar, A., Andersen, A., White, C., Sheldon, H., Klahr, D., Carver, D., & Sharon, M. (1995). Strategies of knowledge acquisition. Monographs of the Society for Research in Child Development, 60, 1–128. https://doi.org/10.2307/1166059

 PubMed Web of Science ®Google Scholar

Kuhn, D., Schauble, L., & Garcia-Mila, M. (1992). Cross-domain development of scientific reasoning. Cognition and Instruction, 9, 285–327. https://doi.org/10.1207/s1532690xci0904_1

 Web of Science ®Google Scholar

Carey, S., Evans, R., Honda, M., Jay, E., & Unger, C. (1989). An experiment is when you try it and see if it works’: A study of grade 7 students’ understanding of the construction of scientific knowledge. International Journal of Science Education, 11, 514–529. https://doi.org/10.1080/0950069890110504

 Web of Science ®Google Scholar

Schauble, L., Klopfer, L. E., & Raghavan, K. (1991b). Students’ transition from an engineering model to a science model of experimentation. Journal of Research in Science Teaching, 28, 859–882. https://doi.org/10.1002/tea.3660280910

 Web of Science ®Google Scholar

Schauble, L., Glaser, R., Duschl, R. A., Schulze, S., & John, J. (1995). Students’ understanding of the objectives and procedures of experimentation in the science classroom. The Journal of the Learning Sciences, 4, 131–166. https://doi.org/10.1207/s15327809jls0402_1

 Web of Science ®Google Scholar

Tschirgi, J. E. (1980). Sensible reasoning: A hypothesis about hypotheses. Child Development, 51, 1–10. https://doi.org/10.2307/1129583

 Web of Science ®Google Scholar

White, B. Y. (1993). ThinkerTools: Causal models, conceptual change, and science education. Cognition and Instruction, 10, 1–100. https://doi.org/10.1207/s1532690xci1001_1

 Web of Science ®Google Scholar

Duggan, S., & Gott, R. (2000). Intermediate general national vocational qualification (GNVQ) Science: A missed opportunity for a focus on procedural understanding? Research in Science & Technological Education, 18(2), 201–214. https://doi.org/10.1080/713694978

 Google Scholar

Schrempp, I., & Sodian, B. (1999). Wissenschaftliches Denken im Grundschulalter: Die Fähigkeit zur Hypothesenprüfung und Evidenzevaluation im Kontext der Attribution von Leistungsergebnissen [Scientific thinking at primary school age: The ability to test hypotheses and evaluate evidence in the context of attributing performance outcomes]. Zeitschrift für Entwicklungspsychologie und Pädagogische Psychologie, 31(2), 67–77. https://doi.org/10.1026//0049-8637.31.2.67

 Web of Science ®Google Scholar

Boaventura, D., Faria, C., Chagas, I., & Galvão, C. (2013). Promoting science outdoor activities for elementary school children: Contributions from a research laboratory. International Journal of Science Education, 35(5), 796–814. https://doi.org/10.1080/09500693.2011.583292

 Web of Science ®Google Scholar

Masnick, A. M., & Morris, B. J. (2008). Investigating the development of data evaluation: The role of data characteristics. Child Development, 79(4), 1032–1048. https://doi.org/10.1207/S15327647JCD4,1-03

 PubMed Web of Science ®Google Scholar

Masnick, A. M., & Klahr, D. (2003). Error matters: An initial exploration of elementary school children’s understanding of experimental error. Journal of Cognition and Development, 4(1), 67–98. https://doi.org/10.1207/S15327647JCD4,1-03

 Web of Science ®Google Scholar

Croner, P. (2003). Developing critical thinking skills through the use of guided laboratory activities. The Science Education Review, 2(2), 1–13. https://files.eric.ed.gov/fulltext/EJ1058493.pdf

 Google Scholar

Durmaz, H., & Mutlu, S. (2017). The effect of an instructional intervention on elementary students’ science process skills. The Journal of Educational Research, 110(4), 433–445. https://doi.org/10.1080/00220671.2015.1118003

 Web of Science ®Google Scholar

Mokros, J. R., & Tinker, R. F. (1987). The impact of microcomputer based labs on children’s ability to interpret graphs. Journal of Research in Science Teaching, 24, 369–383. https://doi.org/10.1002/tea.3660240408

 Web of Science ®Google Scholar

Aoki, T. (1991). The relation between two kinds of U-shaped growth curves: Balance-scale and weight-addition tasks. The Journal of General Psychology, 118, 251–261. https://doi.org/10.1080/00221309.1991.9917784

 PubMed Web of Science ®Google Scholar

Dunbar, K. (1993). Concept discovery in a scientific domain. Cognitive Science, 17, 397–434. https://doi.org/10.1207/s15516709cog1703_3

 Web of Science ®Google Scholar

Quinn, J., & Alessi, S. (1994). The effects of simulation complexity and hypothesis generation strategy on learning. Journal of Research on Computing in Education, 27, 75–91. https://doi.org/10.1080/08886504.1994.10782117

 Google Scholar

Wason, P. C. (1960). On the failure to eliminate hypotheses in a conceptual task. Quarterly Journal of Experimental Psychology, 12(3), 129–140. https://doi.org/10.1080/17470216008416717

 Web of Science ®Google Scholar

Amsel, E., & Brock, S. (1996). The development of evidence evaluation skills. Cognitive Development, 11, 523–550. https://doi.org/10.1016/S0885-2014(96)90016-7

 Web of Science ®Google Scholar

Greenhoot, A. F., Semb, G., Colombo, J., & Schreiber, T. (2004). Prior beliefs and methodological concepts in scientific reasoning. Applied Cognitive Psychology, 18(2), 203–221. https://doi.org/10.1002/acp.959

 Web of Science ®Google Scholar

Wahser, I., & Sumfleth, E. (2008). Training experimenteller Arbeitsweisen zur Unterstützung kooperativer Kleingruppenarbeit im Fach Chemie. [Training of experimental working methods to support cooperative work in small groups in the subject chemistry]. Zeitschrift für Didaktik der Naturwissenschaften, 14, 219–241. https://archiv.ipn.uni-kiel.de/zfdn/pdf/14_012_Wahser_Sumfleth.pdf

 Google Scholar

Klahr, D., & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science, 12(1), 1–48. https://doi.org/10.1207/s15516709cog1201_1

 Web of Science ®Google Scholar

Penner, D. E., & Klahr, D. (1996a). The Interaction of Domain-Specific Knowledge and domain-general discovery strategies: A study with sinking objects. Child Development, 67(6), 2709–2727. https://doi.org/10.2307/1131748

 PubMed Web of Science ®Google Scholar

Ramnarain, U. (2012). Exploring the use of a cartoon as a learner scaffold in the planning of scientific investigations. Perspectives in Education, 30(2), 50–61. Retrieved July 9, 2021, from https://www.ajol.info/index.php/pie/article/view/81906

 Google Scholar

Garcia-Mila, M., & Andersen, C. (2007). Developmental change in notetaking during scientific inquiry. International Journal of Science Education, 29(8), 1035–1058. https://doi.org/10.1080/09500690600931103

 Web of Science ®Google Scholar