ANOVA and Repeated Measures ANOVA Designs Use the University Online Resources to find two peer-reviewed articles in which the authors used ANOVA des

Behavior Research Methods & Instrumentation

1980, Vol. 12 (5), 559-561

Capability of SPSS subprogram ANDVA to handle repeated-measures and nested designs

DONALD B. HEADLEY United States Army Biomedical Laboratory, Aberdeen

Proving Ground, Maryland 21010

Although subprogram ANOVA of Statistical Package for the Social Sciences (SPSS; Nie, Hull, Jenkins, Steinbrenner, & Bent, 1975) is basically a factorial design program, its computing capabilities may be readily extended to repeated-measures and nested designs. Advantage may be taken of ANOVA's capability to perform multiple analyses in a single run. The strategy is to request the appropriate analyses to obtain the proper partitioning of sums of squares (SS). The SS for main effects and interactions are obtained in the usual manner in the first analysis. The additional analyses function to divide the residual SS from the first analysis into SS for the various error terms in a given design.

A data record consists of a datum along with its identifying levels of all factors, including a dummy subjects factor (the factors are listed on a VARIABLE LIST card; the ordering of level numbers on the data record is in accordance with the position of its factor name on this control card). The total number of data records is entered on the N OF CASES control card. The major limitation that prevents obtaining a complete analysis with just one ANOVA statement is that the multiplicative value of the number of factor levels in a given analysis must be less than the total number of data records. This restriction prohibits all the indepen- dent variables listed on the VARIABLE LIST control card to be used in the same analysis if there is one datum per cell (subject-treatments combination). With equal numbers of subjects per group, the multiplicative value of levels must be a whole number multiple of N OF CASES.

The procedures for obtaining the proper partitioning of total SS for repeated-measures designs are shown in Table 1. Both simple and mixed designs are illustrated

Requests for reprints should be sent to Donald B. Headley, Behavioral Toxicology Branch, U.S.A. Biomedical Laboratory, Attention: SGRD-UV-RB, Aberdeen Proving Ground, Maryland 21010. The opinions or assertions contained herein are the private views of the author and are not to be construed as official or as reflecting the views of the Army or the Depart- ment of Defense.

(tor examples of these designs, see Myers, 1972; Winer, 1971). The number of analyses to be requested is equal to the number of within-subjects factors plus one. Many terms of the analysis table are obtained by sub- traction (the degrees of freedom for these terms are likewise determined by subtraction). The values placed in parentheses on the ANOVA procedure card are the ranges of the codes used to define levels of factors. If one has multiple entries in each cell (e.g., multiple determinations from a blood sample) and does not want to analyze these values as a separate factor, no additional individual coding is required; each datum is properly identified by its cell coding. The multiple entries per cell contribute to a within-subjects error term and its corresponding degrees of freedom. ANOVA can also compute SS for designs that have unequal (proportionate or disproportionate) numbers of subjects per group. The user must assume, however, that data for a given subject are present in all levels of the within- subjects factors. No control card adjustments from the equal-number case are necessary. The levels for subjects on the ANOVA statement are the number in the largest group. The default computations for the unequal-number case are in accordance with a least-squares solution (Method 2 described in Overall & Spiegel, 1969; for examples of this method, see Kirk, 1968, pp. 276-281; Winer, 1971, pp. 599-603).

ANOVA statements for three nested designs are shown in Table 2 (see Myers, 1972, for examples). Unless a given design has a repeated-measures variable, a subjects factor need not be coded on data records or listed on the VARIABLE LIST or ANOVA cards. The number of groups within treatments and/or subjects within groups need not be equal.

The requesting of specific analyses on a given data set thus allows the ANOVA factorial program to func- tion as a more general-purpose computational aid.

REFERENCES

KIRK, R. E. Experimental design: Procedures for the behavioral sciences. Belmont, Calif: Brooks/Cole, 1968.

MYERS, J. L. Fundamentals of experimental design (2nd ed.). Boston: Allyn & Bacon. 1972.

NIE, N. H., HULL, C. H., JENKINS, J. G., STEINBRENNER, K., & BENT, D. H. SPSS: Statistical package for the social sciences (2nd ed.). New York: McGraw-Hili, 1975.

OVERALL, J. E., & SPIEGEL, D. K. Concerning least squares analysis of experimental data. Psychological Bulletin, 1969,72, 311-322.

WINER, B. J. Statistical principles in experimental design (2nd ed.). New York: McGraw-Hili, 1971.

Copyright 1980 Psychonomic Society, Inc. 559 0005-7878/80/050559-03$00.55/0

560 HEADLEY

Table I Procedure Dud Coding and Output fOJ Repeated-Measures Designs

Number of Variables' ANOVA Procedure Sums-of-Squares Terms

B W Card Statements from Output F Ratios"

0 DV BY W(1,3)/ (I)R (2)W 2/4 DV BY S(1,5)/ (3)5 (4)WxS=I-3

0 2 DV BY Wl(I,3) W2(1,3)/ (l)R (2)Wl (3)W2 2/6; (4)WlxW2 3/7;

DV BY Wl(I,3) 5(1,5)/ (5)S (6)WlxS 4/8 DV BY W2(1,3) 5(1,5)/ (7)W2xS

(&)WlxW2xS=I-5-6-7 DV BY BO ,2) W(l,3)/ (I)R (2)B (3)W 2/6 ;

(4)BxW 3/7, DV BY S(l,1 0)/ (5)5 4/7

(6)S w/in B=5-2 (7)WxS w/in B=I-6

2 DV BY B(1,2) wiu,n W2(1,3)/ (1)R (2)B (3)Wl 2/ L1; (4)W2 (5)BxWI 3/ u, (6)BxW2 (7)WlxW2 5/Ll; (8)BlxWlxW2 4/14;

DV BY Wl(I,3) S(1,10)/ (9)5 (10)WlxS 6/14; (11)5 w/in B=9-2 7/15 ; (l2)WlxS w/in B=10-5 8/15

DV BY W2(1,3) S(1,10)/ (l3)W2xS (14)W2xS w/in B=13-6 (lS)WlxW2xS w/in B=1

-11-12~14

2 DV BY B1(1,2) B2(l,2) W(l,3)/ (l)R (2)Bl (3)B2 2/10; (4)W (5)BlxB2 3/10; (6)BlxW (7)B2xW S/W; (8)BlxB2xW 4/11;

DV BY 5(1,20)/ (9)5 6/11; (10)S w/in BIB2=9-2- 7/11;

3-5 (ll)WxS w/in BIB2=1-10 8/11

2 2 DV BY B1(1,2) B2(1,2) Wl(1,3) W2(1,3)/ (1)R (2)Bl (3)B2 2/19; (4)BlxB2 (5)Wl (6)W2 3/19; (7)WlxW2 (8)BlxWI 4/19; (9)BlxW2 (10)B2xWI 5/20; (I1)B2xW2 (l2)BlxB2xWI &/20; (l3)BlxB2xW2 10/20; (l4)BlxWlxW2 12/20; (l5)B2xWlxW2 6/22; (l6)BlxB2xWlxW2 9/22;

DV BY WL(1,3) S(1,20)/ (17)S (l8)WlxS 11/22; (L9)S w/in BIB2=17-2 L3/22;

-3-4 1/23; (20)WlxS w/in BIB2= 14/23;

18-8-10-12 15/23; DV BY W2(l,3) 5(1,20)/ (2l)W2xS (22)W2xS 16/23

w/in BIB2=21-9-11-13 (23)WlxW2xS w/in BIB2

=1-19-20-22

Note-B = between-subjects factor. W = within-subjects factor; D V = dependent variable, R = residual; S = subjects. *AIIume two levels per between-subjects factor and five Iii bjects per group; assume three levels per within-subjects factor. **Use mean squares ofindicated terms.

REPEATED MEASURES AND NESTED DESIGNS FROM SPSS's ANOVA 561

Table 2 Procedure Card Coding and Output for Nested Designs

Design

Groups within treatments*

Groups within treatments with a variable within groups**

Groups within treatments with a variable within groups and repeated measures within this variablej

ANOVA Procedure Card Statements

DV BY T(l,2)1 DV BY G(l,6)1

DV BY TO ,2) W10 ,2)1 DV BY GO ,6) W10 ,2)1

DV BY TO,2) W10 ,2) W20,3)1

DV BY G(l,6) W1(l,2) W2(l,3)1

DV BY S(1,60)1

Sums-of-Squares Terms from Output

(l)R (2)T (3)G (4)G w/in T=3-2 (5)S wlin G w/in T=1-4 (1)R (2)T (3)W1 (4)TxWl (5)G (6)GxWl (7)G w/in T=5-2 (8)GxWI w/in T=6-4 (9)S wlin GxWI w/in T=I-7-8 (l)R (2)T (3)W1 (4)W2 (5)TxWl (6)TxW2 (7)W1xW2 (8)TxW1xW2 (9)G (l O)GxWI Ol)GxW2 (l2)GxW1xW2 (l3)G w/in T=9-2 (l4)GxWl wlin T=10-5 (l5)GxW2 w/in T=11-6 (16)GxW1xW2 w/in T=12-8 (17)S (18)S w/in GxWl wlin

T=17-9-3-5-14 (19)SxW2 w/in GxWl w/in T=

1-13-14-15-16-18

F Ratiostt

2/4; 4/5

2/7; 3/8; 4/8; 8/9

2/13; 3/14; 5/14; 14/18; 4115; 6/15; 15/16; 7/16; 8/16; 16/19

Note-DV = dependent variable, R = residual; G = groups, S = subjects, T = treatments; Wi = within-groups variable, W2 x: within- subjects variable. *Assume two levels for treatments and three groups per treatment level. **Assume two levels for the variable within groups. [Assume three levels for the within-subjects variableand five subjects per group. ttUse mean squares of indicated terms.

(Accepted for publication July 2, 1980.)

OPEN ACCESS

CONTEMPORARY EDUCATIONAL TECHNOLOGY

ISSN: 1309-517X (Online) 2021, 13(1), ep282, https://doi.org/10.30935/cedtech/8707

Research Article

Copyright © 2021 by the authors; licensee CEDTECH by Bastas. This articles is published under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).

Examining the Use Self-perceived by University Teachers about ICT Resources: Measurement and Comparative Analysis in a One-way

ANOVA Design

Francisco David Guillén-Gámez Department of Research and Diagnostic Methods in Education, Faculty of Education, University of Zaragoza

(UNIZAR), Spain ORCID: 0000-0001-6470-526X

Maria Jose Mayorga-Fernández Department of Didactics and School Organization, Faculty of Sciences of Education, University of Málaga

(UMA), Spain ORCID: 0000-0003-3749-1264

Marta Ramos Department of Developmental and Educational Psychology. University of Salamanca (USAL), Spain

ORCID: 0000-0002-9643-6495

Received: 3 Aug 2020 Accepted: 4 Sep 2020

Abstract

The growing rise of information and communication technologies (ICT) in all areas of society demands that university professors have an adequate level of digital literacy, so that they can contribute effectively to the training of their students and respond to the demands of the job. The objective of this research is to know and compare the use by university teachers of different ICT resources, in their teaching, evaluation, and research (UTIC-EEI model, its acronym in Spanish), depending on the area of knowledge to which they belong (science and engineering-architecture, health sciences, art-humanities, and social- legal Sciences), in order to be able to take measures to effectively address the digital shortcomings of teachers. An ex post facto study is carried out, with a quantitative methodology utilising a survey technique, with a sample of 867 Spanish university teachers, with a descriptive and inferential analysis via ANOVA for multiple comparisons. The results showed a medium-high use by teachers of ICT resources in four areas, with there being a superior use in the teaching and research dimensions compared to the evaluation dimension in each area of knowledge. These data underline the need to continue training teachers to make excellent instrumental use of specific ICT resources in each area of knowledge.

Keywords: digital literacy, teachers, ICT, educational use, ANOVA

INTRODUCTION

The ongoing evolution in the Spanish educational field is unquestionably the result of the influence of new information and communication technologies (ICT) (Gorghiu, Gorghiu, & Pascale, 2018), since they constitute a key component in the development of the teaching and learning process, thus promoting “great opportunities to improve the quality, accessibility and equity of education” (European Commission, 2012, p. 10). In this reality, higher education institutions whose purpose, according to the guidelines of the European Higher Education Area (EHEA), is to train professionally competent school-leavers to enter and be effective

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in the 21st century labour market (Fernández-Márquez, Leiva-Olivencia, & López-Meneses, 2018). As affirmed by Carrera & Coiduras (2012), the incorporation of ICT in higher education has led to changes in the teaching methods of university teachers, due to the emergence of both technological resources and learning management system (LMS). In this situation, teachers play an important role as change agents (Prendes, Gutiérrez, & Martínez, 2018), while students represent the heart of the learning process (Adetimirin, 2019). Therefore, the success of the integration of ICTs in teaching and learning processes will largely depend on the teachers’ skills to implement these technologies in an optimal way (Hernández, 2017; Lai, 2019), given that the use of technology in the teaching process can have a positive impact on students’ learning (Bingimlas, 2009; DiVall et al., 2013; Karpudewan & Balasundram, 2019). However, as indicated by López, Sánchez, Arámbula, and Esquivel (2019), such use of technology is taking place regarding the method, but is not causing fundamental transformations.

A challenge may arise in the case where teachers have not received sufficient digital training, through innovative teaching methodologies focused on pedagogical-technological issues in the context of higher education institutions, aimed to meet the demands emerging in their practice (Adetimirin, 2019; Cela-Ranilla et al., 2017), so highlighting they have on a low level of ICT skills (Arancibia, Valdivia, Araneda, & Cabero, 2017; Dzikite, Nsubuga, & Nkonki, 2017; Guillén-Gámez, Mayorga-Fernández, & Del Moral, 2020a; Mercader, 2019; Ojeniyi & Adetimirin, 2016; Prendes & Castañeda, 2010). This limited training is partly attributable to the fact that training has focused on issues related to technical-instrumental management and not on integrating ICT into didactic-curricular practice (Llorente, 2008). Furthermore, Gutiérrez, Torres, and Sánchez-Beato (2016) analysed teaching guides of the study plans of three Spanish universities, and found that ICT were hardly present. Furthermore, Sahin and Thompson (2006) discovered that while technology is used in the field of administration and research, it is rarely used for teaching purposes because its inclusion poses challenges to the methods and mental abilities of the teaching staff. However, Díaz, Hernández, and Berea (2016), and Capilla, Torres, and Sánchez (2016) highlight that teachers do not dislike the didactic integration of ICT in their educational processes; on the contrary, they recognise its pedagogical potential. Arguably, the problem lies in “the laboriousness involved in the teaching planning of the different educational levels when employing specific digital resources to improve learning processes in each of the knowledge disciplines” (Carvalho, Tejada, & Pérez, 2019, p. 73).

In the scientific literature, various authors (Guillén-Gámez, Mayorga-Fernández, Bravo-Agapito, & Escribano- Ortiz, 2020b; Hatlevik & Christophersen, 2013; Janssen et al., 2013) have sought to clarify the concept of digital literacy, a complex task, since there are multiple forms of understanding and naming it, among them: digital literacy, digital alphabetising, media literacy, digital skills, or internet skills. Digital literacy can be defined as the technological knowledge, practical use, and attitudes encouraging the critical, responsible, and creative use of ICT for different purposes (Padilla-Hernández & Vanesa, 2018). In the education field and in line with this contribution, as well as with the findings of From (2017) and Rivera-Laylle, Fernández- Morales, Guzmán-Games, and Eduardo-Pulido (2017), digital literacy is understood as a multifaceted and plural concept made up of three dimensions: (1) technological knowledge; (2) attitudes; and (3) didactic use; in three areas of application – teaching, evaluating, and researching – in order to provide students with optimal learning contexts (Carrera & Coiduras, 2012; Pozos, 2016; Prendes et al., 2018). Specifically, this research aims to thoroughly investigate one of these dimensions of digital literacy in particular: its didactic use (Guillén-Gámez & Mayorga-Fernández, 2019).

Various studies (Carrera & Coiduras, 2012; Pozos, 2016) have highlighted the need for university teachers to develop didactic use of technology within their digital literacy, in a transversal manner, in each of the roles and duties they must perform within their knowledge area. In other words, they must develop this dimension of digital literacy both in teaching (the teaching-learning process) and research processes, in order to provide quality teaching on the one hand, and effective development of scientific knowledge on the other. Therefore, it is essential for teachers to use their digital skills to teach, evaluate, and research – combined, this can be referred to as the UTIC-EEI model (use of ICT to teach, evaluate, and research, for its acronym in Spanish). For this reason, in this research it has been decided to design a model called UTIC-EEI (use of ICT to teach,

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evaluate, and research, for its acronym in Spanish), where the digital competence of teachers is assessed in the most important professional fields of university teachers.

When carrying out a literature search on the studies previously carried out in regard to university teachers’ level of competence by branch of knowledge, it can be seen that teachers who work in the experimental and technological fields have an adequate background in ICT, but not from the pedagogical and didactic point of view (Martínez & González, 2015); that is, they make only an instrumental use of ICT. On the other hand, teachers who work in the human and social sciences field still use ICT in a traditional way to transmit information to their students, including through virtual classrooms, to plan their classes and even to evaluate their students (Lorenzo , 2018), considering their low level of technological knowledge (Ladrón-de-Guevara, Almagron, & Cabero, 2019). Teachers in the education sciences field state that they fluently use communication tools, word processors, and online documentation, and report challenges when using online editing tools, online information management, collaborative online work, web 2.0 tools, and multimedia content creation (Carrera & Coiduras, 2012; Pérez-Díaz, 2019).

After conducting a study of 117 teachers in the engineering faculties of the University of Bogotá, Martínez and González (2015) concluded that most teachers used ICT resources in their everyday life, most commonly email, followed by websites, forums, social networks, and chat platforms. In regard to their didactic use of technology, they employed word processors, created didactic material, for instance using PowerPoint, and spreadsheets for scores; on the other hand, they claimed not to know the potential of learning management system (LMS) for managing activities and resources. In relation to teachers who work in the health sciences field, De Ovando and Jara (2019) analysed the use of ICT by 94 university teachers at a private university in Chile, specifically, technological use, didactic use, and design of digital educational materials. The results showed that they had a high level of use with respect to the technological use dimension; a medium-high level in the didactic use dimension; and finally, a medium level in the design dimension of digital educational material. These results coincide with those found by Prendes and Gutiérrez (2013) on the didactic use dimension.

On the other hand, Fernández-Márquez et al. (2018) carried out a study focused on teachers of social and legal sciences, based on 53 teachers at the University of Malaga, and concluded that they had a basic level of digital use; moreover, according to the study, these teachers considered the integration of ICT in their teaching processes to be unnecessary. Despite this, they confirmed their extensive use of word processors, followed by multimedia presentations and search engines, along with their infrequent use of specific software and social networks. Furthermore, among the study’s findings, the authors highlighted the main factors hindering the teachers’ use of ICT, as follows: lack of training, lack of time, and the absence of technological devices. In the business and economic sciences field, Fernández, Sánchez-Oro, and Robina (2016), after conducting a study at the University of Extremadura with 84 teachers in this field, concluded that the importance given to digital literacy, in each of its dimensions, as well as the possibility of applying this skill in day-to-day life, was relatively low. Despite this, these teachers assigned great importance to its use when accessing information sources, but trivialised the use of web 2.0 and multimedia resources for teaching.

Therefore, it can be observed that teachers from experimental and technological fields make greater instrumental use of ICT. This highlights the necessity to develop a greater digital-pedagogical development in all fields, since its deficiency hampers the implementation and use of ICT in educational contexts (Colas- Bravo, De Pablos-Pons, & Ballesta-Pagan, 2018; Ríos, Gómez, & Rojas, 2018). However, it should be noted that most of the studies published to date have been carried out using an individualistic approach, without taking into account the UTIC-EEI model. In addition, most of these investigations have focused on teachers from a single branch of knowledge, with no studies which jointly analyse and compare the use of ICT resources by university teachers according to the areas of knowledge they are specialised in (sciences and engineering-architecture, health sciences, art-humanities, and social-legal sciences).

In view of the above, the present work will focus on analysing whether Spanish university teachers efficiently take advantage of the possibilities offered by ICT for both teaching and research competences, and whether

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any differences exist between teachers in this regard depending on their branch of knowledge. For this reason, the aim of this research is to know and compare the didactic use by university teachers of different ICT resources according to each branch of knowledge, so as to take measures to effectively address their digital deficiencies.

METHOD

Design

An ex post facto methodological design was used in this study, through surveys (Kerlinger, Lee, Pineda, & Mora Magaña, 2002). The expression “ex post facto” means after the events have occurred, since the subjects are selected after the VI has occurred. It was decided to use this design since none of the variables have been modified in the development of the study. First, we present a descriptive analysis followed by an inferential analysis, in order to analyse and compare the university teachers’ level of didactic use, by branch of knowledge, based on the UTIC-EEI model.

Sample-population

The population under study was composed of 120,383 higher education teachers from the Spanish Educational System (MECD, 2018-2019). On this population, non-probabilistic intentional sampling was carried out, with the sample selected according to the branches of knowledge mentioned previously. The sample consisted of a total of 12,538 teachers, who were contacted via email, respecting the confidentiality and privacy of data. In total 1,206 teachers responded. An exploratory analysis was later carried out to refine the database, resulting in a definitive sample of 867 university teachers since some of them did not fill out the complete survey. The distribution of the teaching staff according to the branches of knowledge was as follows: social-legal sciences (N = 400); sciences and engineering-architecture (N = 183); health sciences (N = 173); and art-humanities (N = 111). Regarding the intentional sampling, the intention was to collect a large sample of teachers from each area of knowledge. Furthermore, the authors tried to collect a large and similar sample in each of the areas so that the results were not influenced by the sample size. The only area in which the sample size was larger was Social Sciences since it is the area to which the authors belong, and consequently, we were more interested in analyzing.

Instrument

To measure the level of didactic use of ICT resources in university teachers, an instrument was designed utilizing a five-point Likert scale. Such level was measured in different web 2.0 tools classified in three dimensions which compose the UTIC-EEI model. Understood as web 2.0 tools those where the user becomes an active agent in the design and development of content and services through the network (Nafría, 2007). The first dimension was made up of five items related to the use of ICT resources in teaching tools; the second dimension was made up of four items focused on the use of ICT resources for student assessment; and the third dimension was made up of seven items related to the use of ICT resources for research. The instrument contained a total of 16 items. Regarding the level of educational use and the five-point Likert scale, a score obtained by teachers between 1 and 2 points was deemed to represent a low level of use; a score between 2 and 3 points represented a medium-low level; a score between 3 and 4 represented a medium level; and a score between 4 and 5 points represented a high level.

Analysis of the Reliability and Validity of the Instrument

The reliability of the instrument was guaranteed using Cronbach’s α. The total reliability of the instrument was α = 0.90, being a very acceptable value. Likewise, the reliability of each of the dimensions was also high: ICT resources for teaching, α = 0.81; ICT resources for evaluation, α = 0.81; ICT resources for research, α = 0.86.

To determine the validity of the instrument, Exploratory Factor Analysis (EFA) was carried out using the maximum likelihood extraction, to observe the presence of correlation between items and factors. Construct

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validity was demonstrated, in the first instance, based on the value obtained in the Kaiser-Meyer-Olkin index (KMO = .913) and the Bartlett’s test of sphericity with a chi-square score of 6317,040 (p = .001), which indicated the factor analysis accuracy. The oblimin (oblique rotation) method was used since we consider that the factors are related, which revealed the presence of three factors and explained 59.81% of the true variance of the instrument. Subsequently, AMOS 22.0 software determined the goodness of fit of the proposed model through Confirmatory Factor Analysis (CFA), obtaining the following values: chi-square as test of contrast hypothesis (CMIN/DF, 3.890), considering that values less than 5 indicate a good fit (Bentler, 1989); the comparative fit index (CFI, 0.955) and the incremental fit index (IFI, 0.955), considering values above .900 to represent a good adjustment (Hu & Bentler, 1999); and for the root mean square error of approximation (RMSEA, 0.058), where values below 0.06 indicate a good fit of the model.

Procedure and Data Analysis

The data analysis included several procedures, which are detailed below:

a) Second, a descriptive analysis was carried out on the level of didactic use according to the proposed model, which was classified according to the branches of knowledge.

b) Finally, multiple comparisons were carried out, in order to be able to infer if there were statistically significant differences in the level of use between the different branches of knowledge.

ANALYSIS OF RESULTS

Descriptive Analysis of the Teachers’ Use, based on the UTIC-EEI Model

Table 1 shows the means of the items for each of the dimensions related to the UTIC-EEI instrument, according to the branch of knowledge the teaching staff belonged to. Regarding the use of ICT resources for the teaching dimension, it was generally observed that word processors and multimedia presentations were the most-used resources, with scores close to the maximum value of the five-point Likert scale, being slightly higher than the use of both these resources in the sciences and engineering-architecture branch (M = 4.64 word processors; M = 4.64 multimedia presentations). It should be noted that the most-used ICT resources were learning management systems (LMS) and Google+ in all areas. Also, LMS use was slightly higher in engineering (M = 3.86) and Google+ in social sciences (M = 3.75). It was also observed that content platforms and educational forums were less used by teachers, with average levels in all branches of knowledge, and use of both resources being slightly lower in health sciences (M = 2.13 content platforms: M = 2.42 educational forums).

Regarding the use of ICT resources to evaluate students, it was observed that the most used resources were rubrics and test-type controls through LMS. Specifically in rubrics, it was observed that the sciences and engineering-architecture (M = 3.33) branch showed a slightly higher level of use, with a similar average for the social-legal sciences branch (M = 3.32); while the branch of teaching staff that made greatest use of test- type controls was sciences and engineering-architecture (M = 3.48). On the other hand, the use of e- portfolios or forums to evaluate activities was less common than the use of the previously mentioned resources, showing a medium level of use, with the sciences and engineering-architecture branch making least use of these ICT resources (M = 2.36 e-portfolios; M = 2.68 forums to evaluate activities).

Finally, the results for the use of ICT resources for research dimension showed that the most-used resource by teachers was the academic Google search engines for scientific consultations; the science and engineering-architecture branch obtained a slightly higher average compared to the other branches of knowledge (M = 4.52). The use of search engines in databases or impact journals showed similar values, with the teachers from the science and engineering-architecture branch demonstrating the highest use of these resources (M = 4.29 search engines and databases; M = 4.42 JCR journal websites and SJR according to their quartiles). In regard to the use of programs for data analysis, the teachers claimed to have a medium-high level of use, being superior in the health sciences branch (M = 3.84) compared to, for example, the art- humanities branch (M = 2.74).

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Figure 1 shows the total self-use perceived by teachers in each of the branches of knowledge, for each dimension. The use of ICT resources for Research purposes are the most emplo

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