This design thinking project is an innovative research activity. The research activity happens to be integrating aspects of well-developed projects. They include:
Advanced Computer Vision is used within instruments that are particularly developed for the purpose of detecting emotions of students including confusion, interest, boredom, and frustration (McClendon & Ho, 2016). On the same, studies performed by The Center for Innovative Research portray that at the time a computer tutor makes a response to student affect that is negative, the performance of such a student is improved. However, this project will expound its focus to get established beyond emotions alone (Ronda, 2015). Other factors that will also be detected, in this case, include self-efficacy, the ‘grit’ trait, and persistence. The highlighted constructs will have their impacts on learning processes of students explored. The project also tries to determine if the grit trait is able to be improved as well as determine if the same factor depends on other emotional factors. To achieve everything required in this activity, this activity uses the Smartutors as a tool that would ensure that technological innovation is achieved. The tool applies advanced techniques of computer vision to look at the student’s hand gestures, gaze, face, and head, to widen the “bandwidth” that would ensure that the computer system efficiently and automatically detect student’s affect. The main objective, in this case, is to have students reoriented to attitudes that are more productive on the onset of waning attention (Ronda, 2015).
A study performed in 2009 in the US showed that Grit (which is the tendency of an individual to go for goals that are long-term with hard work and zeal) was used to forecast level of academic achievement, avocational, and vocational domains. Student’s effectiveness would be predicted and the same study showed that grit and talent were totally unrelated. This activity will, first and foremost, compare and contrast two high school students’ relative grittiness (they are from two different settings) (Flyvbjerg & Budzier, 2011). One of the students is from a technical and non-traditional high school setting while the second is from a traditional suburban setting. Secondly, the activity will send fifty Grit-S survey questions to sample students from the two high school settings. This is to have their grit level determined. Third, the technical staff, administrators, and teachers would then be interviewed to have their perceptions on grit determined (Flyvbjerg & Budzier, 2011).
There are few or no documented studies that calculate and analyze the differences existing between grit levels of traditional and non-traditional (technical) high school students using the Smartutor’s tool. This activity will, therefore, analyze the responses of students using the validated Smartutor. Using the advanced computer vision system identified, the innovative basis would regard:
Assessments of Smartutor’s predictive validity using the assigned samples.
The tool will have to provide predictions on grit levels with regards to school settings and their rigorous academic training programs. From the same, the Whole Candidate Index is calculated. According to the identified tool, we would be able to determine the manner in which academic ability of students as well as motivation (as calculated from the Whole Candidate Index) can be implicated achievement predication. This innovative tool has been thoroughly studied and its ability to measure grit intensively and extensively determined. Regarding the mechanism of the Smarttutor, the tool uses a two-factor arrangement methodology for the purpose of providing a self-reported (12-item) measure of Grit-O. We have ensured that the arrangement of the whole process happens to be consistent with Grit theory as a trait that is composite and made up of stamina in the context of effort and interest. From further analysis, we also validate the use of the Short Grit-Scale as a better way of measuring grit. To validate its efficiency, Grit-S was used to measure four grit levels of four student samples. In earlier trial versions, the utilized Grit-S happened to be shorter. To have its statistical strength increased, a twelve-item Grit-Scale was introduced. Under no condition will the items reduction from Grit-O to Grit-S affect predictive validity. In such a case, the longer version proved better than the shorter one.
After thorough research regarding the proposal, it has been established that the system in place has the ability to identify the grit differences between students from the identified learning settings. The research leading to the identification of Smartutor, placed its focus on certain criteria. Such criteria include: dropout rate, grades, attendance, and transcript credits. Prior studies also showed that students who were offered technical forms of education happened to be less motivated and thus were at risk of leaving school. Using the CTE (Proponents of Career and Technical Education) programs, the system was able to determine the effect of learning environment with regards to obtaining the grit level.
Our launch would include a three-item Grit-Survey. The Grit Scale, as used in the entire study process is however, still vital. Again, though short, it provides as much anticipated a result as a 12-item scale. The direction of the study, as well as the questions identified to prove the innovative system has been supported by the survey results. The system will therefore:
The entire system, as constructed, is integrally made to be able to provide predictive validity. It is developed with an internal consistency of +/- 0.83 which is computed through the samples. The system is also intended to have regression coefficients that are unstandardized and also aligned to grit scores. The performance ranges should be consistent and the obtained range not beyond +/-0.22 to +/-0.55. The range of the odd ratios also needs to be from +/- 0.80 to +/-1.73. As mentioned before, the system will be tested in sample schools; of which the identified settings mentioned before will be maintained. These schools will be:
To ensure the efficiency of the system:
Using Grit-S is portrayed to give a proper predictive validity if used in institutions henceforth. To prove the same, the study acquired its permission from the Administrator of New Initiatives and Program Evaluation to use the Smartutor tool to prove the efficiency of Grit-S (Kaur, 2015). Apart from that, the collected data clearly provide the results intended in the research. Students had the surveys completed via computers either within the school computer labs or at home. No one was required to be coaxed to do the surveys, so the students were allowed to complete the tests at their own perils. Using the z-test, the mean differences were then sorted and the students’ Grit scores obtained. All the responses had to be scripted by the researchers (Limbach & Jong, 2014). Regarding the responses from the teacher interviews conducted, the obtained results were coded qualitatively via open coding techniques after which commonalities were obtained from the data arrays. The 3-item Grit-S included the following
In this case, the formula below is used to obtain the Correlation Coefficient.
From the formula, with regards to the data obtained from the respondents, the same can be calculated. The emotional values are as below.
We then calculate the correlation coefficient (r) by expounding the above set of data as below:
Procedural calculations then follow:
Because r ≈ -1, it is worth noting that a negative relationship exists between the variables.
The Smartutor Tools
In this proposal, we have identified one major tool to be applied so as to achieve whatever analysis required. In this case, the model used is the Smartutor. It is via the same model that the entire proposal is initiated (Messnarz & Ekert, 2007). The Grit-S Scale that works alongside the computer mandate model will (as mentioned before) validate the model’s efficiency. This is so because Grit-S measures the grit levels for student samples. This Grit version is longer, unlike the earlier trial versions which happened to be shorter. To have its statistical strength increased, a twelve-item Grit-Scale was introduced. Under no condition will the items reduction from Grit-O to Grit-S affect predictive validity. In such a case, the longer version proved better than the shorter one (Scheib, 2010).
Work Plan
The plan below is tentative. It can therefore undergo further scrutiny and then changed during any particular research process. The time spans can also be adjusted with regards to the requirements of the activity.
|
Activity |
Duration |
|
Research topics determination |
30days |
|
Developing the design grit system |
60days |
|
Developing the Grit-S system |
25days |
|
Development the Smartutor system design |
30days |
|
Complete Advanced Computer Vision System |
65days |
Having the research fully completed require a massive amount of financial input. The financial input required in this case is:
Research topics determination and analysis = $271,200.
Developing the design grit system = $51,000
Developing the Grit-S system = $100,000
Development the Smartutor system design = $69,100
Complete Advanced Computer Vision System = $ 150,200
Conclusion
In summary, the identified proposal project takes the mechanical and technical aspects of the design thinking research activity. The activity uses Advanced Computer Vision within instruments that are particularly developed for the purpose of detecting emotions of students including confusion, interest, boredom, and frustration to achieve the objectives of the proposal. The focus here is to use the Smartutors to ensure that technological innovation is achieved (Boundless, 2017). The tool applies advanced techniques of computer vision to look at the student’s hand gestures, gaze, face, and head, to widen the “bandwidth” that would ensure that the computer system efficiently and automatically detect student’s affect. According to the identified tool, we would be able to determine the manner in which academic ability of students as well as motivation (as calculated from the Whole Candidate Index) can be implicated achievement predication (Scheib, 2010).
Reference
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Hamel, G (2017). Example of a Business Contingency Plan. Smallbusiness.chron.com. Retrieved 13 June 2017, from: https://smallbusiness.chron.com/example-business-contingency-plan-12292.html
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Kamboj, P. & Singh, S. K. (2015). Effectiveness of Selected Teaching Strategies in Relation to the Learning Styles of Secondary School Students in India. Interchange, 46(3), 289-312. Retrieved from https://link.springer.com.libraryproxy.griffith.edu.au/article/10.1007%2Fs10780-015-9253-7
Kampung, W. (2017). The Science of the 5Gs. WOW Kampung. Retrieved 13 September 2017, from https://wowkampung.com/5gs/science/
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Levine, B. (2017). The Positive Power of Graciousness | SmartMouth Communications. Smartmouthcommunications.com. Retrieved 13 September 2017, from https://www.smartmouthcommunications.com/the-positive-power-of-graciousness/
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Margie (2012). Classroom Management Techniques for Group Work. Bright Hub Education. Retrieved 13 September 2017, from: https://www.brighthubeducation.com/classroom-management/19616-group-work-techniques/ .
McClendon, K., &Ho, T. (2016). Building a Quality Assessment Process for Measuring and Documenting Student Learning. Assessment Update, 28(2), 7-14.
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Messnarz, R., &Ekert, D. (2007). Assessment-based learning systems—learning from best projects. Software Process: Improvement and Practice, 12(6), 569-577.
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