Latent Inhibition as a Biological Basis of Creative Capacity in Individuals Aged Nine to 12

This study focuses on latent inhibition, a mechanism behind selective attention, as the biological basis of creativity in schoolchildren. The main objective of this study is to know if low levels of attention positively affect the levels of creativity manifested in students between the ages of nine and 12. The design of this study is non-experimental with an explanatory-correlational cross-sectional quantitative approach. In order to achieve the objective suggested, several education centers located in Murcia were selected, in which 476 students took part in a creativity test (PIC-N), an attention test (D2), and another test about intelligence depending on the educational level (BADYG/E2r or BADYG/E3r). The results obtained showed that selective attention was negatively correlated with graphic creativity, understanding that behind it lies the latent inhibition, and that when certain levels of intelligence are present, this negative correlation increases. In this way, the simultaneous existence of creative and inattentive subjects is demonstrated.

INTRODUCTION
Today's society is fully aware that creativity is one of those qualities most valued in humans. In all areas in which knowledge is applied, including work, an innovative individual has a place, since their divergent thinking pushes them to leave their comfort zone, allowing those individuals to live out of it. Schools should be at the forefront of promoting creative thinking. Authors like Chávez et al. (2020) state that in school, children face situations that they should address and solve in different ways, helping them understand that ideas different than their own are still valuable. Even after this, in a majority of schools, creativity is not something that is given proper attention (Pérez, 2018). It is even necessary to mention that limits are imposed on divergent thinking, with creative thinking being seen as a bad habit (Sternberg, 2007). Taha et al. (2015) defined creativity as the original way in which individuals face challenges in their daily lives. Creative thinking is noted in different ways and can be developed (López and Martín, 2010). Glaveanu et al. (2019) state that creativity is a human quality that can give meaning to our lives. This capacity is a quality that shows up in similar ways in men and women alike, and gender is not a differentiated factor (Harris, 2004;Espinosa, 2005;Elisondo and Donolo, 2011;Abraham et al., 2014;Soisa, 2015).
The current trend in creative thinking is toward specific domain creativity due to correlational studies reporting low relationships between divergent thinking and expert ratings of creative performance in different domains (Artola et al., 2010;Hernández, 2017). Kaufman and Baer (2004) proposed three domains of creativity: verbal (personal problem solving, communication, and writing); graphic (bodily creativity, in art and printmaking); and scientific (creativity in mathematics and science). Previous works, like those from Sternberg and Lubart (1997), support this idea, stating that, as an individual can perform higher in some areas than others, it is possible that this is due to being creative in one area and not in the other. Artola et al. (2010) use the sum of verbal or narrative creativity together with graphic creativity to arrive at the idea of general creativity; narrative creativity is the divergent idea in the execution of verbal tasks, while graphic creativity is implicit in the execution of nonverbal tasks, such as drawing. Verbal creativity is the linguistic act that activates not only creative thinking but also a process of written reflection in which language ceases to be conventional to give way to a superior discourse where metaphors, originality, and imagination shape the narrative without losing textual harmony (Sandoval, 2016). On the other hand, graphic creativity is defined as that which develops innovative and effective ideas around any graphic process, especially in drawing (Torrance, 1977). Carson et al. (2003), psychologists and investigators from Toronto and Harvard University, identified low latent inhibition as one of the biological foundations of creativity, stating that the creative people's brains are in contact with more of the environmental stimulus than non-creative ones, since the process of information does not stop. Important to mention is the fact that they noted the requirement of a high intelligence, to allow a low latent inhibition, not associated with schizophrenia or to a psychosis. Considering this, the research problem that needs to be addressed is whether easily distracted individuals, due to a latent attenuated inhibition, are more creative? Is it necessary to possess a higher intelligence to see this association?
The answer to the inclusion of intelligence in this work can be found in a deficit in the attention element, usually associated with a pathology that can become a creativity advantage in the absence of other creativity strengths, like a high intellectual quotient (Carson et al., 2003). Intelligence is a set of problem-solving abilities (Gardner, 2016). Regarding gender differences in levels of creativity, at early ages they are small or non-existent and it is from adolescence onwards, and especially in adulthood, that these differences become more noticeable (Feingold, 1993). In the school period, Lynn and Irwing (2004) found that there are no significant gender differences between the ages of six and 14.
Attention is one of the superior brain mental functions, allowing one to properly organize information obtained from displaced senses, focalization, or due to irrelevant stimuli inhibition (Hernández, 2017). Based on the inhibition of irrelevant stimuli, surges the concept of latent inhibition which Kaufman (2009) affirmed that it is the capacity to maintain the excess of information, understood like irrelevant stimuli, outside our cognition. Henceforth we will assume that, behind selective attention, we can find the mechanism known as latent inhibition that narrows attention and focus, and limits information influx (Hasher et al., 2007). The absence of a stimuli filter is a disadvantage when an individual tries to concentrate on the achievement of tasks that require a high level of attention, since a wide array of stimuli will have access to the subject's mind (González, 2017).
Latent inhibition will allow a higher number of elements to combine, although not all individuals will notice this due to the distance among the different parts, allowing innovative ideas and, in turn, creative thinking (Carson, 2010). Important to note is the fact that innovative thinking refers to new ideas from association, useful to a specific situation, and the distances among elements makes a more creative process (Mednick, 1962). Mednick (1962) and Campbell (1960) propose that individual differences in attention focus and concentration are the cause of creativity differences. Most of the investigations reviewed use, Eysenck theory (1995) as a starting point in which low latent inhibition influences the level of cognitive inhibition, present in creativity, that combined with sociocultural variable, cognitive variable (IQ), and motivation, allows for creative thinking. The difference between an individual with a psychosis and an innovative individual, when faced with low latent inhibition, is based on the cognitive variables (Carson et al., 2003). Among the population with Attention Deficit Hyperactivity Disorder (ADHD), it has been proven that the inefficiency of the latent inhibition mechanism promotes innovation (Pritchard et al., 2006;González, 2017). Mendelsohn and Griswold (1966) defend the idea that creative individuals are more likely to use irrelevant stimuli, incidentally or randomly present. This translates into difficulties with cognitive information selection, since they will not be able to differentiate between important noise stimuli and distractors (Martínez, 2001). Although it would be beneficial when completing innovative tasks, it would be a problem when completing other tasks (Rawlings, 1985;Eysenck, 1995). Current works about low latent inhibition reinforce the idea of problem-finding as the source of problem-solving (Runco, 1994). When more information enters, the higher the probability is of a combination, which will then generate new ideas. In summary, the amount of information that an individual can manage is directly linked to the creation of an original product or the solution to problems. This is due to cognitive irrelevant stimuli remaining active, allowing a higher level of creativity in the individual, different from less innovative individuals, who show a higher level of focus and attention (González, 2017).
Regarding sex, most works support that there are no significant differences based on gender (Fernández-Castillo and Gutiérrez, 2009;Brickenkamp, 2012;Soisa, 2015). Regarding age, sustained attention increases, due to the use of selective strategies, starting at age 10 (Orjales and Polaino, 1992).
The main goal of this work is to know if low attention levels positively influence creativity levels on students aged nine to 12 years of age. The hypothesis was that students with attenuated latent inhibition and above average intelligence will have higher creativity scores. Specific objectives were: • To know the levels of creativity, attention, and intelligence in students 9-12 years of age.
• To determine if there are creativity, attention, and intelligence differences based on gender.
• To compare creativity, attention, and intelligence based on educational level and to check if there are any significant differences.

Participants
In this non-experimental quantitative, cross sectional, correlational study (Ato et al., 2013), a total of 476 students, ages nine to 12, from four elementary schools, from the region of Murcia, Spain, participated. These are public schools. The families of the students in these schools have a low economic level and a low socio-cultural level. Regarding grade level, 152 fourth grade students (31.93%), 164 fifth grade students (34.46%), and 160 sixth grade students (33.61%) participated. By gender, 248 males (52.10%), and 228 females (47.90%) participated. The participants' selection method was non-probability sampling, known as consecutive, engaging only students belonging to the last phase of Elementary education (Valdivia, 2018). Consecutive sampling is comprehensive since all children in the classrooms of the aforementioned grade levels participated.

Instruments
Four different tests were used to collect information regarding the different variables of this research project.
"Prueba de Imaginacion Creativa para niños (PIC-N)" (Creative Imagination Test for Children) (Artola et al., 2010). This test (α = 0.84) is designed to measure the creativity of children in two specific domains: narrative creativity and graphic creativity. Age range is eight to 12. PIC-N comprises four tasks. The first three assess verbal (narrative) creativity, and the last one graphic creativity. In game one, children have 10 min to write down everything that is happening in a scene from an image of a child opening a chest. In game two, subjects have 7 min to identify all possible uses of a rubber tube. In game three, children have 10 min to describe what would happen if all squirrels turned into dinosaurs. In game four, subjects have 10 min to draw four pictures from given strokes and assign a title to each of the drawings.
D2 Test of Attention (Brickenkamp, 2012). This test (α = 0.60) assesses selective and sustained attention in individuals engaged in a task in which irrelevant stimuli has to be ignored, fast and accurately. It is made up of 14 lines, 47 characters each, for a total of 658 characters. The task is to cross out any letter "d" with two marks.
"Bateria de Aptitudes Diferenciales y Generales E2 renovado (BADYG/E2r)" (Differential and General Skills Battery BADYG/E2r) (Yuste, 2011). This test (α = 0.79) measures cognitive abilities using three elements, verbal, numeric, and spatial, applied to third and fourth grade Elementary students. This instrument is comprised of six tasks (two for each factor), for a total of 24 items each. The six tests are: analog relations, number problems, logic matrices, sentence completion, numerical calculation, and rotated figures.
"Bateria de Aptitudes Diferenciales y Generales E3 renovado (BADYG/E3r)" (Differential and General Skills Battery BADYG/E3r) . This test (α = 0.88) measures cognitive abilities (IQ) taking into account three factors, verbal, numeric, and spatial, applied to fifth and sixth grade Elementary students. It is composed of six tasks (two for each factor), for a total of 32 elements each. The six tests are: verbal analogies, number series, logic matrices, sentence completion, number problems, and figure matching.

Procedure
First of all, school approval was sought and parental permission was provided for their children to participate in the study. Approval obtained, the subjects completed the tasks individually using paper and pencil. Formal instruments were administered by grade level, BADYG/E2r for fourth grade, BADYG/E3r for fifth and sixth grades, in an approximate administration time of 1 h and 15 min for each class. The following day and to avoid students' fatigue, the reminder of the creativity instruments (PIC-N) and attention (D2) were administered over a period of about 40 min and 5 min, respectively, with each class.

Statistical Analysis
Statistical analysis was completed using the program IBM SPSS Statistics (version 24). A data matrix was used to analyze data regarding the proposed objectives. First, statistical evidence was used to address the variable level in the sample. To address possible differences based on the participants' gender, for variables that fit parametric requirements, the independent samples t-test and Mann-Whitney U-test, for non-independent samples, were used. Also, and to address possible differences taking into account the educational level of the participants, a statistical variable analysis using One-way ANOVA was completed. With variables that did not meet parametric requirements, the Kruskal-Wallis H-test was used. Finally, a correlational analysis with the selected variables was completed using Pearson's correlation.

RESULTS
Three types of creativity were analyzed in connection with the first objective: two of them measure as creativity as a specific value, while the third one is general.  Before attempting an inferential analysis, it is necessary to probe that these variable samples follow a regular distribution pattern that supports the use of the proper parametric and non-parametric instruments. To accomplish that goal, the Kolmogorov-Smirnov method was applied to each of them. If p > 0.5 is accepted as a null hypothesis, homogeneity exists between variables distribution and regular distribution. Thus, narrative creativity (p > 0.5), graphic creativity (p > 0.5), general creativity (p > 0.5), total test effectiveness (p > 0.5), and cognition (p > 0.5) are distributed following a regular pattern. If p < 5, the hypothesis of this research project, stating that there are differences, will be accepted. The concentration index (p < 0.5) is not distributed in a regular pattern.
Since the second specific goal was to establish differences based on gender, independent samples t-tests were used ( Table 1). The T-test shows that there are no significant differences based on gender among participants regarding narrative creativity (t = −0.636, p > 0.05), graphic creativity (t = 0.054, p > 0.05), general creativity (t = −0.595, p > 0.05), total effectiveness of the test (t = −0.471, p > 0.05), or intelligence (t = 0.693, p > 0.05). As previously mentioned, the concentration index does not follow a regular pattern. For this reason, a nonparametric approach, the Mann-Whitney U-test, was used for the analysis of the independent samples t-test. T-test results (Z = −1.059, p > 0.05) shows that there are no significant differences based on gender in the concentration index.
The third specific goal was to compare creativity levels, attention, and intelligence, based on educational level, to observe any significant differences. This task was addressed using Oneway ANOVA inferential analysis, as recorded in Table 2.
ANOVA shows that no significant differences can be observed based on the educational level regarding narrative creativity (F = 0.828, p > 0.05) and general creativity (F = 0.359, p > 0.05). In graphic creativity, significant differences can be found regarding the educational level based on the results of ANOVA (F = 5.523, p < 0.01). Post-hoc tests results show that significant differences (p < 0.05) took place between fourth and fifth Elementary grades, with an average difference of 2.42. Other significant differences (p < 0.05) can be found between fourth and sixth Elementary grades, with an average difference of 2.65. ANOVA results (F = 13.658, p < 0.01) show significant differences regarding the total effectiveness of the test by educational level. Post-hoc data show that significant differences (p < 0.01) can be observed between fourth and fifth Elementary grades, with an average difference of  Post-hoc data shows that significant differences (p < 0.01) were observed between fourth and fifth Elementary grades, with an average difference of 13.20. Significant differences can be also observed (p < 0.05) between fourth and sixth Elementary grades, with an average difference of 10.04, in students. As previously stated, the concentration index is not evenly distributed. To address it, it was analyzed with a non-parametric method: Kruskal-Wallis H-test. Kruskal-Wallis test (H = 34.566, p < 0.01) shows that significant differences can be found in the concentration index by educational level. Significant differences (p < 0.01) were found between sixth (M = 85.85) and fourth (M = 43.49) Elementary grades. Significant differences (p < 0.01) can also be found between sixth (M = 85.85) and fifth (M = 50.09) Elementary grades.
The last goal was to determine if low attention levels positively affect creativity in students between the ages of nine and 12. As previously stated, Pearson correlation ( Table 3) was used. Pearson correlation (r = 0.244, p < 0.01) shows that a relation between general creativity and total test effectiveness can be found, and that it is a low correlation since r's value is between 0.20 and 0.40. The same can be observed between narrative creativity and total test effectiveness, with a positive correlation, although with different results as shown in Pearson's correlation (r = 0.274, p < 0.01). The Pearson correlation for graphic creativity and total effectiveness (r = −0.100, p > 0.05), shows that no correlation exists between those variables. Regarding possible correlations with the concentration index, the Pearson's correlation coefficient (r = −0.212, p < 0.05) for the concentration index and graphic creativity shows that a correlation between these two variables can be observed. This correlation is low, and at the same time negative, since r's value is between −0.20 and −0.40. This means that, due to a negative correlation, as the values of one variable diminish, the ones from another variable rise, and vice versa. Between general creativity and concentration index, results from Pearson's correlation (r =0.133, p > 0.05) shows that no correlation exists between these variables. In the same direction, results from Pearson's correlation (r = 0.166, p > 0.05) and between narrative creativity and concentration index show that no correlation among these variables exist.
With this last correlation comes the answer to the main question proposed by this research project. To confirm the hypothesis that low latent inhibition increases creativity, especially when a certain level of intelligence is present, the students selected for this research were those with an IQ higher that the average of this research, with the results listed on Table 4.
As reflected in Table 4, the negative correlation raises with the inclusion of the intelligence variable from the 240 participants' sample. The Pearson's correlation coefficient (r = −0.376, p < 0.01) decreases, raising when the negative significant correlation exists between the variables, although within the range of a low correlation.

DISCUSSION
At the start of this research, a set of goals and a hypothesis were proposed. The first specific goal was to get to know the creativity level, attention, and intelligence in students from nine to 12 years of age. Taking into account the results, creativity levels were diverse in each specific domain and in its general domain, due to the structure of the tests used, since three games evaluated narrative creativity, while only one evaluated graphic creativity (Artola et al., 2010). Regarding selective attention, levels of total effectiveness in the test are higher than the concentration index, as these last ones are lower, but more reliable since it penalized individuals who answer without control, taking into account correct answers, and thus showing a balance between stress and speed, avoiding overestimation (Brickenkamp, 2012). Regarding intelligence quotient (IQ), it shows low scores, since although a regular IQ pattern is shown, the average is 100, and thus almost eight points below the expectation (Yuste, 2011;Yuste et al., 2011). These IQ scores may be influenced by the context of the participants due to their low socioeconomic background.
The second specific goal was to determine if differences in creativity levels, attention, and intelligence could be found based on gender. Data obtained for this research shows that gender is not a variable that determines creativity levels (Harris, 2004;Espinosa, 2005;Elisondo and Donolo, 2011;Abraham et al., 2014;Soisa, 2015). Gender is an irrelevant factor regarding selective attention (Fernández-Castillo and Gutiérrez, 2009;Brickenkamp, 2012;Soisa, 2015). The same findings were obtained regarding intelligence, since individuals between the ages of six to 14 do not show differences based on sex (Feingold, 1993;Lynn and Irwing, 2004).
Regarding the third specific goal, no significant differences could be found based on educational level regarding general creativity and narrative creativity. Regarding graphic creativity, significant differences can be observed in the higher levels of fourth grade. School lowers student's graphic creativity, since significantly lower scores can be observed in the fifth and sixth grades (Sternberg, 2007;Pérez, 2018).
For selective attention, significant differences on the educational level can be found based on the total effectiveness of the test, with the lowest levels observed in fourth grade students. Also, the concentration index shows significant differences based on the educational level, in which higher levels are shown on sixth grade students. Taking into account these results, older students show better scores in selective attention, since scores increase with age (Orjales and Polaino, 1992). This is due to the fact that, starting at about 10 years of age, students start to use selective strategies.
Regarding intelligence (IQ), data shows significant differences based on educational level, with the higher scores in fourth grade Elementary, showing higher IQ scores than the rest of the grade levels. Perhaps this is due to the data collection instruments, although the intelligence test scores were corrected for the age of the children.
It is confirmed, taking into account Pearson's correlation, that there is a relationship between concentration and graphic creativity variables. This is a negative correlation, since students with a lower selective attention present higher graphic creativity levels. Several works stated the idea that individuals showing less focus show higher levels of creativity (Campbell, 1960;Mednick, 1962;Mendelsohn and Griswold, 1966;Rawlings, 1985;González, 2017).
Regarding the research hypothesis, evidence was found that students with attenuated latent inhibition as the mechanism behind selective attention possess high graphic creativity, when a specific level of intelligence is present, as evidenced by above average IQ scores. This idea is supported by Eysenck's theory (1995) and numerous other research projects (Carson et al., 2003;Pritchard et al., 2006;González, 2017).
One of the main conclusions of this research is the concept that a creative individual is also a distracted individual, being frequently diagnosed only as inattentive and not creative (González, 2017), since from the educational point of view, it is easier to address inattentive students than creative ones. This labeling process can explain the higher number of ADHD students identified within the educational system, to the point of having to consider the possibility of over diagnosis. In educational practice, it is necessary to provide educational experiences appropriate to the characteristics of the creative and distracted child, developing in the classroom methodologies that allow for creative behavior and that are not so rigid as to allow moments of inattention as in the case of projectbased learning.
Another conclusion is that creativity needs to be reconsidered within the educational system, in order to reinforce its implementation and development. Empirical evidence is provided showing that Eysenck's theory (1995), and research by Carson et al. (2003), confirms latent inhibition as the biological foundation of creativity, when the IQ requirement is met, although this work addresses the specific domain of graphic creativity. However, this association does not occur with the specific domain of narrative creativity.

DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

ETHICS STATEMENT
Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

AUTHOR CONTRIBUTIONS
AL: data gathering, data analysis, and manuscript writing. OL-M: data gathering and manuscript writing. MV-Y: manuscript writing. All authors contributed to the article and approved the submitted version.


Teaching Science Students How to Think

Scientific thinking is more than just critical thinking. Teaching the full range of ways to think like a scientist who practices high quality science is rare. A new core subject in the Bachelor of Science at the University of Newcastle was developed to allow students to explore six different ways to thinking scientifically through understanding what high-quality science is and contrasting it with poor science and non-science (pseudoscience). Our evaluation indicates that learning about how to think scientifically and be a scientist who practices high quality science is a skill that is valued by and relevant to first year undergraduate students. An evidence-based pedagogy including active learning, participatory learning, student-centred learning, constructive alignment and quality formative and summative feedback to students can support high learning outcomes.

Introduction Scientific thinking in science education
There is significant focus on the value of teaching critical thinking skills in science (for example, Rowe et al., 2015;Zimbardi, Bugarcic, Colthorpe, Good, & Lluka, 2013). Critical thinking skills underpin scientific literacy and the quality of decision making. However, critical thinking is just one thinking skill required by scientists. In an increasingly complex world that requires solutions to wicked problems (Rittel & Webber, 1973) we need innovative ways to address these problems.
Causal thinking or reasoning is implicit in critical thinking. We use different approaches to apply causal logic (inductive, deductive, abductive etc.) to make sense of evidence and multiple sources of information (Dunbar & Fugelsang, 2005). Being aware of which causal reasoning we can use allows us to be much more systematic in how we reason and learn (Lagnado, 2011).
The process of scientific discovery also requires creative thinking just as much as it requires critical thinking. Creative thinking is often understood as that big 'ah ha!' moment when a new idea suddenly transforms how something is understood or done. But creative thinking can also be a learned process (DeHaan, 2009). In this form (something DeHaan denotes as 'small c' creativity) it can be more incremental and emergent as it is combined with other ways of thinking scientifically. Impact in science often derives from conventional thinking combined or applied in new ways (Uzzi, Mukherjee, Stringer, & Jones, 2013). Challenging some of the assumptions about what creative thinking is and how it is done is the first stage of learning creative thinking skills.
Similarly, reflective thinking is equally important in science. The journals of famous scientists (e.g. Marie Curie, Joseph Banks) model the value of reflective thinking. Critical reflection underpins critical thinking (Mezirow, 1910). However, as with creative thinking, reflective practice is often assumed to be something that is not deliberate or learned. Reflective thinking is often learned in a superficial way because it is not purposefully scaffolded into a curriculum (Ryan & Ryan, 2012).
Learning ethical thinking is critical in the Science Learning and Teaching Academic Standards Threshold Learning Outcomes for Australian science teaching in higher education (Jones, Yates, & Kelder, 2011). In science, ethical practice has many layers. At a basic level it is about things like accurately recording and storing data, intellectual integrity, aligning practice with animal and human ethics requirements. But scientists must also respond to social and cultural nuances in their bid to understand the natural world. This requires interpersonal and reflective skills to apply ethical practice.
Even less acknowledgement is given to the importance of emotional thinking in scientific thinking. In fact, emotional thinking is seen to detract from high-quality scientific thinking. The impact of emotions on bias in thinking certainly needs to be controlled for in critical thinking (Martel, Pennycook, & Rand, 2020). However, science that has impact (i.e. changes how we function and understand the world) can't be abstracted from the human setting it must be integrated into. To do so relegates it to the theoretical academic literature where it remains isolated from the society it is intended to benefit (Jones, McGarrah, & Kahn, 2019). Emotional thinking is also critical in learning science for the teacher (Zembylas, 2004) and the student (Mezirow, 1991).
It is the combination of all these different types of scientific thinking that determines the quality of science practice (McBain et al., 2019). Therefore, science students require foundational learning in each to become high-quality scientists.

Context -revision of the Bachelor of Science
In 2017 the University of Newcastle undertook a 'roots and branches' review of its Bachelor of Science (BSc). The strategic planning for the revision was undertaken via a highly collaborative 6-month process. It involved over 200 stakeholders from various groups including science or STEM: 1) academic teaching staff; 2) professional and technical staff, 3) current undergraduate students 4) post-graduate students, 5) prospective university students (school students attending a 'Science Fest' day), 6) industry, 7) UoN staff from other faculties, 8) UoN STEMM leaders, 9) staff with transferrable skills expertise and 10) pedagogy experts. This led to a mandate for 1) teaching how to think scientifically, 2) connecting the student learning journey, 3) explicitly teaching transferrable skills, 4) embedding sustainability, 5) employability/WIL, 6) more-than-single disciplinary practice, and 7) entrepreneurialism.
Six new core subjects (see Figure 1) were to do the 'heavy lifting' for this mandate with consolidation occurring within the disciplinary study elsewhere in the degree (disciplinary Majors, Minors and elective pathways).

First year core subject -Professional Scientific Thinking
The focus of the first-year core subject called 'Professional Scientific Thinking is how to think like, and be, a high-quality scientist. This subject provides a foundational learning about the different ways that scientists think, including critical, creative, reflective and ethical (McBain et al., 2019). Students learn how each type of thinking is critical for high-quality science. The curriculum challenges some of the assumptions commonly made about different types of thinking (e.g. creative thinking is inborn and can't be learned) To ensure that the subject materials are applied, students connect the online subject content with practical workshop activities where active learning (Wieman, 2017) demonstrates elements of different types of thinking. The curriculum is made 'real' through examples from history, from current popular science, and examples from the lived experiences of tutors leading the workshop activities. This helps students learn about the research that is undertaken at their own institution and shows how high-quality science is embodied by a real, practicing scientist. This subject is blended or flipped in response to the highly diverse student cohort. Nearly half of students are mature aged and a third are from a lower socio-economic background with significant work and family responsibilities. In consultations with current students whilst revising the BSc, students told us that the reason they did not often come to lectures was that they needed to work in order to be able to study. These students needed flexibility in their study (the online component of the subject can be studied at any time during the week before the interactive workshops). The constructive alignment (Wang, Su, Cheung, Wong, & Kwong, 2013) of online subject materials with the workshop activities and assessment tasks was intended to emphasise the relevance and importance of engaging with the online material.
The four critical learning outcomes for students are: • what high quality science is • how to identify poor science and non-science • how to become a scientist who practices high quality science and • to develop foundational science writing skills The subject demonstrates the various ways to think scientifically by identifying the practices of high-quality science (through the scientific method) and contrasting that with poor science and non-science (pseudoscience). Both the online subject content and the workshops are embedded into a pedagogy that supports active learning (Partridge et al., 2001;Wieman, 2017), constructive alignment (Wang et al., 2013) and aligns with best practice blended learning (Stone, 2017).

Connections with subsequent learning
This paper discusses a small range of different types of scientific thinking that first year students need to be aware of. However, this paper cannot address the full range of thinking skills required. In our connected curriculum across years one to three, the foundational skills noted here are applied via project-based learning. This project-based learning also adds a range of other scientific thinking skills required by scientists such as entrepreneurial thinking (Robinson, Stimpson, Huefner, & Hunt, 1991), design thinking (Anderson et al., 2014), systems thinking (Meadows, 2008), complexity thinking (Brown, 2010) etc. The subject that we document here is the foundational exposure that allows students to apply these other types of scientific thinking.

Data Collection
Five types of data were collected to evaluate the student experience, engagement and the learning outcomes from the first-year subject: 1. the University of Newcastle routinely collects student feedback on subjects via fifteen questions (Student Feedback on Courses, SFCs). 2. University of Newcastle data on student success (pass rates) and attrition (after census date) for a range of student demographic groups. 3. Brookfield's Critical Incident Questionnaire (CIQ) (1995) in Week 5 of the subject.
The questionnaire asks five open ended questions of students about their experience of doing a subject. The approach is powerful in identifying student lead perspectives because open ended questions don't constrain what students can report. Analytics were collected for student engagement with online modules of subject content each week. 4. student success for individual lines of a grading rubrics for an assessment requiring written communication (a Nobel Prize nomination for an admired scientist). 5. a focus group session undertaken with two students and run by an independent facilitator.

Evaluation
Students rated the relevance of the subject as high in their Student Feedback on Courses (3.92 out of 5) in its first offering. It is difficult to disaggregate student feedback scores and hence it is not possible, from this data, to identify the reasons underlying these relatively high student satisfaction scores (likely it is a combination of things). Brookfield's Critical Incident Questionnaire (CIQ) (1995) allowed us to identify the reasons directly from students. Most students doing SCIE1001 (83%, n = 59) reported that the three most engaging elements of the subject were: 1. content helping them identify high-quality science, 2. the engaging assessments, and 3. the carefully curated online explanatory videos.
The collaborative student learning in face-to-face workshops was also most motivating for 42% of students: 'discussing the assessment task with peers provided useful feedback on how to link content from the workshops into the assessment'.
A key learning outcome in SCIE1001 is to be able to identify the practices of high-quality scientists. Students write a Nobel Prize nomination of their favourite scientist (due Week 6). This requires them to integrate online subject content from Weeks 1 -6. Figure 2 shows that the constructive alignment (Wang et al., 2013) of 1) learning outcomes, 2) online subject material and 3) the assessment successfully engages students with online materials from previous weeks (total student interactions with the online material and the number of modules accessed increases). As the subject progresses and students prepare their first assessment, they not only review the online subject materials for the week they are in, but also review previous weeks' subject materials. The use of online subject modules peaks in Week 6 when the assessment is due as students 'analyse' and 'apply' (Anderson, Krathwohl, & Bloom, 2001) what they have learned from the subject to date (i.e. Weeks 1 -6 inclusive).
Student feedback indicates that the approach to teaching and the subject content can have a profound effect on students' perception of their future career.  Aboriginal and Torres Strait Islander students showed equivalent or higher success (a pass or higher) than non-Indigenous students (85-93% compared to 80%). Students from high, medium or low socioeconomic status also had high levels of success (average of 88%, 84% and 77%, respectively) and low withdrawal (5%, 7%, 7%, respectively). Success or retention were equivalent for students from a range of admission levels (from higher education, from secondary education and from the VET sector). Only mature-aged students had a pass rate 8% lower than the average, triggering more targeted investigations about how to better support these students in the next iteration of the subject.
A key learning outcome of the subject is to develop strong academic writing skills due to its strong links with scientific thinking skills (Dowd, Thompson, Schiff, & Reynolds, 2018). There is significant investment in learning materials to guide students in how to write a strong argument in the Nobel Prize nomination assessment task. Analysis of the individual lines of the marking rubric was undertaken to measure how many students attained the key elements of high-quality writing. Table 1 shows that 98% of students adequately demonstrated key writing skills (a Pass grade or higher) and 60-70% attained highly developed skills in writing (attained a Distinction or higher). Students could also identify the relevance of subject content in the inquiry-based learning (Zimbardi et al., 2013) they were undertaking in the complementary first year core subject SCIE1002 Multidisciplinary Laboratories. For instance, feedback from students in the focus group discussions indicates that 'the [two first year core] subjects support each other, cross overs are helpful rather than repetitive, and learning in one is often applied in the other'. This is particularly critical, as the relevance of learning must be linked to practice if it is to be of value in an applied discipline such as science (Gottersman & Hoskins, 2017).

Implications for best practice science teaching
Both the literature (Dowd et al., 2018;Gottersman & Hoskins, 2017;Rowe et al., 2015;Zimbardi et al., 2018) and the evaluation of this subject indicate that subjects in which students are taught about high quality scientific thinking and scientific practice can successfully engage and support students' learning if: • the relevance of the subject content is clearly articulated, and high expectations are set • constructive alignment of subject learning outcomes, subject materials and assessment tasks occurs • a high quality, evidence-based curriculum and pedagogy support a range of students from different backgrounds • active, participatory learning helps to engage students in their learning, making it more relevant and meaningful to them as individuals • high quality learning resources that engage students with their learning in a way that's both interesting, fun and connected to practice are curated by educators

Conclusion
The use of a range of scientific thinking skills, which can all be learned, is critical for high quality scientific practice across all scientific disciplines. Students completing these subjects valued the learning in the subject and thought it relevant. An understanding of what high quality science is, knowing about the diverse ways to think scientifically and foundational writing skills can be successfully demonstrated by first year BSc students. Rates of academic success were high across the cohort and were equivalent for groups of students across all but one of the recognised equity groups. High quality learning outcomes have been achieved through embedding an evidence-based pedagogy for best practice science educationactive learning, participatory learning, student-centred learning, constructive alignment and quality formative and summative feedback to students. This outcome is consistent with others' findings reported in the literature.


s2fieldsofstudy: Education Psychology

text:
Building a Scale to Measure the Psychological Effects of the COVID -19 Coronaviruson Omani University Students

The world was surprised by the outbreak of the COVID-19 pandemic. The pandemic started in China and rapidly spread in the world. This pandemic impacted different aspects of life such as the economy, health, and social life. The study aimed to build a tool to measure the psychological effects of the corona COVID-19 pandemic on Omani university students. The sample of the scale consisted of (566) students from different Omani universities. The researcher used an electronic method to distribute the questionnaire, which was Google Drive. Fifty-nine factors were used in its initial form to measure the psychological effects of the pandemic on Omani university students. The results showed that the tool is an appropriate indication of validity and reality. They revealed the results of factor validity and face validity on a scale consisting of (45) items divided into five areas: thinking about the future, pressures resulting from the requirements of the study, restrictions of movement and communication, and concern about the disease. The study recommended that student councilors and people in charge of Omani universities can use this measure to overcome the psychological effects of the pandemic on students and conduct other studies to verify the applicability to other samples.

Introduction
Since the beginning of the Corona pandemic, started in China and spread speedily through the world. This was accompanied by many materials and moral consequences that included all sectors of human life. In addition, this pandemic caused an increase in psychological pressure on the human dealing with it, each of the sides that concerns him and affects his life . Al-Adwan (2020) indicated that the effects of the procedures taken to prevent infection with the virus and to limit social communication operations generated the feeling among many that they were restricted inside their home. Which directly reflected on their mental health.
Historically, it was known that the occurrence and spread of diseases and epidemics in a wide range had their social, psychological, and economic impacts. Due to the fast spread of the COVID-19 virus, it was identified as a novel coronavirus in January 2020. It was first reported in Wuhan, China in December 2019, and in March 2020 the World Health Organization has declared COVID-19 a global pandemic.
The virus has caused global repercussions at the level of the economy, health, social, psychological, and educational levels (Huckins et al., 2020). Studies have found that students were among the groups that suffer from a higher degree of psychological anxiety about learning due to the outbreak of the pandemic. These widespread outbreaks are linked to negative mental health consequences (Yan et al., 2020). The world has taken various preventive measures, including restricting movement, quarantining at home, closing schools, and other social services. Which led to an increase in psychosocial stress, especially among students (Tadesse et al., 2020).
The first cases appeared in the sultanate of Oman in early March 2020. In the middle of March suddenly all schools and universities were closed and shifted to distance learning. That caused some aspects of confusion for teachers and students in terms of learning and its methods. Moreover, it also raised some questions in terms of unknown conditions and a lack of knowledge of what things would be led to. This was accompanied by decisions that limited social communication, meetings, and direct conversations between students, their teachers, and colleagues. The house is transformed into a new university campus. This came with widespread news about the disease and its description, speculation about its extent, conflicting stories about it, and the transmission of the social, economic, and psychological effects of the epidemic . With the global development of the COVID-19 pandemic, it was accompanied by widespread psychological problems, anxiety, depression, fears of disease, lack of certainty and clarity about its interactions and consequences (Grubic et al., 2020).
The pandemic affected many life sectors. One of the important sectors directly affected by this pandemic is the education sector. Education related to location and attendance declined. Students are overwhelmed with challenges and pressures, including university students. Therefore, the educational institutions took quick measures to remedy the responsibilities of education to control the effects of the epidemic. They followed methodologies and methods that were introduced to control the disaster; including social distance and lack of social contact, to slow the spread of this virus (Wilder-smith and Freedman, 2020).
University students were affected by the procedures taken by countries, which affected their psychological state. Their new learning required using programs and mechanisms for learning, studying, research, and evaluation processes for what they learned. Many of them were not familiar with distance learning through educational platforms, which caused psychological pressure on the students, whether because they dealt with the events, losing their daily way of learning that they were accustomed to, losing their enjoyment of interaction in their universities, and among their colleagues, or fear of the future of their education as a result of some new educational methods that they are unfamiliar with which sometimes seem unstable in some educational institutions that change from time to time.
Higher education students in the sultanate of Oman were affected by the pandemic as were other students in the world. In Oman, students can join one of many universities. On top of those universities: Sultan Qaboos University and the University of Technology and applied sciences which include several technical and applied sciences colleges. In addition, there are several private universities and colleges such as Nizwa University, Al-Sharqiyah university the for university and Suhar university. The ministry of higher education supervises some aspects related to licensing, monitoring, and accreditation of private universities. Students join after completing the 12th grade which is considered the final level of school education to do students. The effect of COVID-19 on Oman's higher education students from not being able to attend in person at a campus, repeated lockdown, and distance learning. All those things affected the psychological health of many students which was observed by researchers when dealing with their students. Many studies showed the same observation such as Tadesse et al. (2020), Wang et al. (2020), andHuckins et al. (2020).
Thus, the pandemic affected the mental health and social well-being of university students, with the pressure it puts on them. These pressure factors are directly related to students in terms of education, profession, income, living place, and close communication with people due to social distancing (Zhang and Ma, 2020).
Intervention to help students in such circumstances requires clear knowledge of their psychological state and the effects caused by this pandemic on them. This requires the existence of scales through that clear information and data can be accessed. Then the collected information and data can be used to diagnose their needs and build counseling and educational programs for them.

Requirements to Build the Scales
The data collection stage is an important stage for every researcher. This process can only be achieved through research tools in which they collect their data, which are measurement tools or data collection tools. Those tools include scales, these scales are considered as a tool of collecting data and obtaining relevant information most validly and reliably; that validity and reliability give confidence to the information collected through it. In addition, it provides validity to the results and their interpretation. Validity comes at the forefront of those characteristics, and it is a characteristic that is related to the quality of the scale and the decisions made from the results (Tristán and Vidal, 2007). In contrast, scales that are lacking validity have many biases, also poorly reliable scales have many random errors associated with their measurement (Coster, 2000). Ackerman (1991), mentioned the existence of an inverse correlation between statement bias and its validity. If the scale has evidence of its validity and reliability, it can be counted with confidence in collecting repeatable data, and that the results are accurate and reliable.
The process of building a scale requires direct entry into the aspects of the problem by the scale preparer to know its various aspects. Which can only be measured through its measurable parts. After collecting those parts that are called the scale's statements, it is necessary to ensure the validity of the formulation and construction. That can only be done through examination of the exact scale prepared with all its parts and statements.
The attention to the statements of the scale comes because they are the observed behavioral parts of the potential characteristic to be measured. Moreover, the strength and validity of the scale come from the strength and validity of its statements, which should be well taken care of when building the scales. Each of the statements carries a part of the characteristic, and it contributes with the other statements in the scale to form the total characteristic or the largest part of what can be measured represented by the whole scale (Bani-Atta, and Ibrahim, 2009). So, it is necessary to ensure the validity of the statements of each one separately, as the analysis of the statements is an essential step in building psychological scales. Reliability and validity scales can only be obtained through the validity and stability of its statements (Al-Omari, 2011;Anastasi, 1988;El-Sayed and Fouad El-Bahi, 2014).
The researchers reviewed the existing literature about different aspects of COVID-19 effects. Many studies such as Tadesse et al. (2020), andHuckins et al. (2020) came to a result that the most common effects of the pandemic are depression and anxiety. The pandemic also affected the academic performance of students. As per a study that aimed to reveal the impact of the COVID-19 virus on the academic performance of veterinary students by Mahdy (2020), the results showed that the pandemic had affected (96.5%) of participants with varying degrees.
In China, the epicenter of the first outbreak of the pandemic, Wang et al. (2020) conducted a study aimed at exploring the effects of covid-19 on the mental health of respondents. The results indicate that a sample of health workers showed the repetition of words indicating emotional influences such as anxiety, depression, discontent, and cognitive influences such as social risks and life satisfaction. The results also showed that the respondents are concerned about their health and the health of their families and that their psychological adjustment has decreased. Yan et al. (2020), showed similar results in a study applied on five hundred college students in China during the lockdown period. The results also showed that the negative psychological effects were due to fears of infection, frustration, boredom, insufficient supplies, and insufficient information.
In Ethiopia, Tadesse et al. (2020) conducted a study entitled the psychological effects of COVID-19 among college students in Desi Town, Amhara region. The results concluded that 77.2% of students suffer from depressive disorder, and 71.8%; and 48.5% of students suffer from anxiety and stress disorders respectively. The study revealed that levels of anxiety, stress, and depressive disorders were high, and recommended that local governments should develop effective psychological interventions for students.
Many studies explained anxiety and depression for some identified reasons. In Pakistan, Salman et al. (2020) concluded that the pandemic had a significant effect on students' mental health. The researchers also conclude that anxiety was more in individuals whose family members, acquaintances, or friends were infected. On the other hand, Huckins et al. (2020) found that anxiety and depression increase in the study samples as a result of increasing the pandemic news through the phone and by visiting websites.

Concluding Remarks
After reviewing previous studies by the two researchers, they noticed that most studies deal with the psychological and mental health effects of the COVID-19 virus on students, such as the study of Wang et al. (2020), the study of Salman et al. (2020), Yan et al. (2020) study, andTadesse et al. (2020). Some of them also studied the academic effects of the COVID-19 coronavirus, such as Mahdy (2020). While the study of Huckins et al. (2020) examined the effects of the COVID-19 virus on mental health. Also, a study by Al-Hakkak Jaafar's conscience (2012), and Al-Obaidi et al. (2019) focused on extracting validity and reliability and performing factor analysis.
The samples of previous studies were mostly university and college students, except for the study of , their sample was health care and service providers. Thus, it is clear that studies confirm in many regions of the world the effect of the pandemic on university students using the descriptive survey method, and that the questionnaire is the tool of previous studies.
As for the aspects of benefiting from previous studies, the researchers confirm that these studies were the main reference for a lot of information and knowledge that they benefited from in choosing the problem, formulating it, choosing its methodology and procedures, and directing researchers towards many aspects related to the subject. Therefore, the current study is an extension of those studies in this field and was distinguished from previous studies in its purpose, as it is a psychometric study that aims to build a tool to measure the psychological effects of the Coronavirus pandemic on Omani university students.

The Study
Several studies were conducted to confirm the effect of Coronavirus on the psychological state of university students, the studies of Mahdy (2020), Wang et al. (2020), and Salman et al. (2020). Through the interaction of researchers at the University of Nizwa and communication with colleagues at other universities in the Sultanate of Oman, they felt the need for closest to accurate knowledge about the psychological impact of the pandemic on the students of Oman's universities, which helps in diagnostics, in preparation for the development of therapeutic, indicative and preventive proposals. Where there is no scale available in Oman that measures that effect and the previous studies on the subject did not provide any measures according to the researchers' knowledge, and the old studies provide measures for conditions that may not be suitable with the new pandemic. Hence, the problem of the study is all about the need for a scale of the psychological effect of the Corona pandemic on the students of universities in Oman. This scale has acceptable degrees of validity and reliability indicators.
Previous studies about the psychological effect of the COVID-19 pandemic on higher education students were conducted for students around the world. Oman students are part of the words. So, a specifics study about Oman was needed to acknowledge the social, cultural, and environmental differences.

Study Questions
The current study aims to answer the following questions: 1. What are the evidence of the validity of the scale developed to discover the effects of the Coronavirus pandemic on Omani university students? 2. What are the evidence of the reliability of the scale developed to discover the effects of the Coronavirus pandemic on Omani university students?

Study Aims
This study aimed to build a scale with psychometric properties: validity and reliability that make it valid for measuring the psychological effects of Corona virus on university students in the Sultanate of Oman.

Importance of Study
The scale resulting from this study can add a tool that has acceptable indications of validity and reliability to measure the psychological effects of the Coronavirus. Also, it can help researchers and specialists in their studies and research, and counselors in Oman in diagnosing the psychological state caused by the impact of the Coronavirus on university students in the Sultanate of Oman.

Study Limits and its Determinations
The procedures of this study are determined chronologically by the time of the occurrence of the Coronavirus pandemic, objectively by the events of the pandemic and its psychological effects, and by a place targeting university students in the Sultanate of Oman.

Terms Definition
(CORONAVIRUS EFFECTS) on university students: The effect of the spread of the novel coronavirus disease  can be considered, as result of the closure of universities after the World Health Organization declared it an epidemic and a pandemic. Because of the growing concern about the pandemic, universities in most parts of the world have postponed or canceled all forms of on-campus teaching, teaching has moved to online platforms, and as a result, a potential impact of the pandemic has emerged on the education and mental health of university students and faculty (Sahu, 2020).
It is procedurally defined by the degree that the respondent obtains on the scale used in this study on university students in Oman.
Validity means that the tool is valid to measure what it was prepared to measure, or in other words when the tool accurately measures any variable described in the study and this tool has been allocated, which is the validity of the measurement tool for what it was designed to measure (Robson, 2011). Chakrabartty (2013) indicated that validity means that there is no bias in the results.
It is defined procedurally in this study as the procedures taken logically and statistically to ascertain the relationship of the data resulting from the scale prepared with the measured characteristic.
Reliability: The extent to which the test scores are free from measurement error, which is that the measurement results are stable, consistent, and free from errors, and indicates that the degree obtained from the measurement is the true amount of the measured characteristic, and the greater the stability, the more accurate the results (Haradhan, 2017).
It is defined procedurally in this research as the procedures taken statistically to ensure the accuracy of the scale prepared in extracting the true amount of what the individual possesses of the measured characteristic.

Methodology
The current study relied on the descriptive approach due to its relevance to the nature of the study objectives. This approach is based on describing the studied phenomenon as it exists in reality and analyzing its components using a field survey (Melhem, 2000).
The community consists of all students in Omani universities, both public and private, who numbered (52,000) (National Center for Statistics and Information, 2020).

The Study Sample
To achieve the purposes of the research, the study sample consisted of (566) male and female students, who answered the scale before analyzing it and after the apparent validity test, which produced 53 items, through Google Drive.

The Study Tool
The initial construction of the effects of Coronavirus pandemic scale on Omani university students was completed after reviewing the theoretical literature through several studies, (Abu-Ghali et al., 2016;Ajal, 2015;Bolasal, 2014;Momani and Naim, 2012), The World Health Organization (2015), and the United Nations (2015). Several statements that measure the intended characteristic were derived, which amounted to (59) statements, then the scale was presented with its 59 statements to 6 arbitrators of experts and specialists from the University of Nizwa and Sultan Qaboos University test the apparent validity. After that, six statements were deleted based on the opinion of the arbitrators and their claim that they are repeated with other statements, so the scale was taken out with 53 statements that were presented to five students to note the extent of its readability, clarity, and their reactions to it, and the wording of some items was modified. Then the scale was applied to the sample consisting of 566 male and female students from universities, and after the application and extracting the psychometric properties of the scale and its statements came out with a scale of (45) statements

Scale Correction Standard
The alternative answers were given scores (5 = very high, 4 = high, 3 = don't know, 2 = low, 1 = very low), so that a high score indicates a high level of effectiveness of the pandemic, a low score indicates a low effect of the pandemic and a score of 3 indicates to the average effect of the pandemic. The scale is also judges on the high, low, or medium degree by examining the averages and standard deviations and finding a t-test for one sample with a hypothetical mean of 3.

Application Procedures
The scale is built according to the following procedures: 1. Make sure to build the largest possible number of statements, which reached 49 statements.
2. Extracting the apparent validity of the scale and its statements resulted in 53 statements remaining from the scale.
3. An examination of the readability and clarity of the statements for the target audience was presented to 5 students.
4. The data were analyzed after applying the scale to 566 male and female students from Omani universities, and the psychometric characteristics of the scale and statements were extracted. The result was a scale consisting of 45 statements.

Statistical Processors
After the researchers unloaded the collected data, they were processed statistically using the Statistical Package (SPSS) program, to answer the study questions as follows: To answer the first question, the following analyzes were carried out: Exploratory Factor Analysis to find out the factors to which the statements can be classified and their saturation levels.
The corrected correlation coefficient between the statement and the domain to which it belongs after deleting the statement to determine the validity of the statement.
To answer the second question related to the significance of the scale reliability, it was done: Extracting Cranach's alpha coefficient to calculate the Internal Consistency Coefficient, at the level of the domains resulting from the factor analysis and the level of the scale as a whole.
The statements reliability index was derived by multiplying its correlation coefficient with the domain to which it belongs in its standard deviation.
To answer the first question, which states: What is the evidence of the validity of the developed scale to detect the effects of Corona Virus pandemic on Omani university students?

Study Results and Discussion
To answer this question, several indications of validity were verified, represented by: apparent validity, factorial validity, statement validity, and peripheral comparison as follows:

Virtual Validity
The validity of the scale was verified by arbitration by presenting it in its initial form of (59) statements to six arbitrators with expertise and competence; To judge the statements of the scale in terms of formulation and belonging to its fields. This resulted in a review of the statements scale, so the wording of a number of them was modified and (6) statements were deleted, so that the scale became (53) statements. To ensure the readability of the statements for the students, it was presented to five students who are available under the pandemic, to review the clarity of the statements for the examinees, and then the wording of some of the statements was reviewed.

Factorial Validity
The factor analysis was performed on the statements of the psychological effects of the corona pandemic scale on Omani university students (53) statements, and the coefficient of determination was (DETERMINANT = 1.841E-1), which is greater than zero, and the Kaser-Mayer-Olkin KMO coefficient was 0.958 with a statistical significance = 0.000, which is greater than 0.5. The matrix of correlations between statements showed that there is a large group of correlations between variables greater than 0.30, and the one-dimensionality of the scale was examined where it appeared that the first factor alone explains (20.5%), which allows performing factor analysis on the scale and its statements to fit the data for the model. The factors were extracted by the Principal Component Analysis method, by using the criterion of latent roots values (EIGENVALUES) for the factor greater than one, and the saturation criterion for the statement with the factor greater than or equal to (0.40). The preliminary analysis resulted in getting nine factors, but it is not meaningful because either there are statements saturated in more than one factor, factors in which one statement is saturated or other statements are not saturated in any factor.
So the factors were rotated orthogonally (ORTHOGONAL) by ARIMA method X), which gave the factorial structure greater clarity that made it possible to review the saturation of the statements with their factors, as it was found that there are still some statements saturated in more than one factor and with simple differences in their saturation, presence of other statements saturated alone in only one factor, and other statements that are not saturated in any factor. So those nine statements were deleted, which are the numbers (3,4,5,6,17,18,42,43), and after deleting those statements, the re-analysis and rotation again resulted in five factors that explained the amount (61.94%) of the total variance for the statements. Table (1) shows the rounded factors matrix and the saturation of the statements with the factors.  (1) that the factor analysis produced factors whose number settled in five factors and statements, which amounted to (45) after deletion. Those factors are: The first factor explained 20.5% of the variance, it contained (15) statements whose saturation values ranged between (0.79) and (0.56), the statements of this field related to the concern directed towards the academic future, and thus it can be called the field of thinking about the academic future. The second factor explained 18.940% of the variance and included (14) statements whose saturation values ranged between (0.81) and (0.55). , these statements deal with the various problems resulting from the implementation of the requirements of the current education state, hence this field can be called the pressures resulting from the requirements of the study. The third factor explained 11.432% of the variance, and contained (8) statements whose saturation values ranged between (0.78) and (0.56), its statements related to meetings, visits, and movement in general, it can be called the field of movement and communication restriction. As for the fourth factor, it explained 6.446% of the variance, and it consisted of (4) statements whose saturation values ranged between (0.75) and (0.47), the statements of this factor related to something of relief through meetings, so this field can be called In social meetings. The fifth factor explained 4.701% of the variance, and it has (4) statements whose saturation values ranged between (0.63) and (0.42), the statements of this field related to feeling some anxiety about the disease, so this field can be called anxiety about disease.
The component of this scale agrees in the existence and the quality of their psychometric properties with the previous studies conducted on the psychological impact of the COVID-19 pandemic on higher education students. Including the study of Tadesse et al. (2020), Wang et al. (2020), andHuckins et al. (2020). Through which evidence of validity and reliability are presented. This agrees with (Al-Hakkak Jaafar's conscience, 2012) that it is important to use factorial analysis because of its importance in experiments. It also provides reliable data and accurate measurements for the scale and its statements (Al-Obaidi et al., 2019).

The Statements Validity
To extract the validity of the statement, the correlation coefficient was calculated between the degree of each statement and the total degree of the dimension to which it belongs after deleting the statement, and Table (2) shows the results of that.

Peripheral Comparison
The peripheral comparison represents one of the ways to verify the validity of the scales, and to calculate this validity, the sample scores were arranged in descending order for the scale as a whole, then it was divided into two equal groups, then 150 were selected from the upper category and 150 from the lower category, which represents 27% of the total sample size in each category, and then the discriminatory ability of the scale was examined using the "T" test for independent samples, as shown in Table ( 3 .)   (3) shows that there are statistically significant differences at a significance level of less than (0.001) between the average scores of the upper class and the class, and from this it can be said that the scale has a discriminatory ability between students who were affected by the Corona pandemic and those who were low affected.
It is clear from the previous analyzes related to validity that the scale with its settled number of statements (45) has many indications of validity represented in the apparent validity, factorial validity, statement validity and terminal differentiation validity, which makes the scale valid and reliable to measure what was prepared for it, which is to collect information on the impact of Corona virus pandemic on Omani university students.
To answer the second question, which states: What are the indications of the stability scale developed to detect the effects of the Corona Virus pandemic on Omani university students? To answer this question, Cronbach's alpha coefficient was extracted to calculate the internal consistency coefficient of the scores on each dimension and the total score of the tool, and the stability of the statements was extracted.

The Internal Consistency of the Scale and its Domains
The whole stability of the scale and its five domains were calculated by Krumbach's alpha equation, and the table shows this.  (4) shows that the values of the stability coefficients for the degree of dimensions ranged between (0.76 -0.96), and the stability coefficient for the total degree on the tool was (0.97), and these values are acceptable for the purposes of these studies.

Paragraph Stability Coefficient
The item stability index was calculated by multiplying its validity coefficient by its standard deviation, (Al-Omari, 2011) quoting from ALLEN, YEN, 1986, and table (5) shows the stability of each statement  (5) shows the stability indicators for each of the items on which the scale settled after analyzing the validity, which are (45) items. The stability indicators ranged between (0.50) and (0.98), which are indicators that make the scale valid for measuring the intended characteristic and make its results stable that contribute to know how much the respondents carry from the measured characteristic.
This agrees with what mentioned by Al-Omari (2011) and Busschaert et al. (2015). That it is important to refine the scale statements by dealing with each statement and find its reliability and validity.

Conclusion and a General Summary of the Results
Through the previous results, the two questions of the study were answered, and a scale with acceptable psychometric properties was built and applicable within the limits of the society in which it was codified. This scale agrees in its subject and fields with the previous studies, as some of the names of its fields were mentioned in the previous studies, Salman et al. (2020) study, Yan et al. (2020) study andHuckins et al. (2020) study.

Recommendations
This study aimed to build a tool to measure the psychological effects of the Corona pandemic on students of Omani universities, and from the results of the study, a set of recommendations were reached, the most important: Benefiting from this scale by student guidance administrators and university administrators in the diagnosis process in terms of identifying the psychological effects of the Corona pandemic on Omani university students, and thus through which appropriate counseling programs can be developed to overcome the problems faced by students that have a psychological effect on them.
Although the scale is a valid psychometric tool that can be adapted for use in studies related to the psychological effects of the COVID-19 pandemic, the researchers suggest conducting other psychometric studies to ensure the scale's applicability to other samples.


Learning Styles and Listening Strategies of EFL Learners—A Survey Study in a Medical College

In order to find out the differences in medical college students’ learning styles and listening strategies and whether there is any correlation between their learning styles and listening strategies, this study is carried out. Two questionnaires are adopted in this study to survey the different learning styles and listening strategies among medical college students. Based on the experimental data, we conclude that medical college students adopt various learning styles and listening strategies in English learning and there is a strong correlation between learning styles and listening strategies. Namely, when students choose a certain learning style, they tend to adopt a corresponding listening strategy. Therefore, effective measures are put forward to help students choose proper listening strategies by choosing a suitable learning style.

INTRODUCTION
A lot of researches on second language acquisition focus on the individual difference among EFL learners [1]. In recent years, more and more researchers attach a lot of importance to how learning styles and learning strategies affect language learning [2]. With a shifted focus on how learners accomplish their learning tasks, the ESL/EFL classroom researchers agree that learners' making full use of their ability is critical to effective language learning. When Reid noticed that ESL students from different cultural backgrounds showed different preferences for certain learning strategies, she called for further studies in this field [2]. Since then, the researches on the correlation between learning styles and learning strategies have obtained great achievements. Many empirical studies have revealed that learning styles have close correlation with learning strategies [3]. Although listening strategies are regarded as important components of learning strategies [4], few studies are researching the learning styles and listening strategies among medical students. At the same time, studies on the characteristics of successful language learners have proved to be a useful way to investigate how strategies affect language learning and one of the most effective ways to investigate learning strategy [5]. Medical students have taken an increasing proportion among college students in China in these years, and they are also EFL learners at different language levels. Therefore, it is very necessary to find out the differences of medical students' learning styles and listening strategies.

Definition of Learning Styles
Since the concept "learning style" was put forward by Herbert Thelen, more than 30 kinds of theories and models concerning learning styles have emerged one after another (cited from Tan Dingliang) [6]. Different researchers held different views on the definition of learning style. Reid indicated that learning styles referred to an individual's natural, habitual, and preferred ways of absorbing, processing, and reserving new information and techniques [2]. Tan Dingliang defined the learning style as the learners' consistent, habitual, and characteristic ways of learning, which was the sum of learning strategy and learning tendency [6]. From the above definitions, a conclusion can be drawn: learning styles are the natural, habitual, preferred and relatively stable ways of individuals to absorb, process, and retain new information and skills; they contain cognitive, affective, physiological and social elements and are potential causes of learning behaviours.

Definition of Listening Strategies
Listening strategies are important parts of learning strategies, and they are developed based on learning strategy researches. Therefore, the definition and classification of listening strategies correspond with those of learning strategies. Ellis pointed out several characteristics to define language learning strategies, which are also applicable to the definition of listening strategies [7]. Hence, listening strategies can be defined as the particular methods or skills that learners use to enhance their listening comprehension capacity. They can be behavioural (for instance, taking notes while listening to help the listener to remember) or mental (for instance, relating personal knowledge or world knowledge to the listening material to improve understanding).

Subjects
64 freshmen from Chengdu Medical College (CMC) are the subjects majoring in clinical medicine, nursing, preventive medicine and pharmacy. The vast majority of them are between 19 and 22 in age. They all have studied English for more than ten years.

Data Collection and Analysis Procedures
All the subjects received standard listening comprehension test as the pre-test to identify the subjects' listening comprehension proficiency. 64 subjects were divided into two groups according to their test performance. The successful listeners included 32 students (scores ≥ 20), and the other 32 students formed the less-successful listener group (scores ≤ 7.5).Two questionnaires were distributed at different time within one week. Among the 66 copies of distributed questionnaires, 64 responses were collected and valid, in which there are 32 successful and 32 less-successful listeners. The collected data were processed by the Statistics Package for Social Sciences (SPSS25.0). The analysis procedures were stated as follows: Descriptive statistical methods, including Minimum, Maximum, Mean and Standard Deviation, were used to analyze the frequency of students' listening strategies and the patterns of students' learning stylise. Pearson correlation analysis was used to predict the relationship between the successful and less-successful EFL learners' leaning styles and listening strategies.

General Trend of Subjects' Preferred Learning Styles and Listening Strategies
The PLSP investigated students' six learning styles: Visual, Auditory, Kinesthetic, Tactile, Group and Individual styles in successful and less-successful listeners. From Table 1 Among the five strategies, the mean of meaning-focused strategies are 4.03, ranking the first place, followed by social/affective strategies (M=3.68), mother-tonguereliance strategies (M=3.65), metacognitive strategies (M=3.08) and form-focused strategies (M=3.06). The Standard Deviation value indicates that the smallest difference is social/emotional strategies, while the biggest difference is mother-tongue dependent strategies.
Data from Table 2 show that all strategies are used. Students are multi-strategy-used learners. The subjects especially prefer meaning-focused strategies. Form-focused strategies are the least preferred strategy for the successful and less-successful listeners of medical students.

Relationship between Learning Styles and Listening Strategies
In order to investigate the listening strategy differences in EFL learners with different learning styles, correlation analyses were conducted and the results were displayed in Table 3. Students with Visual styles use elaboration strategy of meaning-focused strategies more often than other styles. It means that Visual learners like to choose elaboration strategy. Visual style is not correlated with metacognitive, mother-tongue reliance and social/affective strategies. Students of Auditory styles use form-focused strategies and social/affective strategies more often than other styles and auditory styles have the strongest correlation with form-focused strategies, social/affective strategies and meaning-focused strategies. Concerning form-focused students with auditory style prefer to use grammar strategy more than new word strategy, which indicates that during conducting a listening task Auditory students usually choose the grammar strategy more frequently than new words strategy. Auditory style has no relation with mothertongue reliance strategies Kinesthetic style students prefer to use meaning-focused strategies. Kinesthetic style has no relation with other four strategies.
Students of Tactile styles prefer to use metacognitive strategies, meaning-focused strategies, mother-tongue reliance strategy and Social/affective strategy. Tactile learning style has the strongest correlation with meaning-focused strategies. To the metacognitive strategies, students with Tactile style prefer to use monitoring strategy and planning strategy most. Tactile

Advances in Social Science, Education and Humanities Research, volume 516
style has no relation with form-focused strategies. Data clearly reveal that Tactile-style students prefer to choose mother-tongue-reliance strategy. For English teachers, they ought to pay attention to the situation and help Tactile style students avoid using the mother-tongue-reliance strategy which usually hinders students' listening comprehension; for Tactile style students, it is indispensable for them to realize this correlation and try their best to adjust these listening strategies.
Group style students prefer to use self-evaluating strategy and prediction strategy more often than other styles and it is the strongest correlation with meaning-focused, social/affective strategies. Group style is not correlated with form-focused strategies. Individual style students prefer to use elaboration strategy of meaning-focused strategies. Individual style has no relation with metacognitive, mother-tongue reliance, social/affective strategies. Based on the data analyses and the above discussion, we can see that Auditory, Tactile and Group styles have strong impact on learning strategies. These learning strategies include metacognitive, form-focused, meaning-focused, and social/affective strategies. The result coincides with Oxford's. She believed it was likely that a strong relationship existed between the individual's use of learning strategies and individual's learning styles [9].

CONCLUSION
The study indicates the subjects adopt multiple learning styles and listening strategies instead of a single learning style and listening strategy, which is instructive for English teachers to conduct teaching, since multiple-style learners with multiple listening strategies can better improve their English listening than single-style learners with single listening strategy. Learning styles and learning strategies have positive correlation with each other. The learning styles, as Kinsella said, when teachers introduced knowledge or skills to students through their strongest perceptual preferences and then reinforced it through secondary preferences, students learned more easily and retained far more [10]. Therefore, teachers should pay attention to students' learning styles in strategy training and make students aware of their own learning styles and learning strategies, and strengthen their favorite learning styles and strategies. The purpose is to make students become more efficient and automatic language learners. Teachers can help learners improve or adjust their listening strategies as follows:  When performing listening strategy training, teachers need to help students, especially Tactile and Group students, know the negative effects of mother-tongue-reliance strategy and help students get rid of it and choose more helpful strategies.
 Teachers need to strengthen Visual, Kinesthetic, Individual and Group students' consciousness of metacognitive strategy.
 Meaning-focused strategies consist of inferencing, elaborating and predicting strategies and play an important role in listening. When conducting listening strategy training or teaching in the classroom, language teachers should emphasize the importance of this strategy and tell students, especially Visual students, how to employ this strategy.
 Social/affective strategy is encouraged when listening and it is necessary to recognize the importance of this strategy for students, especially those with Visual, Kinesthetic and Individual learning styles.
In order to learn as effectively and efficiently as possible, students should identify their perceptual learning preferences, and then select learning strategies which will help them master new information by using their preferences. Students should develop flexible styles and "stretch the comfort zone" to diversify their style preferences. It is important for them to know their listening strategies and help them to choose more effective listening strategies and get rid of the ineffective ones under language teachers' help.
In conclusion, this investigation only dealt with a small part of students' learning styles and listening strategies， yet the results of this experiment are definitely convincing. Because of the limited number of experimental samples, the research results need to be further confirmed. Moreover, the results were obtained from two questionnaires, which may just represent the subjects' thoughts instead of the actual use of strategies or preference of styles. Therefore, no matter how carefully the teachers explained the subjects' understandings about questions and choices of Liken Scale could not be absolutely precise. Hence, other research methods, such as learning diaries, interviews, verbal reports and observations should be adopted to broaden the varieties of instruments to collect more abundant data and fully and precisely describe the listening strategies and learning styles. With more investigation devoted to this subject and the improvement of the research methodology, it is likely to have a better and deeper understanding of the issue of learning style and listening strategy.