13 Evaluation of the Learning Models Using Multi-criteria …
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13.3.7 Physics Self-efficacy Questionnaire
In order to identify the effect of the applied teaching methods on the self-efficacy of
the students, the physics self-efficacy questionnaire developed by Tezer and A¸ sıksoy
(2015) was used. It consists of 32 items, and the reliability coefficient of the selfefficacy questionnaire was 0.985. In the questionnaire, the minimum points a student
could receive was 32, and the maximum was 160. High scores from the scale indicate
high self-efficacy towards the physics course.
13.4 Fuzzy PROMETHEE Approach to Learning Models
The PROMETHEE method was developed in the 1980s by Brans, Vincke and
Mareschal, (1986) and is based on a mutual comparison of each alternative pair
with regard to each selected criteria. It is based on crisp data, which means it is not
possible to use this method for vague conditions. Fuzzy logic was proposed by Zadeh
(1965) in order to express vague conditions, experiences of experts or linguistic information mathematically. Fuzzy logic enables decision makers to simplify complex
systems (Gravani et al. 2007). The Fuzzy PROMETHEE method is a hybrid method
that is related to both fuzzy logic and the PROMETHEE method. This technique
was proposed by Wang et al. (2008) for solving multi criteria decision making problems. The fuzzy PROMETHEE method allows the decision maker to use fuzzy input
data, which gives more flexibility to the decision maker when comparing alternatives in vague conditions. Several studies have been conducted based on the fuzzy
PROMETHEE (F-PROMETHEE) approach. Using this technique, Goumas and
Lygerou (2000) ranked alternative energy exploitation projects, Bilselet al. (2006)
evaluated hospital web sites, Chouet al. (2007) evaluated suitable eco-technology
method and Ozgen et al. (2011) applied this technique for the machine tool selection
problem in a fuzzy environment.
This method enables the analysis of complex systems that have fuzzy parameters
and produces effective comparison results. The details of this technique have been
shown in the studies of Uzun Ozsahin et al. (2017a, b). They applied this technique
in the evaluation of various types of nuclear medicine imaging devices and for the
evaluation of cancer treatment techniques. Furthermore, Uzun Ozsahin and Ozsahin
(2018) applied this technique in the analysis of breast cancer treatment techniques and
Uzun Ozsahin et al. (2018) used it in the analysis of X-Ray based medical imaging
devices. Maisaini et al. (2019), Ozsahin et al. (2019a, b, c), Sayan et al. (2019) are also
have used F-PROMETHEE approach to rank the alternatives in selection problems
of the medical and health sciences. In this study, we used the same method for the
evaluation of various learning models.
Learning is a process that occurs in terms of cognitive abilities as much as in
the affective sense (Berber et al. 2010). In the learning process, affective variables
such as motivation, attitudes, and learning satisfaction need to be examined so that
119
13.3.7 Physics Self-efficacy Questionnaire
In order to identify the effect of the applied teaching methods on the self-efficacy of
the students, the physics self-efficacy questionnaire developed by Tezer and A¸ sıksoy
(2015) was used. It consists of 32 items, and the reliability coefficient of the selfefficacy questionnaire was 0.985. In the questionnaire, the minimum points a student
could receive was 32, and the maximum was 160. High scores from the scale indicate
high self-efficacy towards the physics course.
13.4 Fuzzy PROMETHEE Approach to Learning Models
The PROMETHEE method was developed in the 1980s by Brans, Vincke and
Mareschal, (1986) and is based on a mutual comparison of each alternative pair
with regard to each selected criteria. It is based on crisp data, which means it is not
possible to use this method for vague conditions. Fuzzy logic was proposed by Zadeh
(1965) in order to express vague conditions, experiences of experts or linguistic information mathematically. Fuzzy logic enables decision makers to simplify complex
systems (Gravani et al. 2007). The Fuzzy PROMETHEE method is a hybrid method
that is related to both fuzzy logic and the PROMETHEE method. This technique
was proposed by Wang et al. (2008) for solving multi criteria decision making problems. The fuzzy PROMETHEE method allows the decision maker to use fuzzy input
data, which gives more flexibility to the decision maker when comparing alternatives in vague conditions. Several studies have been conducted based on the fuzzy
PROMETHEE (F-PROMETHEE) approach. Using this technique, Goumas and
Lygerou (2000) ranked alternative energy exploitation projects, Bilselet al. (2006)
evaluated hospital web sites, Chouet al. (2007) evaluated suitable eco-technology
method and Ozgen et al. (2011) applied this technique for the machine tool selection
problem in a fuzzy environment.
This method enables the analysis of complex systems that have fuzzy parameters
and produces effective comparison results. The details of this technique have been
shown in the studies of Uzun Ozsahin et al. (2017a, b). They applied this technique
in the evaluation of various types of nuclear medicine imaging devices and for the
evaluation of cancer treatment techniques. Furthermore, Uzun Ozsahin and Ozsahin
(2018) applied this technique in the analysis of breast cancer treatment techniques and
Uzun Ozsahin et al. (2018) used it in the analysis of X-Ray based medical imaging
devices. Maisaini et al. (2019), Ozsahin et al. (2019a, b, c), Sayan et al. (2019) are also
have used F-PROMETHEE approach to rank the alternatives in selection problems
of the medical and health sciences. In this study, we used the same method for the
evaluation of various learning models.
Learning is a process that occurs in terms of cognitive abilities as much as in
the affective sense (Berber et al. 2010). In the learning process, affective variables
such as motivation, attitudes, and learning satisfaction need to be examined so that