Regionlets for Generic Object Detection
Summary (4 min read)
Participants
- The participants of the study include 869 early childhood education teachers who work in public preschools in Turkey gathered through simple random sampling.
- Nearly 95% of the participants were working in state preschools and nearly 5% were working in private schools.
- Participants were from 7 regions of the Turkey with the following percentages: 14.7% from Mediterranean Region, 10.1% from Eastern Anatolia Region, 17.9% from Aegean Region, 7.9% from Southeastern Region, 16.6% from Central Anatolia Region, 14.1% from Black Sea Region, 18.7% from Marmara Region.
- Nearly 59% of the participants were married, 37% were single, and nearly4% were divorced or widowed.
- 94% of the participants were actively working with children.
Instruments
- In order to examine the individual and contextual variables, which might have a relationship with burnout, a demographical information questionnaire was used.
- The questionnaire included questions about age, marital status, monthly income, educational level, educational background, years of experience, housing status, being a parent, region of residence, type of school and current position at work.
- In order to explore the burnout phenomenon among early childhood teachers Maslach Burnout Inventory (MBI) which was developed by Maslach and Jackson (1981) was used for the study.
- The MBI is 22-itemed, 7-point Likert-type scale and is the most frequently used instrument to assess burnout worldwide.
Data collection
- The schools that participants work were chosen from the list of schools serving children from 3 to 6 years, obtained from the Ministry of National Education through random sampling.
- The participants were asked to participate in the study on a voluntary basis.
- For the ethical purposes and to protect the participants’ confidentiality and anonymity, participants were given information about the study, informed that they are free to participate or not to participate the study, and were asked not to give any identifying information and to return their responses in the closed and sealed envelopes provided by the researchers.
Data Analysis
- Data for the study were analyzed using SPSS for Windows, version 16 (SPSS Inc., Chicago, IL).
- The descriptive statistics, frequencies and percentages of categorical variables and the means and standard deviations of numeric variables were calculated.
- Numerical variables were investigated by independent sample t-test and one-way analysis of variance .
- When the statistically significant difference was found in one-way ANOVA, pairwise comparisons were performed using post hoc Tukey HSD test.
RESULTS
- The purpose of the study was to explore the individual and contextual variables gathered through demographic information questionnaire that contribute to burnout of early childhood teachers in Turkey.
- The individual and contextual variables that were included in the questionnaire were gender, marital status, geographical location, being a parent, educational level and background, current position at work, actively working with children, age group of children, years of experience, work experience in integrated classrooms, monthly income and ownership of the residential house.
Burnout and gender
- T-Test was used to compare male and female participants for their scores from MBI subscales, and the results are given in Table 1.
- As given in Table 1, there are significant differences between means scores of male and female participants for EE subscale but no significant differences for DP and PA subscales.
Burnout and marital status
- One-way ANOVA was applied to determine any significant differences among single, married and divorced or widow participants for their scores from MBI.
- Any significant differences found among these groups were tested by means of post hoc Tukey HSD test to determine any differences between two groups in triple groups.
- As given in Table 2, there are significant differences among single, married and divorced or widow participants for their scores from EE and DP subscales but there is no significant differences for the PA subscale.
- For EE subscale, the mean scores of single teachers were significantly lower than the mean scores of married teachers.
- For DP subscale, the means scores of single teachers were significantly lower than the mean scores of married and divorced/widow teachers.
Burnout and Geographical Regions
- One-way ANOVA was applied to determine any significant differences among participants according to geographical region for their scores from MBI subscales.
- Any significant differences found among these groups were tested by means of post hoc Tukey HSD test to determine any differences between two groups.
- For EE subscale, teachers from Southeastern Anatolia Region had significantly lower scores than the teachers from Marmara and Eastern Anatolia Regions, and teachers from Aegean Region had lower scores than teachers from Eastern Anatolia Region.
- For y DP subscale, teachers from Marmara Region had significantly higher scores than teachers from Aegean, Black Sea and Southeastern Regions.
- For PA subscale, teachers from Central Anatolia and Eastern Anatolia Regions had lower scores than teachers from Aegean and Mediterranean Regions.
Burnout and Being a Parent
- T-Test was used to compare teachers with and without children for their scores from MBI subscales, and the results are given in Table 4.
- As given in Table 4, there were significant differences between mean scores of teacher with and without children for EE and DP subscales but no significant differences for PA subscales.
Burnout and ECDE training
- T-Test was applied to determine whether there are any significant differences among participants according to the field from which teachers graduated for their scores from MBI subscales.
- As given in Table 5, teacher graduated from a child development and education department had significantly lower scores for EE subscale than teachers not graduated from a child development and education department, and there were no significant differences between teachers graduated from different areas for DP and PA subscales.
Burnout and Educational Level
- One-way ANOVA was applied to determine any significant differences among participants according to their educational levels for their scores from MBI and its subscales.
- Any significant differences found among these groups were tested by means of post hoc Tukey HSD test to determine any differences between two groups.
- As given in Table 6, there are significant differences among teachers according to their educational levels only for scores of EE subscale.
- For scores from EE subscale, teachers with high school educational level had significantly lower scores than teachers with Associate and BSc/PhD degrees.
Burnout and Current Position at Work
- One-way ANOVA was applied to determine whether there are any significant differences among participants according to their position at work for their scores from MBI subscales.
- Any significant differences found among these groups were tested by means of post hoc Tukey HSD test to determine any differences between two groups.
- As given in Table 7, there are significant differences among participants according to their positions for total scores from EE & DP subscales.
- For scores from EE and DP subscales, covenanted teachers had significantly lower scores than managers and teachers, and teachers had significantly lower scores than managers.
Burnout and Active Teaching Experience
- T-Test was applied to determine whether there are any significant differences among the participants who are actively working with children or not working with children for their scores from MBI subscales.
- As given in Table 8, there was no differences between teachers actively working with children and teachers not actively working with children for scores from MBI subscales.
Burnout and Age Group of Children
- One-way ANOVA applied to determine any significant differences among participants according to the age groups they worked with (mean age groups: under three years, three years, four years, five years, six years and mixed ages) for their scores from MBI subscales.
- Any significant differences found among these groups were tested by means of post hoc Tukey HSD test to determine any differences between two groups.
- As given in Table 9, there were significant differences between teachers only for the scores from DP subscale.
- Teachers working with children under three years had significantly lower scores than the teachers working with children 4 and 5 years old.
Teacher burnout and Monthly Income
- One-way ANOVA applied to determine any significant differences among participants according to their monthly income for their scores from MBI subscales.
- Any significant differences found among these groups were tested by means of post hoc Tukey HSD test to determine any differences between two groups.
- As given in Table 10, there were significant differences among teachers according to their income level for their scores from EE and DP subscales.
- For the scores y from DP subscale, there are significant differences between the teachers with the lowest income level and the teachers with the income level of YTL 2500- 3999.
- P<.05 Burnout and Experience with Children with Special Needs t-Test was used to compare teachers with and without experiences with handicapped children for their scores from MBI subscales, and the results are given in Table 11.
CONCLUSION
- It is a commonly acknowledged fact that teacher burnout is among the major factors that influence teachers’ performance, health and well-being regardless of the country and the grade level.
- Prior studies (Kokkinos, 2007; Noor & Zainuddin, 2011) suggest that being married and female increases the likelihood for teachers to experience emotional exhaustion and depersonalization, as a result of the conflicting demands of work and family, not because of the marriage itself.
- In other words, depersonalization scores of teachers increase as the educational level increases.
- Class size is a significant variable to explain burnout.
- Findings suggest that covenanted teachers have significantly lower levels emotional exhaustion and depersonalization than managers and teachers, and teachers have significantly lower scores than managers.
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Frequently Asked Questions (12)
Q2. What are the future works mentioned in the paper "Regionlets for generic object detection" ?
As a future work, the authors plan to improve the way of proposing bounding boxes in term of recall and speed. Second, the authors will investigate how the context information can be integrated into the boosting learning process for further improving detection performance.
Q3. What is the way to extract the features of a rectangle?
A rectangle feature extraction region inside the bounding box is denoted as R, which will contribute a weak classifier to the boosting classifier.
Q4. What is the method used to train the detector?
In their implementation of regionlet-based detection, the authors utilize the selective search bounding boxes from [25] to train their detector.
Q5. What is the general principle of the selective search approach?
The selective search approach first over-segments an images into superpixels, and then the superpixel are grouped in a bottom-up manner to propose some candidate bounding boxes.
Q6. Why is generic object detection an open problem?
Despite the success of face detection where the target objects are roughly rigid, generic object detection remains an open problem mainly due to the challenge of handling all possible variations with tractable computations.
Q7. What is the way to find objects?
Since the resolutions of the object templates are fixed, an exhaustive sliding window search [12] is required to find objects at different scales and different aspect ratios.
Q8. What is the way to extract descriptive features of an object?
When large variations especially deformations occur, a large rectangle region may not be appropriate for extracting descriptive features of an object.
Q9. Why does ImageNet perform well in both cases?
Due to the regionlets representation and enforced spatial layout learning, their proposed approach performs perfectly in both cases.
Q10. What is the fundamental problem of object class representations?
These pose a fundamentaldilemma to object class representations: on one hand, a delicate model describing rigid object appearances may hardly handle deformable objects; on the other hand, a high tolerance of deformation may result in imprecise localization or false positives for rigid objects.
Q11. How many object categories are included in the proposed method?
The proposed method is further validated on the much larger ImageNet object detection dataset (ILSVRC2013) [21], including 200 object categories.
Q12. How many window detectors are used in the sliding window search?
This approach typically produces 1000 to 2000 candidate bounding boxes for an object detector to evaluate on, compared to millions of windows in an exhaustive sliding window search.