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The instructions provided describe how to submit feedback on Esri Basemaps and the ArcGIS Online World Geocoding service. Esri has created feature services intended solely for feedback to enable users to self-report errors found in Basemaps or with Geocoding results.
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The feedback is reviewed by the ArcGIS Online team and is considered for an update, providing better maps to customers.
Navigate to the following link to help improve Esri Basemaps via the Community Maps Program where you can Provide Feedback, Edit Features, and Share Data.
We value your feedback, both pro and con. Please respect staff. Name-calling, insults, racial slurs, profanity or similar remarks are not okay. We do not tolerate harassment in any form. Staff do not have to respond to such messages.
Aims: The study examined the efficacy of a web-based unguided self-help programme with automated feedback. The programme was based on cognitive behaviour therapy for insomnia (CBT-I). The investigation particularly focused on factors that contribute to the maintenance of insomnia and tested whether treatment effects were stable over a period of 12 months.
Conclusions: This study adds evidence to the literature on unguided online interventions for insomnia, and indicates that online CBT-I can have substantial long-term effects on relevant sleep-related outcome parameters. Moreover, the results indicate that sleep-related cognitions and safety behaviour can be successfully altered with an unguided CBT-I intervention.
Auer et al. (2014) investigated the effect of a pop-up message that appeared after 1,000 consecutive online slot machine games had been played during a single gambling session. The study analyzed 400,000 gambling sessions (200,000 sessions before the pop-up had been introduced and 200,000 after the pop-up had been introduced). The study found that the pop-up message had a limited effect on a small percentage of players. Although the study reported nine times as many gamblers stopped after 1000 consecutive plays compared to those gamblers before the introduction of the pop-up message, the number of gamblers that actually stopped after viewing the pop-up message was less than 1%.
Outside of the addiction studies field, Cho et al. (2009) studied the effects of personalized behavioral feedback in the management of Type 2 diabetes. Here, Type 2 diabetes patients used mobile phones that automatically measured glucose levels and transferred this information to the internet. The authors found that web-based charts displaying individual data and personalized feedback in the form of text messages were effective means for decreasing the glucose level in diabetes sufferers. A similar study by Farmer et al. (2005) described a real-time system for Type 1 diabetes patients. Their telemedicine system collected real-time information about glucose level as well as information regarding insulin dose, eating patterns, and physical exercise. The system gave verbal and illustrated feedback so that patients could better keep track and control their glucose level. The feedback led to regular maintenance of blood glucose level and an increased number of patients met their predetermined targets. Another area where behavioral feedback has been investigated is in the area of sports and fitness. Buttussi et al. (2006) investigated the use of mobile phone guides in fitness activities using a Mobile Personal Trainer (MOPET) application. The mobile app gave verbal navigation assistance and also used a 3D-animated motivator. Evaluation of the results supported the use of mobile apps and embodied virtual trainers in outdoor fitness applications.
After the data were provided, the present authors gave very carefully thought about all of the ways in which the data could be analyzed. Following an initial inspection of the data, it became clear that comparing the overall amount of time and money spent by gamblers before and after using the personalized feedback system (i.e., within-group analysis) would not be meaningful because there was very large variation in what individual gamblers spent financially and how long they played in terms of time. For instance, some gamblers spent 100s of Euros on every gambling session while others spent just a few Euros per session. The resulting mean average differences in terms of time and money spent as a whole group before and after using the personalized feedback tool were therefore likely to be spurious because of the large individual differences in gambling behavior. Futhermore, there was no way of assessing whether the difference in the amount of time and money spent within group was significant as there was no reliable comparison point. Therefore, a control group was needed.
One way to determine a valid control group is via a matched pairs design in which similar players out of the population are assigned to each of the 1,358 target group members. The control group population only comprised online gamblers that had not used the system but who played during the period in which those in the target group signed up for the system. Matched pairs for the target group members were chosen using the following criteria:
The way that the information was presented was in line with previous laboratory research and followed concepts of HCI and PSD principles (Fogg, 2003; Wohl et al., 2010, 2014). One of the goals of the study was to investigate whether personalized numerical and visual (as well as normative) feedback could change behavior (i.e., reduce the amount of time and money spent gambling) in a real world gambling environment. Results showed that compared to the control group, players significantly decreased the amount of time and money spent after they were exposed to the personalized information about their individual behavior for the first time. These results appear to show that personalized, behavioral feedback has significant and relatively immediate effects on subsequent gambling behavior compared to controls. This is not surprising given the evidence in the gambling studies field (e.g., Auer and Griffiths, 2013a; Kim et al., 2014) as well as other areas of non-gambling research (Farmer et al., 2005; Buttussi et al., 2006; Cho et al., 2009; Colkesen et al., 2013). The main results were also validated by additional analysis showing that the individual players reacted similarly with respect to time and money spent when provided with personalized feedback.
Despite the many strengths of this study, there are a number of key limitations. All of the participants in the target population had voluntarily registered to use the system and were therefore not selected randomly from the population of players. In an attempt to overcome this, a matched pairs design was chosen in which each and every target group member was matched with a number of most similar control group members who were not given personalized feedback. This matched pairs design is the next best approach in overcoming the problems associated with investigating non-randomly selected target group members. However, it is worth noting that in reality, most responsible gambling tools and systems used currently are very often based on voluntary commitments from the player. Therefore, the context in which the gamblers were investigated in the present study had high external (and ecological) validity. However, the reliability is limited due to the fact that data were only collected from one online gambling environment. Replicating the results with other operators and other gambling channels (such as EGMs) would help further corroborate the findings reported here.
Given this limitation, the authors cannot be certain that it was the intervention that caused the difference in behavior compared to controls, rather than differences in the gamblers who signed up and their motivation to gamble more or less. Simply looking at reductions in time and money spent gambling does not allow the causal mechanism to be determined in the present study. This could only have been done if the players were randomly allocated to receive the informative messages (which was impossible to do given the data were collected on a real gambling site). The present authors have no way of determining if the gamblers read the messages they received and how they were influenced if they were read, or whether it was the personalized feedback and/or the comparative feedback that had most influence in reducing the time and money spent gambling.
On this page, the following commonly used feedback systems at Stanford -- Canvas, Google Forms, Qualtrics, and Poll Everywhere -- are recommended as self-service options to generate a mid-term evaluation survey.
Anonymous online surveys are effective teaching tools that help provide the opportunity to gather feedback, make modifications, assess prior student knowledge, and perform active learning exercises. There are a variety of tools enable instructors to create and customize surveys and capture detailed anonymous feedback. Four tools to create surveys are listed below with supplemental resources. General recommendations for online surveys are to assure students of anonymity, to write precise questions, keep the survey short, and to quickly review and respond to student feedback after it is captured. For more information, please see the Responding to Online Feedback page.
In this article, you will find the best software you can use to record and analyze customer feedback, such as customer survey tools, online review tools, community feedback tools, and even user testing tools. Read on to see what each of these customer feedback tools has to offer.
Customer feedback tools automate the feedback collecting process and transform it into data that can be easily analyzed to deliver a better customer experience. Here are a few tools you should consider using: 2351a5e196
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