Chat with us, powered by LiveChat Proposal Reflection Assignment Write a 2-3 page reflection to comment on the similarity report gen | Max paper
  

 

Proposal Reflection Assignment

Write a 2-3 page reflection to comment on the similarity report generated by Turnitin. 

What does this report say about the originality of your work as it exists now? What do you need to do to improve on the current condition of the work? What support do you need and what would you do to get the needed support? (Please be detailed and specific).

23

Increasing AI Agriculture in Emerging Countries and Countries with Low Economy

Submitted by

Sateesh Rongali

A Proposed Study Presented in Partial Fulfillment

of the Requirements for the Degree

Doctor of Education/Philosophy in Leadership

with a specialization in Computer Science

Judson University

Elgin, Illinois

08-15-2021


Abstract

This research study focuses on exploring the field of AI agriculture from an emerging countries’ standpoint. The goal of the research study is understanding the reason for the decline in agricultural productivity and popularity in emerging countries and exploring how AI agriculture can help the countries improve agricultural processes. The research study will also explore the major limitations that have restricted the adoption of AI agriculture in these emerging countries. After providing a brief introduction into the current state of agriculture in emerging countries, the research study defines the core research questions that would drive the study. To gain further insights into agriculture in emerging countries and the limitations of AI adoption, the research study provides an in-depth literature review that explores literary sources focused on the relevant topics. The main research methodology of the proposed research study will be document analysis that will identify the relevant themes in both historical and current peer-reviewed literary sources exploring the topics of AI agriculture, agriculture in emerging countries, and agricultural limitations. In addition, the research study will also conduct qualitative interviews to participants selected from the AI agriculture industry. To ensure that the research study is focused on emerging countries, the proposed study will ensure that the document selection is strictly based on topic and thematic relevance. The participants for the interviews will be selected through snowball sampling. In addition, the proposed study also provides brief insights into the expected limitations and ethical considerations surrounding the research. Through the research methodology, the proposed study aims to arrive at valid and reliable results that helps identify AI agricultural methods that can improve agricultural production and popularity in emerging countries. Comment by Mellissa Gyimah: No indent on abstracts as per APA

Table of Contents


Chapter 1: Introduction
2

Background
2

Problem Statement and Significance
4

Theoretical Framework 4

Researcher’s Positionality
8

Purpose
8

Research Question(s)
9

Significance
10

Definition of Terms
11

Summary
11

Chapter 3: Introduction
13

Statement of the Problem
14

Research Question(s)
14

Research Methodology
15

Research Design
15

Study Population & Sample Selection
16

Data Collection Methods
17

Sequential Document Selection 18

Qualitative Interview 18

Data Collection Procedures
18

Data Analysis & Procedures
19

Validity & Reliability
20

Ethical Consideration
21

Limitations
22

Summary
23


Chapter 1: Introduction

Background

Agriculture has been a field that is gradually declining in popularity in several countries around the world. The rate of growth of the global demand for agricultural products has also started to decline in the recent past. This is particularly significant in countries that are referred to as developing and having low economy that were dependent on agriculture (Sivarethinamohan et al., 2020). The number of agricultural lands in developing countries like India have started to decrease. This decrease can be attributed to several factors including an increase in modernization which has changed the way of life of people from doing agriculture as a way of earning their living to other modernized means and the decrease of groundwater levels in several regions which has affected the water needed for irrigating the agricultural farms. Although this decrease in popularity might feel insignificant, it might result in disastrous effects in the long run (Sivarethinamohan et al., 2020). Comment by Mellissa Gyimah: Nice! Comment by Mellissa Gyimah: cite

A decline in agricultural production can significantly impact countries with low economy because it further reduces their economy. An increase in agricultural production helps lower food prices and increases the country’s ability to do commerce based on the agriculture products. Therefore, it is important for these countries to improve their economic condition. In addition to increased modernization and decreasing water levels, most countries also face a decrease in agricultural labor (Sivarethinamohan et al., 2020). This is because most of the youths of the countries do not view agriculture as a viable option for sustenance or growth. Agriculture is also not viewed in a positive light in most of these societies, which also adds to the factor. They are more attracted to other fields that provide them more money and increase their status in the society. Since this mentality is inbred into most of the societies, the reformation of such ideas will take significantly more time (Sivarethinamohan et al., 2020). Comment by Mellissa Gyimah: do you have evidence for this?

Due to these factors, most of the agriculture in emerging and low economy countries are carried out by an older population. This poses several problems for the economy. The lack of a younger agricultural labor population makes agriculture a non-sustainable option for economic growth. As mentioned earlier, the lack of agriculture could cause economic disruptions. There is also the fact that the older population is unable to pass on their knowledge to other generations because of the lack of interest (Sivarethinamohan et al., 2020; Tzachor, 2021). Thus, farmers in these countries are less able to take advantage of other areas that produce food or products. If these issues are not solved, further problems may arise such as social unrest or political instability within the populations. This poses a threat to emerging economies that are dependent on agricultural production (Sivarethinamohan et al., 2020).

Problem Statement and Significance

The main problem behind the decrease in agriculture in emerging and low economy countries is the decrease in the significance and popularity of agriculture. Because of modernization, the younger population in most of the countries do not understand the value of agriculture in their economy. This could be partially attributed to the growth of various industries and their marketing ability (Tzachor, 2021). This has attracted many youths in the countries to ignore farming as a viable option for their economic or social growth. As more and more people gyrate towards modern fields and industries, they have started occupying more land in the countries. This has resulted in the transformation of valuable agricultural lands into factories, companies and residential areas in most of the countries (Tzachor, 2021). Comment by Mellissa Gyimah: This is a big claim..cite to support it Comment by Mellissa Gyimah: Gyrate? Or gravitate

The lack of agricultural knowledge is also a significant factor in developing countries. Knowledge of farming is extremely important for developing countries to manage an agricultural process. Since most emerging and low economy countries need to grow their economy rapidly, they are forced to disregard agriculture as one of the main sources of economy and focus on modern industries and companies that provide opportunities for rapid growth (Tzachor, 2021). To improve agricultural growth, these countries need revolutionary methods that can increase production at lower costs. But this is a challenge as older people contribute to most of the active population of farmers. This has impacted technological and technical advancements in the agricultural field, which is a necessity to mitigate the existing threat to agriculture in most of these countries (Tzachor, 2021). This paper will therefore seek to induldge ins a extensive discussion looking at the use of AI in agricultural sector and consider how the same can be used in looking at how countries can develop their production activities Comment by Mellissa Gyimah: I would use another word here

Theoretical Framework Comment by Mellissa Gyimah: This should be centred

The term “AI” refers to information processing and intelligence. The general idea is that this technology is used to learn and master, and to build applications with that knowledge. In most cases, the information processing and intelligent nature of such a system is what is taught in the different literatures that will be referenced and discussed in this proposed study. The main goal of this proposed study is to explore agriculture in emerging and low economy countries and find ways to induce the use of Artificial Intelligence (AI) (Jha et al., 2019). The theoretical framework for the proposed study will focus on compiling instances of AI usage in global agriculture and explore the possibilities and challenges that are involved in the same, some of the theories include metric embedding, cryptography, computational geometry etc. The proposed study will research the concepts through the exploration of various literary resources that are based on AI Agriculture to develop a comprehensive and comprehensive understanding of the field. Furthermore, the research will look at the practical and social challenges that arise from the use of such technologies, with the aim of encouraging the use of AI technologies in agriculture (Jha et al., 2019). Comment by Mellissa Gyimah: Are these things you can discuss in this actual section?

This study will focus on the development and adoption of AI as a means of agriculture, which is crucial for future economic development and to make large scale agricultural production more efficient in emerging countries and countries with lower economies. The use of Artificial Intelligence system in the field of agriculture is rapidly increasing (Jha et al., 2019). There have been several breakthroughs and advances in AI and some countries have been able to leverage the technology through the development of AI programs and systems. In many of the countries, the economic output as a result of the advances made in agricultural technology has been greatly increasing. In many of the nations where the production has increased, the development of AI has been a critical help in substantially increasing agricultural productivity and production (Jha et al., 2019). This is evidenced in several literary papers. Comment by Mellissa Gyimah: Cite…I would love to hear more about these breakthroughs

The growth of agricultural technology as a field provides great opportunities for emerging and low economy countries that are struggling to improve their agricultural production. Thus, the theoretical framework will focus on exploring the use of technology, particularly AI technology in the global agricultural field. While exploring the opportunities for AI-induced agriculture in emerging countries, it is important to understand the different types of AI technology that are being used in agriculture (Jha et al., 2019). With the aid of literary papers, we can learn that there are several different types of AI systems including machine learning algorithms, deep learning, and computer vision for increasing agricultural productivity and economic growth. A variety of AI systems are being tested and used in today’s agro-industry and, as such, the concept of using AI-enhanced agriculture is a field that has great potential and the use of the field as a solution to poverty alleviation and other environmental problems will be explored further in the future (Jha et al., 2019). Example of AI systems being used in agro-industry include predictive analytics, crop and soil monitoring, agricultural robots, etc. Predictive analytics helps farmers predict weather and crop yield to help them improve their perpetual performance. Agricultural robots have started to replace farmers and they are able to autonomously farm, irrigate and collect crops with the aid of Machine Learning. Farmers in many countries have started to use predictive analysis and precision farming techniques with the help of the aforementioned AI technology. It is important to understand that precision farming has started to increase in popularity, and has held the largest market size in 2019. The use of precision farming and predictive analysis has resulted in high crop yields and lower food costs in several developed countries (Karnawat et al., 2020). The proposed study will focus on using peer-analyzed literary resources to evidence the same and add further proof that supports AI-induced agriculture. While some emerging countries like India, China and Brazil have started to adopt AI agriculture systems, the use of AI technologies in agriculture has still not an integral part in several emerging countries. There are two primary challenges that are responsible for this drawback, namely the lack of ability to automate traditional agricultural processes, and the lack of awareness about AI agriculture. These factors prove to be the main internal factors that have hindered the penetration of AI agriculture in emerging and low economy countries (Karnawat et al., 2020). Comment by Mellissa Gyimah: Yes, but from what lens exactly?

In addition to challenges that threaten the AI agriculture framework, there are also several external factors that hinder the adoption of AI in the agricultural model of some developing countries. It is important to understand that each country has a unique climate and environment, and follow different agricultural frameworks to maximize agricultural production (Karnawat et al., 2020). Therefore, AI systems need to accommodate external factors, and also accommodate local cultures and languages. For example, the monsoons in countries like India and the dry and& hot climate in countries like Africa will prove challenging for the induction of AI agriculture frameworks, therefore these AI cannot be used in every conditions, there is the need to modify them for them to fit the climates and the conditions of the areas in which they will be functioning in. It is for this reason therefore that each emerging country might have the need for different AI applications for specific agricultural needs. Therefore, there is more work and research required to determine the best and most efficient solutions in each specific scenario (Karnawat et al., 2020). Comment by Mellissa Gyimah: Africa is a continent…not a country…..

As AI continues to grow at a rapid pace and become important in agricultural production, it is crucial that the agronomic applications become well supported, well understood, and supported in the AI agriculture framework. Countries with low economy need to implement superior AI agriculture systems that can be implemented as efficient and quick as possible with a focus on supporting local food production and local culture (El-Gayar & Ofori, 2020). The main goal of the theoretical framework is analyzing the theoretical and practical applications of several AI technology that is applicable for increased agricultural production. By using the methodology from the perspective of AI agriculture, the proposed study aims to identify several relevant features that will allow agronomic applications to be implemented using the most advanced technologies available in AI agricultural systems. This will be supported by the global AI agriculture data that is collected through the literary research of several peer-reviewed literary sources (El-Gayar & Ofori, 2020). Comment by Mellissa Gyimah: This person is Ghanaian—from my parents’ country!

Researcher’s Positionality Comment by Mellissa Gyimah: This should be centred

The topic that was used for this proposed study is influenced by my passion for increasing agriculture production in developing countries. The research is to be conducted primarily using document analysis as the main data collection methodology. The research is conducted with the support of Judson University and the research methodologies are based on qualitative research. The main participants of the research are agricultural AI technicians and agricultural farmers from several countries (El-Gayar & Ofori, 2020). The research will not be directly focused on understanding the opinions through interviews, and rather use document analysis and other indirect methods to quantify the use of AI technology in agriculture and determine the efficient technology that could help some of the emerging technology improve their agricultural production (El-Gayar & Ofori, 2020).

Comment by Mellissa Gyimah: centred

Purpose

The purpose of the study is to learn the opportunities for integrating AI technologies to improve the agricultural production of various emerging countries and countries of lower economy (Araújo et al., 2021). The proposed study uses literary research and document analysis to explore the various methods of AI technology used in global agriculture and understanding the challenges in emulating the same. The relationship between AI-based agricultural framework and the various internal and external factors will provide the desired result, which is understanding the appropriate AI technology necessary for the increase in agricultural production (Araújo et al., 2021). Comment by Mellissa Gyimah: may provide….don’t use absolutes otherwise people will think there is no need for the study

Research Question(s
) Comment by Mellissa Gyimah: centre

Global agricultural development is gradually changing and the integration of AI technology in agriculture has helped several countries improve their agricultural production. However, the popularity of agriculture has gradually declined in emerging countries and countries with lower economies (Araújo et al., 2021). The decrease in the production and popularity of agriculture in emerging countries is due to several important factors ranging from increased modernization to decrease in groundwater. The lack of a young agricultural workforce is also another factor that negatively affects agricultural production enhancement and development (Araújo et al., 2021).

Moreover, these countries also face a further decrease in agricultural production due to the gradual loss of agricultural land. Therefore, emerging countries need to revolutionize agricultural frameworks to increase agricultural production and improve their economic standards (Araújo et al., 2021). This can be done through the induction of AI technology in agricultural frameworks as this has been a proven method in several developed countries. This proposed study is focused on the integration of AI technology into agricultural processes in emerging countries. Therefore, it looks to answer some important research questions that would help develop a method of AI integration (Araújo et al., 2021):.

R1: How can AI technology be used to improve the popularity of aAgriculture in eEmerging Countries?

R2: How can AI technology be used to improve aAgricultural production in eEmerging Countries?

R3: What are the challenges and& training necessities involved in the implementation of such AI aAgriculture processes?

Significance

The importance of agricultural revolution has been the topic of several studies, especially in recent times where several countries are facing economic crises. There has also been significant research into the use of AI tools and technology in global agriculture and its positive effects on the same (Tzachor, 2021). However, there is much to be explored on the integration of AI technology into the agricultural processes of emerging countries. Since agriculture is gradually declining in popularity in several emerging countries, this is an important avenue for research. This will help emerging countries revolutionize their agricultural processes and future-proof their agricultural frameworks (Tzachor, 2021).

Using literary documents on AI integration in global agriculture, the reasons for agricultural production decline in emerging countries, and the opportunities and challenges present in integrating different types of AI technology, the proposed study will focus on understanding the best way to create AI-induced agricultural processes in emerging countries. The proposed study will use document analysis as the main data collection methodology and conduct a thematic analysis on the data collected from the research studies (Tzachor, 2021). This thematic analysis will be focused on the use of different types of AI technology and the external factors of several emerging countries like weather, local population, culture, etc. This will help us find the best technology that can be used to improve agricultural production based on an emerging country’s external factors (Tzachor, 2021).

Definition of Terms

i. AI-induced Agriculture – An agricultural framework that is based on the use of Artificial Intelligence. Comment by Mellissa Gyimah: maybe also just define agriculture in general, too?

ii. Machine Learning – Machine Learning is a type of Artificial Intelligence that is based on the idea that systems can learn from data, identify patterns and learn to make decisions with limited human intervention.

iii. Deep Learning – Deep Learning is a category of Machine Learning that uses the human brain as a model for processing data. Through Deep Learning, machines can process complex data without human intervention (Tzachor, 2021).

iv. Computer Vision – Computer Vision is a type of Artificial Intelligence that trains computers to understand and interpret the visual world using digital cameras, videos and other deep learning modules.

v. Precision Agriculture – Precision Agriculture is an agricultural management concept that uses technology to observe, measure and respond to various inter-field and intra-field variables to increase crop yields and agricultural profitability.

vi. Predictive Analysis – Predictive Analysis is a branch of advanced analytics that to analyzes current data using various methods like data mining, statistics, etc., to make future predictions (Tzachor, 2021).

Summary Comment by Mellissa Gyimah: centred

Agriculture has been declining in popularity in emerging countries. In a time when most of the developed countries are using AI to increase agricultural production, there is no clear indication of the same happening in various emerging and low economy countries. Thus, this proposed study was created to understand how agricultural processes in emerging countries can be improved through AI technology. Through literary review and document analysis, the proposed study aims at understanding the best AI technology that needs to be used to improve agricultural production in emerging countries. This is also the main research question that the proposed study aims to answer. The proposed study will also explore the various challenges that will hinder the integration of AI technology in the agricultural processes of emerging countries. Through the proposed study, the researcher aims at increasing the agricultural production and the economy of emerging and low-economy countries. This is the main goal of the thesis. Comment by Mellissa Gyimah: nice summary!


Chapter 3: Methodology

Introduction

The methodology section of the proposed study provides a comprehensive overview of the research methodology that will be to explore the integration of AI agriculture in emerging countries. The research methodology will be firmly based on literary review and document analysis that will focus on analyzing documents that discuss the different types of AI agriculture, the benefits/limitations of AI agriculture, and the challenges in incorporating AI agriculture in emerging countries (Weißhuhn et al., 2018). The goal of the research methodology will be to provide fact-based analyses and supporting qualitative research by using peer-reviewed literature and case studies to demonstrate the benefits and negative impacts of AI agriculture. By using historical literature in this way, the proposed study will aim to present AI agriculture as a credible and affordable alternative to conventional agriculture in emerging countries around the globe. This section will look at the research methodology used in the proposed study. The section will also explore the validity and reliability of the study along with any ethical considerations that need to be addressed (Weißhuhn et al., 2018). Comment by Mellissa Gyimah: For a proposal, your research design and methodology is not the lit review. Unless you were actually writing a metanalysis or a systematic review, which you’re not. So this is solely a document analysis.

Statement of the Problem

Agriculture is a critical field in many countries. However, the popularity of agricultural production is on the decline in several emerging countries. The decline in popularity can be attributed to rapid modernization and lack of education about agriculture. This limits the involvement of the younger generation in agriculture. In addition to the low quantity of active farmers, the lack of technological advancements in the field is also a major factor for the decline in agricultural production (Weißhuhn et al., 2018). With most developed countries focusing on incorporating AI systems in agriculture, the limitations of AI agriculture in emerging countries need to be understood and analyzed.

Comment by Mellissa Gyimah: centred

Research Question(s)

The proposed study will focus on addressing the following critical questions

R1: How can AI technology be used to improve the popularity of Agriculture in Emerging Countries?

R2: How can AI technology be used to improve Agricultural production in Emerging Countries?

R3: What are the challenges & training necessities involved in the implementation of such AI Agriculture processes?

Research Methodology Comment by Mellissa Gyimah: centred

The proposed study will primarily use qualitative research methodologies to study the potential limitations and benefits of AI agriculture in emerging countries. The primary research methodology is a systematic document analysis, i.e. thematic analysis that will be conducted on both historical and current literary sources pertaining to AI agriculture and the current roadblocks in developing countries (Terry et al., 2017). Thematic analysis is a qualitative research methodology that is centered on using identifying relevant themes in literary sources and grouping them for further analysis to identify factual evidences from literary sources. One of the strong points of the methodology is that it can be applied in many areas of research and is thus useful for the field of AI agriculture. Furthermore, it also complements the fact that AI agriculture is a field that is being discussed currently in several literary sources. The thematic analysis will be conducted on literary sources that focus on the field of AI agriculture. The goal of the thematic analysis is to quantify the primary research by providing unique perspectives on the field. This will help enhance the context and achieve a more comprehensive result (Terry et al., 2017). Comment by Mellissa Gyimah: do you have those specific sources already?

Research Design Comment by Mellissa Gyimah: centred

The design of the research methodologies is focused on sequential analysis of both the literary sources and the interviews through thematic analysis. The sequential research framework is based on the core research methodology of document analysis. The framework is focused on logical design that emphasizes efficient data collection. The literary sources for the proposed study will be selected from peer-reviewed research studies and case studies on the topic of AI agriculture (Terry et al., 2017). The thematic analysis will be conducted initially to identify relevant data about AI agriculture’s limitations and challenges. The sequential research design involves the synthesis of factual data from the selected literary sources about AI agriculture and its role in a changing world, using the current tools that AI agriculture provides us today. The design will be then be applied to the interviews with the focus on creating context within the work which assists farmers, business owners and other members in the field of AI agriculture and thereby deepen their understanding of the topic.

The goal of the qualitative research design is to give a broader context and an objective approach to a particular literature in order to determine its relevance in the current context of AI agriculture. The research design uses the thematic analysis of the interviews that were conducted to participants in the field of agriculture. The interview format will be digital interview and the participants will be selected by snowball sampling method. These interviews will help answer questions related to how AI agriculture can benefit emerging nations (Lane et al., 2018). This approach provides a unique view of the field from a social, cultural, environmental, technological, and philosophical perspective. Therefore, the research design is focused on providing a unique picture of the current AI agriculture field. The research framework will have a holistic approach and ensure that the thematic analysis of both the literary sources and the interviews will be integrated and studied in order to provide a comprehensive picture. The primary research will be based on the most up-to-date information in the field of AI agriculture and the qualitative interviews will be used to explore the topic from a …

Increasing AI Agriculture in Emerging Countries and Countries with Low Economy

Submitted by

Sateesh Rongali

A Proposed Presented in Partial Fulfillment

of the Requirements for the Degree

Doctor of Education/Philosophy in Leadership

with a specialization in Computer Science

Judson University

Elgin, Illinois

08-22-2021


Chapter 2: Literature Review

This chapter will explore the field of AI Agriculture and provide insights on the need for further research in the field through an in-depth literature review. The focus of the literature review is to explore the existing literature and highlight the current trends in the development of AI usage for Agriculture and the possible future use in Agriculture. Particularly, the literature review will be a review of articles that focus on the field of AI agriculture. By discussing the potentials, challenges and limitations in the development of AI in Agriculture, the literature review hopes to provide a snapshot of the current state of AI usage in Agriculture. It is evident that agriculture in emerging countries have started to decline because of the diminishing popularity of the agriculture field in the developing nations and its consumers. The literature review will use peer-reviewed literary sources to understand the reason behind the same and the importance of AI agriculture in these developing nations (Beriya, 2020). Comment by Author: its Comment by Author: this chapter Comment by Author: review of relevant literature Comment by Author: it is hoped that this chapter will provide a snapshot…

AI agriculture has become a major topic of interest for scientific research in the last few years. This can be mainly attributed to the fact that the need for AI in the agricultural sector is rapidly increasing because of the growing population and diminishing land of crop plants available for agriculture. In developed countries, AI agriculture provides support to farmers in the farming sector by automating farming practices, which can be applied to the field of agriculture in countries that are suffering from the food crisis and facing environmental problems (Beriya, 2020). Although, the implementation of AI in the agriculture sector is still evolving, the potential of the use of AI in agriculture is promising. By integrating AI into the existing technological system, farmers can use various technologies that include remote-sensing, smart irrigation, and automatic fertilization to provide a high-quality crop. The use of remote-sensing technology to provide an accurate crop yield prediction using information from satellites is a notable example (Beriya, 2020). Although remote sensing technology uses a plethora of information from space to identify a crop, such a system is not yet accessible to developing nations due to the high- cost of satellite-based technology. Comment by Author: cite some of those studies to justify this claim Comment by Author: availability of land for agriculture. Comment by Author: Specify some of those practices Comment by Author: This can equally be applied to …. Comment by Author: Using it Comment by Author: High quality crops

In developed countries, the use of robots and smart technologies in Agriculture has helped boost Agricultural popularity and production. The objective of this research is to explore the potential of artificial intelligence (AI) in Agriculture and the application of AI in Agriculture, in particular, to improve Agricultural popularity and production in emerging countries like India and Africa (Garske et al., 2021). The literature review will be focused on identifying the state of research in AI Agriculture and highlight on potential applications of AI in Agriculture, including robotics in Agriculture. The scope of the literature review includes any research which used robotics and AI in Agricultural development, as the focus of the literature review will be the use of robots and AI in Agricultural development. By exploring existing literature in the field, the literature review will be able to identify the gaps in the knowledge and areas of further research in the field (Garske et al., 2021). Comment by Author: Elaborate on how this has been achieved and cite sources Comment by Author: Africa is not a country

The study will use peer-reviewed literature as the main source for the literature review. Peer-reviewed research papers can be divided into two groups: journal articles and research reports. Journal articles are scientific papers that are published in academic journals and have undergone peer review process. The peer review process ensures the scientific validity of the research paper, such as the research question posed by the researcher. Research reports are scientific reports written by the researcher (Garske et al., 2021). These reports are not peer-reviewed before publication, which allows the researcher to freely write about their research without much scrutiny. This chapter will focus on reviewing journal articles from the peer-reviewed research literature. Journal articles from the peer-reviewed literature will be the main source of the literature review. By focusing on peer-reviewed journal articles, the study will ensure that the literature review is valid and that there are no biases. Comment by Author: You’ve said this earlier. delete Comment by Author: Not necessary. Delete Comment by Author: I really donlt see the relevance of these… Comment by Author: Delete

This chapter will use the literature review for both the knowledge mapping and literature review, which will provide a comprehensive review of the literature in the field of agricultural AI applications. Both types of scientific papers can provide valuable information about how research on a particular topic has been conducted (Singh, 2020). The review process of journal articles and research reports on AI use in Agriculture can be divided into two steps. The first step will involve the selection of a specific topic of interest. Next, the review process will be continued by selecting appropriate bibliographic sources which may include peer-reviewed articles, articles, book chapters, or reports. Lastly, all information found from the selected bibliographic sources will be documented (Singh, 2020). Comment by Author: Do you also notiuce the way the same thing is being repeated? Avoid doing this Comment by Author: I’m struggling to see the relevance of this entire section above.
State very briefly what you want to focus on and promptly go on to the actual review. Trying to educate your reader about the difrences etc may be insulting. They already know.
Please redo this section

Theoretical Foundation

The literary review will also help create a concrete theoretical foundation for the proposed study. Some of the important concepts that needs to be studied in the literature review are the motivation for using AI in agriculture, the barriers for to implementing AI agriculture systems, and the significant benefits of using the same. An understanding of these concepts is necessary to understand how the AI can be used to improve and automate the existing technology in the agriculture sector (Farooq et al., 2020). Therefore, a review of existing literature that covers these topics will help narrow down the list of potential references and improve the strength of the study. While literature reviews are often conducted by analyzing the current literature on a certain topic, AI use in agriculture is a very new area of research and hence has limited exploration. Hence, to conduct a literature review, more information is required, which include the proposed research question, the topic, and the bibliographic sources of the research (Farooq et al., 2020). Comment by Author: delete Comment by Author: delete

It is also important to understand the assumptions associated with the field of AI agriculture and validate the same through the literature review. One of the main assumptions is that the AI will significantly increase the production rate in an agricultural sector and help in increasing its efficiency (Farooq et al., 2020). Hence, a study on how AI is being used to solve problems and automate some processes within the agriculture sector is also required. In the literature review, the use of the AI within the agriculture sector can also be explored by researching the current progress and barriers that prevents the sector from progressing. Another assumption is that the AI will improve the way farmers are operating their farms. The need for an understanding of this issue is that this might lead to new ideas in the field of the operation and management of the farms (Farooq et al., 2020). Comment by Author: Ok, her’s what you need to do here, for all the issues you are highlighting, find the literature that talk about them, read them and state what they say as regards these points.

The literature review will also help verify whether the proposed AI system will help automate the traditional processes of the farming or not. Therefore, the assumption associated with the technology is crucial to be explored. The literature review hopes to identify and define the existing areas of research, gaps, issues, and challenges that are present in the AI agriculture field. This will form the foundation of the research design and help guide the methodology for the research process (Sonaiya, 2019). However, a careful evaluation of the scope of the problem is essential. This will be done through a careful analysis and review of the literary sources that study the existing fields of AI agriculture. This will help create a comprehensive theoretical foundation for the investigation and identification of the problems that are relevant to the selected field of study. To that end, the literature review will investigate several ideas or suggestions regarding the current scope of AI Agriculture, which will later form the basis for the research hypotheses that surround the field (Sonaiya, 2019). Comment by Author: Like I pointed out earlier, so far, I haven’t seen much of the actual review of the literature that you have consulted. Thius chapter should not be prescriptive. It should be an actual review of the relevant literature that talk about the issues you are raising,.

In addition, it will provide a summary of key concepts, definitions, and theories that will be needed for the research study. Following this, the methods of evaluation of AI in the agriculture field will be identified and categorized. Additionally, the current scope of the industry and its development as well as its progress, as well as its progress, will also be reviewed. This will be done through a comprehensive search of the literature using keywords such as: machine learning, deep learning, autonomous farming, and AI (Sonaiya, 2019). The review will also investigate the technological, financial, legal, and operational constraints faced by the farmers as well as challenges that the industry might face. This will help create the design and evaluation framework that will guide the rest of the investigation process.

Review of Literature

This literature review section will review various peer-reviewed literary sources that are relevant to the scope of the research. It will be a chronological review of the literature, beginning with the works that have the greatest effect on the state of the industry or the technology that has been developed. This review will attempt to provide a general overview of the field of study, the related theories, and concepts, and to identify the various technological developments and methods of investigation (Shokat & Großkinsky, 2019). The current state of the AI and its application in the agriculture industry as well as its progress will be reviewed through a bibliography search. The research hypothesis will be derived from the proposed objectives and the conclusions from the literature review. The literature review will be used to define the research question to be answered by the study, along with providing context, definitions, and terminology. It will also be used to define and evaluate the research design and the data analysis method used in the proposed research (Shokat & Großkinsky, 2019). Comment by Author: Where is the actusal review of the relevant literature?

Agriculture in Emerging Countries

Agriculture in emerging countries has decreased in importance due to a variety of factors including the increase in urbanization, the decreasing demand for agricultural products, and the shifting global commodity markets. There is a general belief in the research community that in order to meet the demands of the emerging markets, agricultural research must change and be conducted in an entirely different and new manner (Singh, 2020). Many factors contribute to this lack of change, including the belief that agricultural research is simply too difficult to conduct, the lack of agricultural funding in the emerging countries, the difficulty to recruit and retain researchers, and the lack of a research infrastructure. Although agricultural research conducted in developing countries is often aimed at improving the agricultural production systems in developing countries, the findings from this research often provide solutions that will benefit the agriculture in developing countries. There is a general belief that developing countries such as China and India, with their relatively lower per capita income, lower literacy rates, and smaller agricultural land base, have less fewer resources to pursue agricultural research (Singh, 2020). Comment by Author: cite Comment by Author: cite

In reality, there is some evidence that indicates that the developing countries, particularly China, have a larger agricultural research sector in comparison to the developed countries. Nevertheless, the magnitude of agricultural research activities and research institutions in emerging countries are relatively less than in the developed countries like the USA and the UK. However, there are some literary sources that assure that both developed countries and developing countries have some type of agricultural research that is improving the agriculture of those countries. This is in stark contrast to the major consensus in the field. Therefore, there is a need for further study of agricultural research in developing countries (Singh, 2020). Specifically, there is a need for a greater emphasis on conducting research on how to improve the agriculture in the developing countries, and an examination of the relative success of those agriculture-related research conducted in the developing countries. This will help identify the major problems hindering the agricultural research in the developing countries and provide a clear understanding of why there is such a disparity in agricultural research in developing countries compared with the developed countries (Singh, 2020).

The majority of agricultural research is conducted in the United States, Australia, the United Kingdom, and other developed countries. While there is some research that is being conducted in these countries, the majority of agricultural research is based on research and development initiatives supported by the United States and developed countries. While this research is not necessarily detrimental to the countries in which it is conducted, the research is often focused on improving the agricultural production systems in developed countries (Farooq et al., 2020). This research and development effort could provide new insights, but may not be sufficient to provide sustainable solutions for agricultural production in emerging countries. In addition, the lack of agricultural research and development in developing countries could be attributable to factors such as inadequate funding, a lack of trained agricultural personnel, the lack of sufficient access to data and technology, and the lack of opportunities to partner with other institutions (Farooq et al., 2020). Comment by Author: cite them Comment by Author: cite them discuss them

On the whole, agriculture in emerging countries seem to be on a decline, and this trend is being observed throughout the world. Although agriculture is still the single largest contributor of gross national product, the majority of agriculture-related research is based on research being conducted in the developed countries. The developed countries spend billions of dollars on research, and that cannot be replicated in developing countries that must rely on locally based research that may not provide more sustainable solutions (Farooq et al., 2020). This warrants continued global effort to identify and develop agricultural production systems in developing countries that are sustainable and economically viable. Thus, a study on the need for AI agriculture and its future potential is a relevant and much much-needed topic. It will help emerging countries worldwide to develop more sustainable agricultural production systems that ensure food security for people worldwide (Farooq et al., 2020). Comment by Author: cite sources for these claims

Reasons for Low Popularity

There are also several literary sources that describe the factors which have contributed to the decline in the number of agricultural productivity and popularity. Some of the reasons that have been quoted in relevant literary sources are the increase in modernization and the lack of agricultural education among the younger generation (Alreshidi, 2019). Other factors include poor funding, a lack of research infrastructure, and the lack of agricultural production. In fact, several developing countries have a lower output per unit of agricultural land. While there is a clear consensus among various authors about these factors being the reason for the lack of agricultural production and popularity, the extent of the impact of these factors is not explored in-depth (Alreshidi, 2019). Comment by Author: what are they, be specific, cite them Comment by Author: which ones? Be specicfic

For example, there is a consensus that the decrease in agricultural production and popularity can be attributed to the lack of agricultural education, especially in the case of people from the younger dynamic. While this is true in a way, there isn’t enough literature and research to confirm this with any degree of certainty. In fact, the number of people who are illiterate is increasing while the literacy rate is not commensurate with population growth (Alreshidi, 2019). The education systems in many countries have been affected by a number of challenges. For example, the increase in literacy and the rise of the literacy rate are not in sync with agricultural production. The increasing literacy rate has also not led to a corresponding increase in the number of agricultural producers and entrepreneurs. There is a clear need for further research to determine the extent of the contribution of various agricultural education factors towards declining agricultural production (Alreshidi, 2019). Comment by Author: cite source Comment by Author: is not Comment by Author: cite

Another factor which is attributed for the dwindling population in the agricultural sector is the introduction of a number of modern agricultural technologies. For example, the rise in mechanization and the improvement in agricultural research and development are attributed to the rise in production efficiency (Bannerjee et al., 2018). However, it would be helpful to investigate whether there are is any empirical or research-based evidence to support these claims. While it is true that the agricultural population in emerging countries is likely to decline due to the introduction of modernization, there is also a growing body of literature to indicate that the agricultural population is growing at an unprecedented pace in less developed countries. However, even if the agriculture population in the developing countries is declining, there is a need to understand why people are choosing to stay in the farming sector (Bannerjee et al., 2018). While the agriculture population is declining, there is also a corresponding decrease in the labor force in the agriculture sector. This could be due to the lack of available jobs in the agricultural sector and/or the changing nature of employment which makes the work force less attractive. More research needs to be done on these issues to determine the key reasons for declining agricultural production.

The question is whether, and to what extent, these factors have also contributed to the lack of agricultural production or research in emerging countries (Bannerjee et al., 2018). This question is based on the assumptions that the developing countries have the potential to increase their agricultural production if the factors that prevent such production, and the agricultural research in emerging countries, could be corrected. The question is also based on the assumption that the agriculture research in emerging countries could be conducted in the same manner as in developed countries. Therefore, it is important to examine the current factors that contribute to the current status of agricultural research in emerging countries (Bannerjee et al., 2018).

In many cases, the emerging countries research that is being conducted is mostly based on developed countries. The reasons behind this are not only the lack of technical, infrastructural, and monetary capacity, but also due to the difference in the environment, culture, and mentality of the countries. In order to understand the reasons that underlie the current status of agricultural research in developing countries, it is necessary to first explore and understand the current status of agricultural research and the potential of agricultural research in developing countries (Bannerjee et al., 2018). Comment by Author: repetition. delete

Importance AI Agriculture

Literary sources that explore AI agriculture offer a strong argument to support the research of AI integration in the agriculture field. AI agriculture has gained a lot of interest due to its wide range of potential applications in various fields in the agriculture industry (Eli-Chukwu, 2019). AI agriculture is also gaining popularity as the technology is becoming more advanced, cheaper, and easier to use. In fact, many authors and researchers in the field propose that the impact of the artificial intelligence on the food and agriculture industry is expected to be tremendous. They state that the technology has the potential to change the way farming is done and how food is harvested, and that it can be an advantage to farmers if they can harvest early, as the weather could be favorable for a certain crop and then bad for another crop. This showcases the importance of AI research and development in the agriculture industry and the importance of using AI as a tool to solve problems (Eli-Chukwu, 2019). Comment by Author: cite

There is also a strong consensus among researchers that AI agriculture is highly beneficial for both farmers and societies. One of the biggest uses of AI technology is to improve the efficiency of farming. It is possible that farmers could harvest a crop, store it, and harvest another crop using an AI technology system. These systems could potentially allow farmers to harvest in the middle of the night when the weather is not conducive to doing so. The importance of improving efficiency cannot be emphasized enough (Eli-Chukwu, 2019). A study by the Department of Agriculture (USDA) estimated that in an average farm, a farmer can save around 9 cents when using an improved AI agricultural technology. As the efficiency of farming improves, the cost of food production also decreases. This can provide a sustainable food source and reduce the amount of money required by the farmer to obtain a food source. However, AI technology is still in its infancy, and there are more research, development, and testing needs to be done before more people can use AI technology to improve their food and agriculture (Garske et al., 2021). Comment by Author: which ones? cite Comment by Author: delete

Another use of AI technology in agriculture is ‘predictive farming’ that helps determine harvest times and crop use. This can be especially important if there is an environmental concern because a farmer might not want to harvest when the environment is becoming unfriendly. This could provide an opportunity for more efficient and more cost effective farming. This is especially important for the use of crops and the usage of water because the use of the most efficient crops could require less water (Garske et al., 2021). The efficiency of a farm could also be defined by its net income. This would mean that the higher the efficiency of the farm is, the more the net income will be. Since the efficiency of the farm is directly linked to the net income, the farm is more efficient if it is able to attain a greater net income. With predictive farming, a farmer may learn what the soil is able to tolerate and when to plant. It can also determine what the crops are best for its environment at what time of the year (Garske et al., 2021). Therefore, AI technology helps farmers use these tests to optimize the quantity and quality of crops. Comment by Author: delete

An overwhelming amount of literature recommend integrating AI tools and systems like machine learning, IoT and data visualization in order to monitor and control farms as a whole. AI can work with the information gathered in the field to determine what crops need to be grown based on weather, soil, and other environmental factors (Gurumurthy & Bharthur, 2019). Because of the huge amount of data that can be gathered from this information, it is hard to process it by human. AI can process the data, determine what crops need to be grown, and then send instructions to grow the best crops. AI can also use IoT devices and sensors to determine how much of a fertilizer, or other chemicals need to be used to improve the overall health of the soil. This can help the farmer plan for the best possible results (Gurumurthy & Bharthur, 2019). Comment by Author: cite them

However, there are also concerns in the mind of researchers and experts in the field about the implementation of said AI systems in the field of agriculture. This is because of the fact that a lot of the AI technology and systems that are used today in the agricultural sector have not been extensively tested in emerging However, there are also concerns in the mind of researchers and experts in the field about the implementation of said AI systems in the field of agriculture (Gurumurthy & Bharthur, 2019). This is because of the fact that a lot of the AI technology and systems that are used today in the agricultural sector have not been extensively tested in emerging economies, like India. While these emerging economies have a lot to gain by utilizing AI systems in their agricultural sector. It is important to do further research and testing of the said AI systems in order to make sure that they are effective and that there are no side effects to the ecosystem. Therefore, several experts and researchers state that the implementation of AI technology in the agricultural sector of emerging countries needs to be done slowly and carefully (Gurumurthy & Bharthur, 2019).

Exploration of Benefits

There is an overwhelming consensus that there are several benefits in implementing AI agriculture systems in the field of agriculture. The economic benefits of using AI systems in the agricultural sector in emerging economies is pretty significant. Many countries in the world, including the USA, are struggling with many issues like unemployment, poverty, increasing food prices, and increasing costs of agriculture as a result of climate change. While other countries like India have a much higher population and need more food (Bannerjee et al., 2018). Therefore, in these emerging economies, the implementation of AI systems in the agricultural sector will significantly help in the improvement of the economy, not to mention the improvement of the overall welfare of the people and agriculture as a whole. The implementation of AI technology in agriculture is going to be the most effective way of overcoming the food shortage and increasing food security issues that are plaguing the world (Bannerjee et al., 2018). This is because a lot of the world’s population is living in rural areas. As a result, the majority of the population do not have access to food that is sufficient and healthy. Therefore, using AI systems in agriculture to help farmers in rural areas produce food in a sustainable way is going to be the best way to solve the growing issues of food shortages and food security in the world (Bannerjee et al., 2018). Comment by Author: You need to site them and also justify what you mean by ‘overwhelming.’

Another significant benefit of using AI systems in the agricultural sector is going to be the increased income of the farmers. Because there is a growing need for food in emerging economies, and a lot of the people in these countries are living in rural areas (Beriya, 2020). Therefore, it is pretty obvious that there is a huge need for increasing the productivity of the farmers in these countries. The implementation of AI technology in agriculture, and especially in the agricultural software, can significantly help these farmers to increase their income as well. Because a lot of the farmers that are in rural areas don’t know about any of the agricultural software applications that are available on the market (Beriya, 2020). Therefore, they are going to find it really difficult to benefit from these applications. This can be mitigated with the aid of AI education for …

Dissertation
by Sateesh Rongali

Submission date: 04-Jan-2022 02:54PM (UTC-0700)
Submission ID: 1737544605
File name: 7316_Sateesh_Rongali_Dissertation_399527_51021102.docx (54.17K)
Word count: 14652
Character count: 80464

1

1

11

25

4

17

2

5

12

27

28

6

13

14

14

15

8

24

9

6

11

9

22 23

19

26

3

3

3

10

16

21

18

4

7

10

4

20

7

8

1

3%
SIMILARITY INDEX

2%
INTERNET SOURCES

1%
PUBLICATIONS

1%
STUDENT PAPERS

1 <1%
2 <1%
3 <1%

4 <1%
5 <1%
6 <1%
7 <1%
8 <1%

Dissertation
ORIGINALITY REPORT

PRIMARY SOURCES

Submitted to Apollo Group, Inc.
Student Paper

repository.stcloudstate.edu
Internet Source

“Smart Agriculture Automation Using
Advanced Technologies”, Springer Science
and Business Media LLC, 2021
Publication

link.springer.com
Internet Source

Submitted to University of Central Lancashire
Student Paper

scholarworks.waldenu.edu
Internet Source

vuir.vu.edu.au
Internet Source

“Sentimental Analysis and Deep Learning”,
Springer Science and Business Media LLC,
2022
Publication

9 <1%
10 <1%
11 <1%
12 <1%

13 <1%
14 <1%
15 <1%
16 <1%
17 <1%
18 <1%

admin.umt.edu.pk
Internet Source

dokumen.pub
Internet Source

scholarworks.gsu.edu
Internet Source

David Velásquez, Alejandro Sánchez,
Sebastián Sarmiento, Camilo Velásquez et al.
“A Cyber-Physical Data Collection System
Integrating Remote Sensing and Wireless
Sensor Networks for Coffee Leaf Rust
Diagnosis”, Sensors, 2021
Publication

journals-crea.4science.it
Internet Source

www.ncbi.nlm.nih.gov
Internet Source

Submitted to University of Bolton
Student Paper

journal.suit.edu.pk
Internet Source

peerj.com
Internet Source

digitalcommons.pepperdine.edu
Internet Source

19 <1%
20 <1%

21 <1%

22 <1%

23 <1%

sersc.org
Internet Source

Amit Sood, Rajendra Kumar Sharma, Amit
Kumar Bhardwaj. “Artificial intelligence
research in agriculture: a review”, Online
Information Review, 2021
Publication

Elsayed Said Mohamed, AA. Belal, Sameh
Kotb Abd-Elmabod, Mohammed A El-
Shirbeny, A. Gad, Mohamed B Zahran. “Smart
farming for improving agricultural
management”, The Egyptian Journal of
Remote Sensing and Space Science, 2021
Publication

Ho Soo Kim, Sang-Soo Kwak. “Crop
biotechnology for sustainable agriculture in
the face of climate crisis”, Plant Biotechnology
Reports, 2020
Publication

Kirsten vom Brocke, Clarisse Pulcherie
Kondombo, Marion Guillet, Roger Kaboré,
Adama Sidibé, Ludovic Temple, Gilles
Trouche. “Impact of participatory sorghum
breeding in Burkina Faso”, Agricultural
Systems, 2020
Publication

24 <1%

25 <1%
26 <1%
27 <1%

28 <1%

Exclude quotes On

Exclude bibliography On

Exclude matches < 5 words

Michael Carolan. “Filtering perceptions of
climate change and biotechnology: values and
views among Colorado farmers and
ranchers”, Climatic Change, 2019
Publication

repository.tudelft.nl
Internet Source

www.mdpi.com
Internet Source

“Internet of Things and Analytics for
Agriculture, Volume 2”, Springer Science and
Business Media LLC, 2020
Publication

WILLIG. “EBOOK: Introducing Qualitative
Research in Psychology 4e”, EBOOK:
Introducing Qualitative Research in
Psychology 4e, 2021
Publication

FINAL GRADE

/0

Dissertation
GRADEMARK REPORT

GENERAL COMMENTS

Instructor

PAGE 1

PAGE 2

PAGE 3

PAGE 4

PAGE 5

PAGE 6

PAGE 7

PAGE 8

PAGE 9

PAGE 10

PAGE 11

PAGE 12

PAGE 13

PAGE 14

PAGE 15

PAGE 16

PAGE 17

PAGE 18

PAGE 19

PAGE 20

PAGE 21

PAGE 22

PAGE 23

PAGE 24

PAGE 25

PAGE 26

PAGE 27

PAGE 28

PAGE 29

PAGE 30

PAGE 31

PAGE 32

PAGE 33

PAGE 34

PAGE 35

PAGE 36

PAGE 37

PAGE 38

PAGE 39

PAGE 40

PAGE 41

PAGE 42

PAGE 43

PAGE 44

PAGE 45

PAGE 46

PAGE 47

PAGE 48

Comment 1

I’m a bit thrown off by your methodology section. For starters, you said you will be using the
document/thematic analysis to gather your data, which is do-able if your committee agrees
that it’s ok, but later, you started to refer to participants, interviews, etc. and said that there
are 2 major approaches – thematic analysis and interviews. 

All this is still ok, but if you are using any humans, you have to do IRB

 and to do IRB, you have to show very specific details as to who, how, when, what, etc. of the
people, the instrument/questionnaires, etc that you will use…all these have to be submitted
to IRB for approval.

PAGE 49

PAGE 50

PAGE 51



Insight Consulting, LLC

October Deliverable:

Summary of Student Revisions and Recommendations

Version 1.0

October 29, 2021

Presented by:

Dr. Brittny James

October Deliverable

Summary of Student Revisions and Recommendations

General Comments:

Of the five student papers reviewed, all clearly need more detailed revisions, including returning to the literature and properly citing claims made throughout the document, more thought into conceptualizing their ideas, developing attainable research questions for a dissertation study, and developing a detailed methodology section with clear steps for how the dissertation will be completed (i.e., data collection, sample description where applicable, data analysis tools). The table below will detail the summation of comments from Dr. Gyimah-Concepcion with the explanation of such needs to the program of study, and recommendations for how to address each revision need should you choose to continue with the written dissertation as the measure of success for students to graduate. Comments below were consistent for all students. Given these recommendations would require intensive time and human capacity, additional conversation can ensure on how to move forward without impacting the graduation of these students and ensuring a meaningful matriculation through the Doctor of Education in Computer Science program with a practice-based format instead of the traditional written dissertation.

Table 1. Explanation of revisions needed for the DSC program student submissions of Chapters 1-3

Revisions Needed

Explanation

Recommendations

1. Conceptualization of study design needed

There is a general lack of clarity about the concept of the study, meaning no students were able to articulate a clear purpose and rationale for their proposed studies, nor is it evident the type of study that is proposed (e.g., phenomenology, case study, observation, etc.). Once research methods are clarified, then there can be a clear explanation of data collection procedures and measures, as appropriate. This is also either unclear or not described in the documents.

This would take detailed discussions with students to understand their goals for each study and assistance articulating these studies’ purposes. Essentially, either the students’ committee members or an outside research assistant should be employed to carry out this task. It is evident that the students do not understand the research process; therefore, a project-based format is recommended such that they can still employ their obvious technical expertise to a predetermined problem.

2. Missing citations

APA 7 needs to be employed for in-text citations. This will take time and resources for students to return to the literature, find the claims made, and correctly cite them. It is not clear based on these submissions whether students are committing plagiarism purposely or if there is a lack of understanding about how and when to appropriately cite information. Citations are inconsistent between and within students’ documents. This comment is also applicable to the document formatting in general, and references.

If students can relocate studies or links to information in these documents, the recommendation is to hire an APA 7 expert to edit these documents; however, this would cost approximately 60 hours of work, and students would then be disconnected from the process. To fully engage students, a guided writing process would be best, but if this is not an intention for future cohorts, this recommendation is a waste of time and resources.

3. Proposal language not used (should be in future tense)

Language in these documents is written as if the studies have already been either approved or completed. Given these are proposals, proper tense should be future (will or would). Current language might mean that language has been improperly obtained from another study or document (refer to revision #2).

Revise documents to reflect future tense and ensure that the proposal is original.

4. Content organization and clarity needed

Content is arbitrarily placed and often the wrong chapters/sections. This is also a function of the students’ lack of understanding of APA 7 format (refer to revision #2).

Given previous recommendations, once a clear understanding of the proposals is developed, these sections can be created in APA format, with templates/guidance for what should be included in each section (see recommendations #1 and #2).

5. Unclear/inconsistent definitions of terms

Definitions of terms are included in Chapter 1, but throughout the documents, either different terms are used or other definitions are applied.

Revision of documents to include consistent language throughout.

6. Lack of explanation of appropriate theoretical frameworks

It is unclear if theoretical frameworks were advised to be part of these proposals, as explanations of theory are largely absent from these documents.

If theory is not a requirement, it should be reconsidered and included. This very necessary revision might require a refresher course on the available popular theories used in computer science literature.

7. Lacking understanding of ethical considerations

These sections are either missing or incorrectly written.

Given the lack of clarity about research and data collection, additional problems will likely arise during the data collection and analysis phases that could create ethical dilemmas for the program. To avoid, students could be tasked with revising the current documents to fit a literature review format to identify gaps in the available literature. Upon identification of research and practical needs, students

1

error: Content is protected !!