Self-assessment in student’s learning and developing teaching in geoinformatics – case of Geoportti self-assessment tool

Figures & data

Figure 1. Flowchart of the Geoportti self-assessment Tool from the users’ perspective. ¹Nine questions for the UEF geoinformatics advanced course (“Advanced”).

Figure 2. The spider graph results from the Geoportti self-assessment Tool for three exemplary students in the “geoinformatics advanced course” (UEF 2019) show the change in their self-assessed knowledge and understanding in nine subject topics during the course. Acronyms T1–9 refer to the nine subject topics of the self-assessment (Table 2). For example, the scores of student 1 had generally increased, except for subject topic T9 that had decreased by one unit. Note that, unlike a default self-assessment with eight subject topics, this assessment had nine topics. Also, note that the scales of the graphs vary based on the maximum score (4 or 5) of an individual evaluation, and display the actual output from the Tool.

Table 1. Details of the self-assessment datasets examined in this study.

Course	Course name acronym (self-assessment subject topics, cf. Table 2)	Level	University	Year	Self-assessment respondents			Research objective(s)
					N	Gender: male/female %	Main study field: geography/non-geography^a %
Introduction to Geoinformatics	Basic (1)	Bachelor	UTU	2020	73	16.4/79.5^b	45.2/54.8	2
Introduction to Geoinformatics and Cartography	Basic (1)	Bachelor	UEF	2020	59	30.5/69.5	42.4/57.6^c	1, 2, 3
Introduction to Geoinformatics and Cartography	Basic (1)	Bachelor	UEF	2021	62	30.6/69.4	53.2/46.8^d	1, 2, 3
Methods in Remote Sensing	Remote sensing (2)	Bachelor	UTU	2019	20	55.0/40.0^b	85.0/15.0	2
Methods in Remote Sensing	Remote sensing (2)	Bachelor	UTU	2020	34	29.4/70.6	61.8/38.2	2 (COVID-19 impact)
Geoinformatics advanced course	Advanced (3)	Bachelor	UEF	2019	31	32.3/67.7	29.0/71.0^d	1, 2, 3
Geoinformatics advanced course	Advanced (3)	Bachelor	UEF	2020	41	43.9/56.1	41.5/58.5	1, 2, 3
Participation, spatial planning and GIS	Participatory mapping (4)	Master	UTU	2019	13	30.8/69.2	100/0	2, 3
Participation, spatial planning and GIS	Participatory mapping (4)	Master	UTU	2021	11	54.5/45.5	81.8/18.2	2, 3

^aThe main study field of the students has been divided between Geography and non-geography. Note that Geography includes Physical, Environmental, and Human Geography; non-geography means subjects such as Forestry, Biology, Geology, Environmental politics, and Environmental Sciences.

^bRemaining students did not report their gender

^cNon-geographers include one student, whose main study field cannot be defined due to UEF’s online system cutting the study path names.

^dNon-geographers include three students, whose main study field cannot be defined due to UEF’s online system cutting the study path names

Table 2. The subject topics of the examined self-assessments in brief. See Appendix 1 for detailed descriptions.

	Title of self-assessment (course acronym)
Subject topic	1. Basics in geoinformatics^a (Basic)	2. Remote sensing of the environment^b (Remote sensing)	3. Geoinformatics advanced course^c (Advanced)	4. Participation, spatial planning and GIS (Participatory mapping)
T1	Role of geoinformatics in society	Physical basis of remote sensing	Data: attributes, databases, queries	Definitions and differences between terms
T2	Geospatial data structure, metadata, data quality	Remote sensing systems, data, resolutions	Data: digitization, data modification	Application fields and approaches
T3	Projections	Photogrammetry	Data: satellite navigation systems, RTK-GPS, remote sensing	Participation and planning
T4	Coordinate and height systems	Geometric and atmospheric corrections	GIS workflow and its sketching	Ethical issues
T5	Basics of remote sensing	Image processing methods	Geometric properties calculation, data classification	Tools and approaches for data collection
T6	Satellite navigation systems, GPS	Image classification methods	Neighbourhood and overlay analyses	Sampling approaches
T7	GIS software	Active and close-range remote sensing	Spatial interpolation theory and practice	Data management and protection
T8	Cartography, maps, data visualisation	Remote sensing applications	Network and cost-efficiency analyses, uncertainties, geographically weighted regression (GWR)	Spatial and non-spatial analysis methods
(T9)			Data visualisation, thematic maps

^aIn Finnish: Geoinformatiikan perusteet.

^bIn Finnish: Kaukokartoitusmenetelmät.

^cIn Finnish: Geoinformatiikan jatkokurssi.

Table 3. Change (the proportion of students with positive changes, no changes, or negative changes) in their self-assessed knowledge and understanding in geoinformatics subject topics between the initial and final self-assessments in the basic and advanced courses at UEF. See Table A1 for the frequencies of the self-assessment scores related to each subject topic in each course.

	Positive (%)	No change (%)	Negative (%)
Basic 2020	81.4	14.6	4.0
Basic 2021	85.4	13.7	1.2
Advanced 2019	74.2	21.5	4.3
Advanced 2020	77.2	17.9	4.9

Table 4. The subject topic where the students had the highest and lowest self-assessed knowledge and understanding in the initial and final assessments in the Basic and Advanced courses at UEF. The acronyms of the subject topic are explained in Table 2 (i.e. Tn). Each self-assessment subject topic is mentioned briefly when first mentioned.

	Subject topics specific self-assessed knowledge and understanding
Course	Initial: Highest/lowest	Final: Highest/lowest	Most increase	Least increase
Basic 2020	T8 (Cartography, maps, data visualisation)/T5 (Basics of remote sensing)	T8/T5	T2 (Geospatial data structure, metadata, data quality)	T1 (Role of geoinformatics in the society)
Basic 2021	T8/T5	T8/T5	T7 (GIS software)	T1 and T3 (Projections)
Advanced 2019	T9 (Data visualisation, thematic maps)/T4 (GIS workflow and its sketching)	T9/T6 (Neighbourhood and overlay analyses)	T4	T9
Advanced 2020	T9/T8 (Network and cost-efficiency analyses, uncertainties, GWR)	T9/T6	T8	T9

Figure 3. Students’ self-assessed median knowledge and understanding (1 = low, 5 = high) at the end of a course across all course subject topics.

Figure 4. Distribution of self-assessments scores at the end of a course across the course subject topics. See Table 2 for the description of course subject topics.

Figure 5. Individual categories denoting the impacts of utilising the Geoportti self-assessment Tool on studying and learning processes as perceived by the students. A response from one student (n = 206) could link to several impacts. In total, the various impacts were mentioned 285 times for studying, and 262 times for learning (100%).

Table

Acronym	Subject topics and description
T1	The role of geoinformatics in society: I am familiar with the different societal applications of geoinformatics, and know how geospatial methods enable the study of phenomena that are relevant to society. I can identify the key national and international data producers, and the downloading and map services.
T2	Structure of geospatial data, metadata and data quality: I am familiar with the structure of geospatial data (vector and raster), and know their strengths and weaknesses in different applications. I am familiar with the concepts of metadata and data quality, and know how to evaluate their quality.
T3	Projections: I understand how the surfaces of the earth transforms into a plane surface by using different map projections (plane, cylinder and cone). I know how different projections preserve different metric properties (conformal, equal-area, equidistant), and how to evaluate their suitability for different-use cases.
T4	Coordinate and height systems: I am familiar with the characteristics of different coordinate systems (for example, geographic and plane coordinates), and the key concepts related to coordinate systems, such as datum and referenced ellipsoid. I know how to read coordinates from different systems, and know which systems, including the height system, are in use in Finland.
T5	Basics of remote sensing: I am familiar with the basics of remote sensing, including the principles of active and passive remote sensing, the concept of resolution (spatial, temporal, radiometric and spectral), and what is meant by channels and how they are connected to remote sensing images and their interpretation. In addition, I know how these methods produce geospatial data, including how to georeference a satellite image.
T6	Satellite navigation systems and GPS: I know the basic functionalities of a satellite navigation system and the possible sources of error. I know how to produce geospatial data with GPS locators, and how to analyse and modify the collected data in the GIS desktop software.
T7	GIS software: I am familiar with the most popular GIS software and know their basic functionalities. I know how to use a software of my choice for viewing data, data management, and basic analyses.
T8	Cartographic communication, maps and data visualisation: I know what elements are within a good map, how to communicate with a map, and what different types of maps there are. I know how to select suitable symbology, colours, and scale among other elements that serve my purpose, and I know how data attributes (quantitative vs qualitative, continuous vs classified etc.) affect geovisualization.

Table

Acronym	Subject topics and description
T1	Physical basis of remote sensing: I can define remote sensing and explain what remote sensing consists of. I know the physical basis of remote sensing, i.e. the spectrum of electromagnetic radiation and how radiation interacts with different surfaces and the atmosphere. I am familiar with the general characteristics of analog, digital, panchromatic, and multispectral remote sensing imagery.
T2	Remote sensing systems, data and resolutions: I know the components of a remote sensing system. I am familiar with the most important remote sensing platforms, sensors and datasets. I know how to examine differences in remote sensing systems, mainly from the perspective of the four resolution types.
T3	Photogrammetry: I understand how the implementation of a remote sensing campaign and the imaging geometry influence geometric distortions in different parts of the image. I’m aware of how image geometry influences the use of remote sensing data, and how geometric distortions can be corrected. I understand how geometric distortions can be used to calculate new information on ground features.
T4	Geometric and atmospheric corrections: I understand why geometric and atmospheric corrections are needed and the general principles of the methods. I know how to find out whether my data has been geometrically and atmospherically corrected.
T5	Image processing methods: I understand why radiometric, spectral, and spatial image processing methods are useful, and their impact on multispectral data and the produced image. I am familiar with the basic concepts and most common methods. I know how to interpret remote sensing imagery using a systematic visual image interpretation approach.
T6	Image classification methods: I am familiar with the benefits of classifying remote sensing images. I know the most important classification methods, their differences and applicability to different purposes. I know how to evaluate the quality of the classification using visual and statistical approaches.
T7	Active and close-range remote sensing: I understand the principles and differences of passive and active remote sensing. I know the principles of laser scanning and how the laser scanning systems and datasets differ. I know what a point cloud looks like, and how it can be processed and analysed.
T8	Remote sensing applications: I am familiar with the applications of remote sensing in research and in society. I understand the relationship of remote sensing as a discipline to other applied sciences, such as social and environmental science.

Table

Acronym	Subject topics and description
T1	The data control and production (attributes, databases and queries): I know how spatial data is stored and managed in a geodatabase, including doing basic queries using the SQL-language. I understand why metadata is important, and how to create and interpret it.
T2	The data control and production (digitization and data modification): I know how to create a vector dataset, digitise new features into it, and modify the spatial data and its attributes if necessary. This process includes the ability to evaluate the overall quality of the data and skills to improve it when necessary.
T3	The data control and production: satellite navigation systems, RTK-GPS and remote sensing (incl. Laser scanning): I know the basic functionalities of a satellite navigation system, basic and high-precision GPS locators, and the possible sources of error in these systems. I know how to produce geospatial data with the GPS locators, and what the pros and cons are for the different systems. I know the different opportunities for data collection through remote sensing methods.
T4	GIS workflow and its sketching: I know how a chain of geospatial analysis steps can be described as a GIS workflow. I also recognize the benefits of formulating the work in this way. This understanding includes the principles of workflow chart symbology and structure, as well as the capacity to produce a GIS workflow from my own work. I know how to apply these skills by using ModelBuilder and its options to iterate models.
T5	Calculation of geometric properties and classification of data: I know how to analyse vector and raster datasets based on the location, size, shape, and geometry, and how to classify these datasets based on location and/or attributed information. I know the main differences in area calculations between the vector and raster datasets.
T6	Neighbourhood and overlay analyses: I know how to analyse geospatial datasets based on the neighbourhood, distance or overlap, and how these analyses differ between the vector and raster data sets. I can estimate when the original datasets would be good to be pre-processed and/or harmonised before the analyses (e.g. conversion from raster to vector or vice versa, generalisation of the data).
T7	Spatial interpolation in theory and in practice: I know how to interpolate spatially sampled data to create spatially continuous maps. This skill includes the capacity to evaluate the character of the data sample in relation to the data as a whole, and how this affects the results and interpretation of the interpolation.
T8	Network and cost-efficiency analyses, uncertainties, and geographically weighted regression: I know how to use open spatial data sets to perform network and cost-efficiency analyses. I understand the importance of geographical location, uncertainties within the spatial data, and the quality of the data on the results of the analyses. In addition, I understand the effects that spatial sampling can have on the analysis results.
T9	Data visualisation data and designing thematic maps: I know how the attributes of the data in question (quantitative vs qualitative, continuous vs classified) and the potential analysis method (e.g. interpolation) should be considered in geovisualization. After designing a suitable visualisation, I know how to select symbology, map elements, colours, and scale that serve my purpose in thematic maps.

Table

Acronym	Subject topics and description
T1	Definitions and differences between terms: I know the definitions and differences between the terms PGIS, PPGIS, VGI, and citizen science.
T2	Application fields and approaches: I know in which fields and with what kind of approaches participatory mapping is applied in practice (including e.g. urban and regional development, health and recreation services, landscape and natural resource management).
T3	Participation and planning: I am aware of the different levels of participation in planning, and can identify examples of how participatory mapping (i.e. place-based participation) has been applied in planning practice.
T4	Ethical issues: I am aware of the ethical issues related to participatory mapping approaches.
T5	Tools and approaches for data collection: I know the different tools and approaches used for data collection in participatory mapping, and the elements of a good quality map-based survey in web-based, mobile, and offline approaches.
T6	Sampling approaches: I know the principles of different sampling approaches for data collection (e.g. random or purposive vs. crowdsourced/volunteer sampling).
T7	Data management and protection: I am aware of the data management and protection issues (e.g. The EU General Data Protection Regulation (GDPR) related to participatory mapping data collection and storage.
T8	Spatial and non-spatial analysis methods: I know the spatial and non-spatial analysis methods to explore, explain, and predict/model participatory mapping data.

Table A.1. The frequencies of the self-assessment scores related to each subject topic given in the Basic and Advanced courses at UEF. The largest frequencies are colored. The comparison of scores between the initial and final self-assessments shows the negative, no change (same value in initial and final self-assessment), or positive change in the self-assessed knowledge and understanding between the initial and final self-assessments.

Basic 2020	Self-assessment score	T1	T2	T3	T4	T5	T6	T7	T8
Initial self-assessment	0	2	16	4	9	21	8	12	2
	1	8	13	18	17	17	18	13	7
	2	19	9	13	18	12	10	13	14
	3	19	12	11	9	6	18	12	11
	4	10	8	11	6	2	3	8	20
	5	1	1	2	0	1	2	1	5
Final self-assessment	0	0	0	0	0	0	0	1	0
	1	0	2	0	1	6	1	0	0
	2	3	3	5	6	10	9	5	0
	3	8	15	15	21	24	19	15	3
	4	37	33	29	23	16	19	26	30
	5	11	6	10	8	3	11	12	26
change	final - initial
	-2	0	0	0	0	0	1	1	0
4.0 % negative	-1	2	0	4	1	2	2	2	4
14.6 % no change	0	7	12	9	5	5	11	9	11
81.4 % positive	1	25	12	18	19	18	13	11	17
	2	16	14	13	21	16	15	16	13
	3	6	13	10	9	15	14	12	9
	4	3	8	5	3	3	3	8	5
	5	0	0	0	1	0	0	0	0
Basic 2021	Self-assessment score	T1	T2	T3	T4	T5	T6	T7	T8
Initial self-assessment	0	3	14	10	13	24	16	18	4
	1	8	15	7	19	18	11	13	6
	2	21	16	14	23	11	25	14	10
	3	22	12	19	5	4	6	10	15
	4	7	5	12	2	3	4	6	25
	5	1	0	0	0	2	0	1	2
Final self-assessment	0	0	0	0	0	1	0	0	0
	1	0	0	0	1	1	1	0	0
	2	2	3	3	8	19	8	5	0
	3	13	24	18	26	22	23	15	5
	4	39	25	35	25	16	23	32	27
	5	8	10	6	2	3	7	10	30
change	final - initial
	-2	0	0	0	0	0	0	0	0
1.2 % negative	-1	0	0	1	0	1	1	3	0
13.7 % no change	0	8	8	12	8	8	9	3	12
85.1 % positive	1	28	14	22	14	13	17	12	25
	2	17	19	17	19	26	13	18	14
	3	8	13	5	19	10	14	17	6
	4	1	6	5	2	4	6	8	4
	5	0	2	0	0	0	2	1	1
Advanced 2019	Self-assessment score	T1	T2	T3	T4	T5	T6	T7	T8	T9
Initial self-assessment	0	1	0	0	10	2	3	7	4	0
	1	6	8	4	9	6	7	10	9	3
	2	16	8	11	7	9	12	9	7	0
	3	2	12	12	4	7	7	4	9	12
	4	5	2	4	1	7	2	1	2	10
	5	1	1	0	0	0	0	0	0	6
Final self-assessment	0	0	0	0	0	0	0	0	0	0
	1	0	1	1	0	1	1	1	1	0
	2	3	1	6	5	2	6	2	3	1
	3	7	15	9	10	12	11	12	8	7
	4	17	11	15	14	11	12	14	18	13
	5	4	3	0	2	5	1	2	1	10
change	final - initial
	-2	0	0	1	0	0	0	0	0	0
4.3 % negative	-1	0	1	1	1	3	1	0	2	2
21.5 % no change	0	5	7	9	2	8	8	3	3	15
74.2 % positive	1	9	13	15	4	5	9	7	9	10
	2	14	8	5	13	10	9	11	9	4
	3	3	2	0	7	5	3	7	7	0
	4	0	0	0	3	0	1	2	1	0
	5	0	0	0	1	0	0	1	0	0
Advanced 2020	Self-assessment score	T1	T2	T3	T4	T5	T6	T7	T8	T9
Initial self-assessment	0	1	0	2	9	0	4	8	7	0
	1	11	8	6	16	10	11	19	16	1
	2	11	15	17	9	12	11	5	14	4
	3	10	12	11	2	8	12	7	4	16
	4	6	5	5	4	8	3	1	0	15
	5	2	1	0	1	3	0	1	0	5
Final self-assessment	0	0	0	0	0	1	1	0	0	0
	1	1	2	1	3	3	3	2	1	0
	2	2	3	2	6	1	4	4	3	3
	3	10	15	15	12	13	15	9	12	3
	4	19	17	15	14	14	15	21	23	24
	5	9	4	8	6	9	3	5	2	11
change	final - initial
	-2	0	1	0	0	1	1	0	0	2
4.9 % negative	-1	0	2	1	1	2	2	3	1	1
17.9 % no change	0	8	9	9	4	11	5	3	2	15
77.2 % positive	1	14	15	10	12	14	16	5	8	18
	2	13	11	15	13	9	13	9	12	4
	3	5	3	6	6	3	4	17	15	1
	4	1	0	0	4	1	0	4	2	0
	5	0	0	0	1	0	0	0	1	0

Table A2. The percentages (as a percentage of all students) of gender and main study field categories among students with positive, no, and negative change trends between the initial and final self-assessments. See the n values for respondents from Table 1.

	Positive change		No change		Negative change
Gender	Female	Male	Female	Male	Female	Male
Basic 2020	70.3	29.7	62.3	37.7	78.9	21.1
Basic 2021	68.2	31.8	67.6	32.4	33.3	66.7
Advanced 2019	66.2	33.8	71.7	28.3	75.0	25.0
Advanced 2020	58.6	41.4	37.9	62.1	83.3	16.7
Main study field	Geography	Non-geography	Geography	Non-geography	Geography	Non-geography
Basic 2020	38.3	61.7	56.5	43.5	73.7	26.3
Basic 2021	51.7	48.3	64.7	35.3	33.3	66.7
Advanced 2019	32.4	67.6	20.0	80.0	16.7	83.3
Advanced 2020	38.6	61.4	48.5	51.5	61.1	38.9

Table A3. Differences (percentages) between the student’s background and the perceived impact of utilising the Geoportti self-assessment tool on studying and learning, which is distinguished based on the “no or negative” impacts and positive impacts. Statistically significant differences between groups were tested through the Pearson chi square test (the significant result is bolded).

		No or negative impact					Positive impact
		Not mentioned	1 impact	2+ impacts	Total	Pearson Chi-square	Not mentioned	1 impact	2+ impacts	Total	Pearson Chi-square
Studying
Gender (n = 203)	Male	29.7	55.4	14.9	100.0	(χ2(2) = 3.342, p = 0.188)	70.3	24.3	5.4	100.0	(χ2(2) = 14.253, p < 0.001)***
	Female	42.6	45.7	11.6	100.0		48.1	27.1	24.8	100.0
Main study field (n = 202)	Geography	37.8	46.9	15.3	100.0	(χ2(2) = 1.050, p = 0.592)	54.1	23.5	22.4	100.0	(χ2(2) = 2.960, p = 0.228)
	Non-geography	38.5	51.0	10.6	100.0		57.7	28.8	13.5	100.0
Level of education (n = 200)	Bachelor	37.6	50.9	11.6	100.0	(χ2(2) = 1.450, p = 0.484)	57.8	26.6	15.6	100.0	(χ2(2) = 4.993, p = 0.082)
	Master	40.7	40.7	18.5	100.0		44.4	22.2	33.3	100.0
Learning
Gender (n = 203)	Male	31.1	68.9	0.0	100.0	(χ2(2) = 2.654, p = 0.103)	55.4	35.1	9.5	100.0	(χ2(2) = 2.673, p = 0.263)
	Female	42.6	57.4	0.0	100.0		44.2	41.1	14.7	100.0
Main study field (n = 202)	Geography	39.8	60.2	0.0	100.0	(χ2(2) = 0.227, p = 0.634)	49.0	35.7	15.3	100.0	(χ2(2) = 1.300, p = 0.522)
	Non-geography	36.5	63.5	0.0	100.0		48.1	41.3	10.6	100.0
Level of education (n = 200)	Bachelor	35.8	64.2	0.0	100.0	(χ2(2) = 2.542, p = 0.111)	50.9	37.6	11.6	100.0	(χ2(2) = 4.906, p = 0.086)
	Master	51.9	48.1	0.0	100.0		29.6	48.1	22.2	100.0

Data availability statement

The data supporting the analysis presented in this article is available in the Supplementary Material. However, the UEF data regarding initial and final self-assessment results is not published due to the potential identification of individual student responses.