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Essay paper on death and civil war women’s rights history essay help: women’s rights history essay help

Like any other war, the American civil war impacted society negatively as it brought about pain and suffering. However, civil war is associated with some positive changes in American society, some of which Americans enjoy or experience today. American civil war impacted the citizens in numerous. It led to mass displacement and forced migration, a common residue of war. The effects of the war were felt at both community and individual levels. The outbreak of civil war resulted in several people fleeing the ancestral land in fear of losing property, loved ones, and their lives. Apart from running their homes, women assume the responsibility of feeding and caring for families without their husbands. This is evident in the video, where communication was through letters since the war was separated loved. Further, people had also dealt with severe issues such as sicknesses, inflation, and lack of essential supplies.
Surprisingly, the American civil war triggered some positive changes, which stood out for me in the video. The war affirmed the United States’ single political entity, freed millions of American citizens from the bondage of slavery, and established the most powerful centralized federal government. Also, the civil war propelled the United States to become a world power in the twentieth century by laying the foundation for this endeavor. Further, today’s ambulances and hospitals can be attributed to the civil war. It revolutionized the medical sector; the war led to positive reforms in medicine. The first ambulance was introduced to transport injured soldiers to hospitals on the battlegrounds during the war.


Benefits and Costs of Gamification in Mental Health do my history homework

Gamification in Mental Healthcare

There is no doubt that we have had decades of research geared at developing new and more effective treatments for mental conditions ranging from autism to anxiety, from schizophrenia to depression and so on. What is rather worrying, however, is that we have very little to show for it. Mental disorders such as these continue to impact on the quality of life of a significant proportion of the population, costing the taxpayer millions of dollars every year. Currently, approximately 90 million Americans, which translates to approximately one-third of the population, suffers from some form of anxiety disorder, yet a majority of these fail to seek out treatment for the same owing to the stigma, burden and cost associated with such evidence-based treatments. Mental health professionals are, thus, focusing their attention towards the development of low-burden, effective interventions for mental illness. Gamification, the introduction of game-like elements in mental health interventions, is one of the newest trends in that direction, and one that experts regard as having significant potential. This text examines the various ways through which gamification has been used to impact mental health outcomes among members of the Millennial Generation, who are also the greatest users of smartphones and other mobile devices. It is intent on showing that if properly-regulated and controlled, gamification could contribute significantly to improved mental health outcomes.

Overview of Gamification in Mental Healthcare

Gamification in mental health basically refers to the strategy of translating or embedding interventions into game formats that could range from animated graphics, to software apps and game-like interfaces accessible through mobile devices. A report by the Pew Research Center estimates that approximately 61% of young persons between the ages of 12 and 30 own a smartphone or some form of mobile device (Chan, Torous, Hinton & Yellowlees, 2014). Of these, 31% use their devices to obtain health information from either online platforms or psychiatric patient networks (Chan et al., 2014). Today, there are numerous apps at the disposal of both patients and healthcare practitioners, and which help in among other key procedures patient record-keeping, decision support systems, patient monitoring and surveillance, health promotion, community mobilization, appointment reminders, and treatment adherence and monitoring. The overriding aim of gamyifing mental health treatments is to increase patient engagement and reduce the stigma associated with treatment appeal. Its use, however, remains limited owing to challenges of accessibility and patient privacy.

Examples of Mental Health Information Obtained through Gamification

As mentioned earlier on in this text, numerous software apps have been developed to improve the mental health of the population. With the help of mobile apps and wearable devices, one can track just about every aspect of their mental health just as much as they can track their physical health. The Recover Record App, for instance, is designed as to send reminders and notifications to patients to enable them cope with their psychological disorders. For people with eating disorders, for instance, the app sends a notification at 5 a.m. everyday reminding them of the need to take breakfast (Arthur, 2015). After eating, the patient then records on their phone what they ate, and how they felt. The procedure is repeated throughout the day, and the app acts like some form of online diary, reminding them in intervals to log her supper or eat a snack (Arthur, 2015). By recording their dietary habits and tendencies, patients are helped to cope effectively with their disorders. Besides Recovery Record, there are numerous other apps used to address a range of mental health issues; some for dealing with anxiety through breathing or meditation techniques, others for tracking mood swings and others specific to bipolar disorders, depression, phobias, and so on. Some of the most popular apps and their specific functions have been discussed in the subsections that follow.

StudentLife: the StudentLife Android App, developed by researchers at Dartmouth College, collects location, audio, and motion data from the sensors of a user’s smartphone, draws patterns from the same and uses these to predict and alert users of changes in their mental health (Bolluyt, 2014). Algorithms within the app process the data collected to obtain a clear view of their sleeping patterns communication patterns, the places they visit, their level of physical activity and so on; so if the user begins to show changes in any of these patterns, the app takes these to correlate with changes in stress, loneliness, and depression and notifies the users or their registered caregivers that there could be changes in their mental health (Bolluyt, 2014).

SelfEcho: in addition to apps geared at helping users cope with their disorders, other apps and software have been developed to assist practitioners with information about their patients. One such software is SelfEcho, which allows mental health practitioners to enroll their patients to use smartphone sensors and self-reports to record data pertaining to their daily lives. The software provides mechanisms for the practitioner to assess their patients’ progress and determine which aspects of the treatment plan are working, and which ones are not (Bolluyt, 2014). The metrics tracked by the software include base models for restfulness, worry, anxiety, guilt, physical activity, pleasure, hopefulness, positivity, and so on (Bolluyt, 2014). Practitioners can use this information to not only track progress, but also identify triggers and make better diagnoses.

My M3: this is one of the few apps that can be used by both healthcare providers and consumers. It provides a simple test that can be used to detect posttraumatic stress disorder, bipolar disorder, anxiety, and depression. Users take the test to help not only their practitioners, but also themselves understand whether or not they are suffering or are likely to suffer from a mood disorder (Bolluyt, 2014). This puts practitioners in a better position to make accurate diagnoses and to consequently administer effective treatments.

MindShift: this is an Android app meant to help teenagers and young persons deal with anxiety. It focuses on getting them to change the perceptions they hold about anxiety and to consequently be more willing to face it (Simon Fraser University, n.d.). It provides tips on how to devise helpful ways of thinking, how to relax, strategies for coping with everyday anxiety and so on, all of which help them control their anxiety (Simon Fraser University, n.d.).

SPARX: this is an interactive fantasy game meant to help adolescents deal with depression and anxiety (Sarasohn-Khan, 2012). Users are required to pick an avatar and then take part in a range of challenges to bring about balance in a GNAT (Gloomy Negative Automatic Thoughts)-dominated world (Sarasohn-Khan, 2012). The game is available on CD-ROM and users play by installing the same in their devices. Research has shown SPARX to be effective in reducing levels of depression and anxiety among adolescent users (Sarasohn-Khan, 2012).

Other popular apps and mental health software include the Therapy Outcome Management System, which provides feedback to practitioners on the outcomes of therapy and counseling; the Sleep Well Be Well App; the Headspace, the Thought Diary Pro, and the My Mood Tracker. All of them, however, work almost in the same way — tracking users’ emotional well-being in correlation with their behavioral patterns (Bolluyt, 2014).

The Impact of Gamification in Mental Health Interventions

The Advantages / Potential Benefits of Gamification

The benefits of gamification in mental health settings can be discussed from the perspective of the patient as well as that of the health practitioner.

Benefits to Patients: Mental health professionals, like any other medical practitioners, have an ethical duty to ensure that any interventions or treatment plans they use on their patients are supported by evidence (Goodman, 2003). Research has shown games to be an effective way to engage patients and enable them cope effectively with psychological disorders such as posttraumatic stress disorder, obsessive compulsive disorder, social phobia, depression, and anxiety (Cugelman, 2013). In her study seeking to assess the effectiveness of SPARX in reducing the level of depression and anxiety in teenagers and adolescents, for instance, Sarasohn-Khan (2012) exposed 117 students to the program for a period of three weeks and found 63% of these to have significantly lower levels of the same upon completion. Elsewhere in Finland, Lappalainen and his colleagues conducted a study to assess the effectiveness of the P4Well App and found the same to have a considerable effect on certain aspects of burnout and job strain, including over-commitment and cynicism (Lappalainen et al., 2014).

There are a number of possible explanations for the effective working of games in mental health treatments. To begin with, gamification eliminates the need to make a trip and communicate face-to-face with the mental health provider (Dennis and O’Toole, 2014). Patients can have their practitioner make diagnoses from the postings they make online, and this essentially helps them reduce the stigma associated with seeking out mental care and having to explain one’s problems to a practitioner in a face-to-face communication setting. This helps to maintain the relationship between practitioners and their patient, and reduces the cost of obtaining help, making mental healthcare more accessible to a greater number of people. Secondly, unlike the traditional methods of administration of care, gamification allows patients to self-monitor themselves and ensure that they remain on the right track in relation to their treatment or prevention plan (Lister et al., 2014). Members of the target group spend the highest number of hours with their smartphones and mobile devices compared to members of any other age group (East and Havard, 2015). This makes self-monitoring relatively easy. From a patient’s perspective, therefore, gamification enhances psychological services and makes mental care more conveniently accessible to a greater number of people. Thus, generally gamification in the mental health setting improves the mental health status and overall well-being of the population.

Benefits to the Practitioner: the main advantage of gamification to the mental health practitioner is physical workload-reduction. It is estimated that one in every four young people experience some form of depressive disorder before they are 20 (Sarasohn-Khan, 2012). Moreover, approximately 15% of adolescents suffer from depression; however, there is not enough practitioners and counseling resources to address this concern (Sarasohn-Khan, 2012). There is an undersupply of medical resources to address the psychological concerns of the target population, and over 70% of its members end up not receiving appropriate treatment (Sarasohn-Khan, 2012). Gamification basically compensates for this shortage of counseling resources and prevents practitioners from being overworked. This places them in a better position to offer personalized care and come up with more accurate diagnoses and more effective treatment plans.

Disadvantages of Gamification

In as much as gamification enhances psychological services and improves the quality of mental health in the population, it is not without its share of disadvantages. It is these disadvantages that have limited user engagement and made it rather difficult for mobile apps and software programs to diffuse effectively among users and prospective users (East and Havard, 2015). The core ones include:

Difficulty in Securing User Information: mental health gaming programs collect lots of personal information, including names, contact details, health statuses, familial background and so on from users; and with their information-sharing functionality, privacy concerns become almost unavoidable (East and Havard, 2015). Some programs try to increase the security of their users’ records by incorporating an anonymous-sharing feature, which allows users to share information with other users anonymously (Sarasohn-Khan, 2010). Others, however, do not have this feature, making it possible for users to share personal health information amongst themselves invariably, and this places them at high risk of falling prey to unscrupulous persons.

The Requirement of Technological Literacy: technological literacy can be defined simply as the intellectual dispositions, abilities, and processes needed for a practitioner “to understand the link among technology, themselves, their clients, and a diverse society so that they may extend human abilities to satisfy” the health needs of their patients (Tyler and Sabella, 2004, p. 5). Whereas the target population of young people aged between 12 and 30 may have high levels of technological literacy, a majority of the practicing and most experienced mental care providers may not be equipped with the same level of understanding when it comes to technology. For this reason, most of these experienced care providers may be unable to take advantage of gaming platforms and hence, unable to reap the potential benefits of the same.

Accessibility of Mobile Devices: mental health games and software programs are meant for use with smartphones and other mobile devices with internet-enabled functionalities. These devices are, however, quite costly and beyond the reach of most prospective users, particularly in the rural areas. In this regard, therefore, the use of games cannot be relied upon as a substitute for face-to-face therapy and treatment sessions because it causes disparities in the administration of, and access to mental care between the rich and the poor.

Solutions for Increasing Engagement and Enhancing Diffusion

The most viable way to increase user engagement in mobile gaming programs is by developing solutions to address the disadvantages that hinder its diffusion. East and Havard (2015) propose a three-part model that could be used to achieve this. To begin with, they propose that app and gaming software developers ensure that their apps and software are HIPAA-compliant. They can do this by putting in place effective security measures such as passwords and encryptions to govern information-sharing among users and ensure that information-sharing among users is regulated. This would obviously not eliminate the privacy concerns associated with the use of games in mental health interventions, but it would give app developers and administrators greater control over any information provided by users. Users will most certainly be more willing to engage with mental gaming platforms if the security of the information they provide is guaranteed.

In addition to making mobile gaming platforms HIPAA-compliant, relevant stakeholders nee to also take steps to increase the levels of counselor awareness on the potential benefits of gamification in mental health settings. This they could do by organizing professional association conferences focused on increasing their knowledge on how to use such platforms to enhance the health outcomes of their patients, and the potential benefits that could accrue from such use. Steps should also be taken to make such programs available on a wider variety of platforms such as low-cost mobile phones. For instance, app developers could create messaging services to be used by all mobile phone users and not only those with open access to the internet. This way, gaming services would become accessible to a wider range of users, particularly in the rural areas.


Gamification is a rather new aspect in the medical industry, particularly in mental health settings. Its effectiveness in helping patients and practitioners realize better mental health outcomes has, however, been proven by multiple researchers. Its effectiveness stems from the fact that it allows for the administration of timely diagnoses and allows for self-monitoring. Despite its inherent benefits to the health system, however, gamification is not without its share of disadvantages. Its main disadvantage is that it does not guarantee the privacy and security of user information as well as traditional metrics do. Moreover, not all practitioners are technologically literate, and mobile devices may also not be accessible by all prospective users, particularly those in the rural areas. These disadvantages have made it difficult for mental gaming programs to diffuse effectively among users. To increase user-engagement with the same, therefore, stakeholders will need to take relevant steps to make such platforms HIPAA-compliant, and to increase the awareness of mental care providers.


Arthur, G., 2015. Cellphone Therapy: New Apps Help Track and Treat Mental Illness. [online] Available at [accessed 22 May 2015]

Bolluyt, V., 2013. How Apps are Tackling Important Mental Health Issues. Cheatsheet. [online] Available at [accessed 21 May 2015].

Chan, S.R., Torous, J., Hinton, L., and Yellowlees, P., 2014. Mobile Tele-Mental Health: Increasing Applications and a Move to Hybrid Models of Care. Healthcare, 2(1), pp. 220-233

Cugelman, B., 2013. Gamification: What it is and why it Matters to Digital Health Behavior Change Developers. JMIR Serious Games, 1(1), pp. 1-6.

Dennis, T.A., and O’Toole, L.J., 2014. Mental Health on the Go: Effects of a Gamified Attention-Bias Modification Mobile Application in Trait-Anxious Adults. Clinical Psychology Science, 2(2), pp. 1-15

East, M.L., and Havard, B.C., 2015. Mental Health Mobile App: From Infusion to Diffusion in the Mental Health Social System. JMIR Mental Health, 2(1), pp. 1-14.

Giota, K, G., and Kleftaras, G., 2014. Mental Health Apps: Innovations, Risks and Ethical Considerations. eHealth Telecommunication Systems and Networks, 3(1), pp. 19-23.

Goodman, K.W., 2003. Ethics and Evidence-Based Medicine: Fallibility and Responsibility in Clinical Science. New York, NY: Cambridge University Press

Lister, C., West, J., Cannon, B., Sax, T., and Brodegard, D., 2014. Just a Fad: Gamification in Health and Fitness Apps. JMIR Serious Games, 2(2), pp. 1-12.

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Torous, J., Friedman, R., and Keshavan, M., 2014. Smartphone Ownership and Interest in Mobile Applications to Monitor Symptoms of Mental Health Conditions. JMIR mHealth and uHealth, 2(1), pp. 1-8

Tyler, J.M., and Sabella R.A., 2004. Using Technology to Improve Counseling Practice: A Primer for the 21st Century. Alexandria, VA: American Counseling Association

What we use to recognize people essay history homework: history homework


The concepts and use of the Fusiform Face Area (FFA) in terms of facial recognition and the Superior Temporal Sulcus (STS) in terms of voice recognition are not new on their own. However, those individual technologies and concepts have evolved on their own and now they are being analysed in terms of how they are perhaps used concurrently when one person does (or tries) to recognize another person. This report will cover what the FFA and STS are in general, prior ideas, frameworks and outcomes that have informed and influenced current research and what the future holds, at least based on current trends for the use of FFA and STS in combination or on their own.

FFA & STS Combined

Subject of Discussion

There is a great amount of debate with the circles that exist in the neuro-psychological field regarding the direct integration, or lack thereof, of the brain regions known as the fusiform facial area (FFA) and the superior temporal sulcus (STS) as they are used to identify a person using speech and/or facial characteristics.

What the FFA & STS Are

The use of a combination of STS and FFA is believed to have first come about through the work of Bruce and Young in 1986. The posed a hypothesis that there was a classical model that could be used to identify a person that would be based on hierarchal and/or distinctive pathy manners for facial and speech perception. This perception could then result in correct identification results (Haxby, Hoffman & Gobbini, 2000).

When it comes to superior temporal sulcus (STS), there is either a top to down approach or vice versa. The former approach to perpetual recognition of a sensory processing stage is established in the higher-level mechanics of STS-driven actions that are constructed by audio-visual cognition like in the alternative models that are sometimes used and mentioned. By contrast, a bottom to top approach begins with perception in different lower-level to rigid higher-level pathways in order to distinguish auditory-only data cognition that is seen only with the more conventional models that exist.

Past Ideas

The prior-mentioned model as crafted and formulated by Bruce and Young in 1986 was fairly shoddy in terms of its accuracy and applications. Indeed, it was overall a poor model as it lacked the explanations for other dissociative impairments relating to existing physical deficit and/or medical injury. There were also concern about the definitive brain regions involved that may or may not be involved. The main concern was that there was not an observable and definitive way to prove ideas and hypotheses one way or another. This does mean that the idea of Bruce and Young were wholly wrong and off-base. It just meant that they could not be proven definitively one or another and thus lacked validity (Haxby, Hoffman & Gobbini, 2000).

Recent & Future Directions

More recent and prospective future directions came to light as the 1900’s came to a close in the 1990’s. The prior-mentioned problem of not being able to prove ideas related to FTA and STS were eased greatly by rapid advances in technology and the prior-mentioned Haxby was on the forefront of those advances as a result. Haxby, of course, was able to pick up where things left off and Haxby was obviously involved with the prior research so the learning curve was not as steep. Haxby was able to take the model posulated by Bruce and Young and then use neuroimaging techniques to investigate any given function. Looking at the two core structures (the FFA and STS) seemed to render in the form of a single coordinated input that flowed into a single path of processing and analysis for scientists and other analysts (Haxby, Hoffman and Gobbini, 2000).

In terms of the very recent past, there are new methods being introduced that help detect various levels of functional neuroimaging that involve STS and/or FFA (if not both) on one level or another. For example, the use of functional magnetic resonance imaging (fMRI) techniques has been used to measure a single neuron in lesion Macaques primate studies. These techniques and their measurements are similar to human studies that have foxed on face-voice selective areas and this can obviously and completely be related to STS and/or FFA, depending on the application and the situation. Image recognition tasks are being used on conscious subjects to reveal dissociated behaviors when it comes to identifying certain facial characteristics and features such as gender when linkable to things like gender, eye gaze, expression, lip moments when it comes to STS (Schall & von Kriegstein, 2014).

Other alternative models have started to emerge in the modern day above and beyond what has already been mentioned. More conventional and more widely accepted models of anatomical localization are useful to help classify the different uni-sensory pathway patterns. However, there are clear and definite clinical limits in using brain lesion injury stidies to establish brain area functions. Schall and his associates investigated FFA and STS as it related to direct communication that occurred even during auditory-only speech. Their study compared and contrasted normal subjects against those whose sufferings were from a perception disorder. For example, the Schall study compared normal subjects to those that were suffering from a perception disorder known as prosopagnosia. This disorder is when a person is unable to recognise any other face (Schall & Von Kriegstein, 2014). The evidence from the study found that there was no significant between the normal subjects and the subjects that had Prosopagnosia in terms of their functional connection to FFA. One recent output of the alternative model is that there is a lengthened multi-sensory view that helped to uncover unconscious perception. There were minute but detectable differences in the facial/auditory areas during auditory-only voice recognition in which an emotional response was clearly also present (Schankin & Wascher, 2007). There is evidence that FFA and STS indirectly communicate in concert and together and this can be measured through the prior-mentioned fMRI technology. The crosstalk between the FFA and STS signatures is noticeable and direct. There is a distinct and functional coupling between FFA and unimodal STS during voice-selected familiar speaker recognition.

Another recent treatise on FFA/STS combinations was found in the work of Iidaka of Nagoya University. He states that the “neuroimaging literature indicates the functional significance of both FFA and STS in face processing; however, the evidence for neural connectivity between the regions is limited, which suggests that these two sites play mutually independent roles in face perception and recognition” (Iidaka, 2014). This is obviously a bit of a hedge between what is being argued in this report and what has been believed prior to the FFA/STS combination framework that existed prior. It would seem there are some that are still not sold on FFA and STS being used together, at least at all times (Iidaka, 2014). Some of the same verbiage can be found in the 2013 work of Pyles et al. (Pyles, Verstynen, Scheider & Tarr, 2013). In a similar fashion, the work of Rhodes et al. (2009) reflects that the STS framework “does not code identity,” at least on its own. Indeed, different people are going to have similar (but not identical, obviously) voiceprints (Rhodes, Michie, Hughes & Byatt, 2009). The author of this report found yet another study, published in 2015, that actually combined FFA, STS as well as inferior temporal gyrus (ITG). Indeed, some have taken the FFA/STS combination and taken the concept even further (De Souza, Feitosa, Eifuku, Tamura & Ono, 2008).


The alternative model that was aimed at direct speech perception is believe to be controlled prior to recognition of a person as fMRI data also seems to imply that the STS is more complex than the voice-only processing that was done previously. This alternative model manifests and shows how simultaneous information to face/voice sensitive areas thought to cause noisy and uncontrollable environmental inputs actually result with a communicative illusion called the McGurk Effect (Blank, Anwander & Von Kreigstein, 2011).

Tractography 3D modelling systems were used to represent anatomical fibre bundles data visually against observable patterns associated with the FFA and STS areas at each individual level. Diffusion tensor imaging techniques ought differences between auditory and visual connections in magnetic resonance imaging using 2D data analysis that would help identify a person using the general FFA/STS framework. Neuropsychology combined with technological techniques provided evidence for cross-modality between face and voice. This would reveal structural connection patterns which are linked between FFA and voice-sensitive areas in the STS framework that direct voice recognition and thus rejects ideas proposed in the previously discussed models on the subject (Jou et al., 2011). Duly noted are the issues that still persist with tractography methods. This includes connectivity limitations that might induce or cause false-positive pathways (Blank, Anwander & Von Kriegstein, 2011).

There are actually other areas of life and academic study where the subject of FFA/STS comes through and one of those would be the entertainment industry. One such study was conducted in 2014 as it relates to Italian actors and the people that watch them. That study looked at STS, FFA and several other areas of face and/or body processing (Proverbio, Calbi, Manfredi & Zani, 2014). Another proper point of discussion is that FFA/STS usage and manifestations will be different in children as compared to adults because of the parts of the brain that are being used or not used not to mention that children’s brains are still forming and shaping (Golarai et al., 2007). This is echoed in the work of Scherf et al. (2007) when it is said that reduced face-selectivity and extent of activation within the reginos that will become the FFA, OFA and STS in adults” (Scherf, Behrmann, Humphreys & Luna, 2007). The point is that any findings in adults cannot be extrapolated so easily and cleanly to other age demographic. Presumably, the same would hold true of other animals. Apes and chimps would probably be similar but certainly not the same as humans, for example.


In conclusion, it is widely known that neuro-psychology has taken a dynamic consideration, at the very least, of the underlying cognition apart from behaviour and to any related neural structures. This has shone a light on people and other beings with abnormal and selective impairments as mentioned before that can happen and at the same time possibly have an effect on facial and/or speech recognition. This is compared and contrasted to other functions like vision and auditory inputs where information is flowing under natural conditions. This dual-phased approach has been advantageous in converging ideas about the FFA and STS frameworks by their respective functional and structural brain imaging data, both qualitative and quantitative. Research has also aided in the overall better understanding of behaviour and cognition to predicting epidemiology in disorders that can affect social and/or biological interactions.


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British Journal of Psychology, 102(4), pp.711-725.

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Voice- and Face-Recognition Areas. Journal of Neuroscience, 31(36), pp.12906-12915.

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Golarai, G.K. (2007). Differential development of high-level visual cortex correlates with category-specific recognition memory. Nature Neuroscience, 10(4), 512-522.

Haxby, J., Hoffman, E. And Gobbini, M. (2000). The distributed human neural system for face perception. Trends in Cognitive Sciences, 4(6), pp.223-233.

Iidaka, T. (2014). Role of the fusiform gyrus and superior temporal sulcus in face perception and recognition: An empirical review. Japanese Psychological Research, 56(1), 33-45.

Jou, R., Jackowski, A., Papademetris, X., Rajeevan, N., Staib, L. And Volkmar, F. (2011).

Diffusion tensor imaging in autism spectrum disorders: preliminary evidence of abnormal neural connectivity. Aust NZ J. Psychiatry, 45(2), pp.153-162.

Proverbio, A.M., Calbi, M., Manfredi, M., & Zani, A. (2014). Comprehending Body Language

and Mimics: An ERP and Neuroimaging Study on Italian Actors and Viewers. Plos ONE, 9(3), 1-15. doi:10.1371/journal.pone.0091294

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Perception Network with White Matter Connectivity. Plos ONE, 8(4), 1-12.


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Early morning Business term paper history essay help

Early Child Learning

What is the basic meaning of the term data-supported (or data driven) instruction?

The basic meaning of the term data-supported instruction is that individuals should utilize practices that are supported by data as the foundation for their teaching methods to use with students. There are a number of different teaching methodologies that one can employ that are either corroborated or unsubstantiated by quantifiable data. Data-driven instruction is largely based on analytics and various forms of analyzing data. Many of these different forms are based on statistics. However, the point of these analytics is that instructors can actually determine — in advance to using them in their own classrooms — best practices for teaching that are demonstrable due to findings that are rooted in data. As such, there is less need to rely on instinct and it is becoming mor readily available to utilize data to influence any number of means of pedagogy and classroom management as a whole.

2. What did you learn in the program you are now completing, including in student teaching, about the use of data-supported supported instruction? Which courses were most helpful in this regard?

There are many different facets about the use of data driven instruction that I have learned in this program. Some of the more salient points include what sources to utilize for finding practices that are supported by (relatively contemporary) data, as well as how to best implement those practices. Some of the courses that were most helpful in this regard were the Early Literacy Instruction K-Grade 2 class I completed, in addition to the Student Teaching in a prekindergarten class I have taken. These classes helped to familiarize me with the concept of data supported instruction, and also provided copious examples of ways to do it. Another course that was able to help me in the same way was technology in general education and special education class. Perhaps this one was even more useful than the other two since its focus was on technology.

3. What are the specific uses you have made of data-supported instruction in your practicum?

I was specifically able to utilize data-supported instruction in the instances in which I worked with students with disabilities in my practicum. There were not very many of these students, but I was well prepared as to what sources I could utilize to inform the learning practice of students that were on the autism spectrum and which had Asperger’s syndrome in particular. The course I took entitled Study of Disabilities in Infancy and Early Childhood helped to prepare to work with these students as well. However, I learned some techniques regarding basic socialization skills for these types of students through research in sources that offered data driven instructional practices. I was also copiously aided by the individuals that I worked with in my practicum, who were also using data supported instructional methods to work with students with disabilities.

4. What are some of the ways knowledge of and skills using data-supported instruction have made a difference in your instructional practices?

The main way in which knowledge of and skills using data-supported instruction has made a difference in my instructional practices is in my attempts at lesson planning. My coursework in general has influenced this process for me; additionally, the other professionals that I work with when I assist with classes for early learners are highy supportive of this aspect of my work as well. Still, it certainly helps me to know that there are myriad sources I can access that present data and findings based on data related to this particular area of pedagogy (early childhood instruction). I have verified and actually supplemented some of the information I have gained from my coursework and from other instructors by utilizing data driven practices to aid in my didacticism.

5. Critically discuss and evaluate the principle that methods of instruction and intervention should be data-supported. Should all instruction be data-supported? If so, why? If not, why not?

The vast majority of instruction should in fact be data driven. Data driven practices are actually influencing virtually all walks of life, from contemporary business practices to current methods for managing and implementing health care practices. Thus, utilizing a data-centric approach to solidify methods of teaching should, in theory, be no difference. The principle upon utilizing data supported approaches for pedagogy (as it is for using data in the aforementioned disciplines as well) is that such teaching is predicated on factual information as opposed to tradition, hunches, and other non-quantifiable factors.

6. Discuss the difference in meaning between the terms data-supported instructions and evidence-based methods of instruction. Are the principles of equal importance regarding their application to instruction? If so, why? If not, why not?

Although the general concept involved in data-supported instruction and evidence-based methods of instruction is the same, there actually is a distinction between these two methodologies for teaching and offering instruction to students. Evidence-based instruction is expressly predicated on empirical research, which is generally either quantitative or qualitative. Such research is typically peer-reviewed and accounts for a number of different biases, controls, and other facets of conducting original research to gain empirical evidence. Data driven instruction, on the contrary, does not necessarily involve original research. It simply involves data. Whereas the focus of evidence-based practice is actually the research conducted to determine results, the focus of data driven practices is more on the analytics and the types of analyses that are gleaned from data.