The first in a series examining innovative and effective strategies for improving student success, this introductory article examines current challenges to persistence and completion, and the demographic trends likely to further compound the issues in the coming years. It lays out a framework for building institutions designed to promote student success outcomes.
It also surveys some of the most promising innovations across all dimensions of the student experience—from the classroom and support services to campus operations and partnerships with the broader community. Every year across the United States, a significant number of students fail to complete their college degrees.
According to the National Student Clearinghouse Research Center, 30 percent of students who entered college in the fall of did not return in the second year. Often saddled with debt, and without the benefit of the increased earning power that college graduates accrue, they tend to face a difficult struggle.
According to the Federal Reserve Bank of New York, defaults are most common among students with the lowest debt burdens. By any measure—whether it's persistence from year one to year two, time to graduation, or the percentage of students who complete their degrees—many postsecondary institutions are falling short. Adding to the challenge, the profile of incoming college students has changed dramatically in recent years see figure 1. No longer does the typical student come to college straight from high school, attend classes full-time, and live on campus.
Today, 44 percent of college and university students are 24 years of age or older. Thirty percent attend class part-time, 26 percent work full-time while enrolled, and 28 percent take care of children or other dependents while pursuing their postsecondary studies. On top of that, 52 percent are the first in their families to seek higher education, 42 percent come from communities of color, and 18 percent are non-native English speakers.
Given the implications behind these changing demographics, colleges and universities need to find new ways to effectively support their students on the path to graduation. As a result, most efforts to enhance student success, though successful to some degree, have had more limited impact than they should or could.
So what should institutions of higher education do differently? How can they develop effective strategies to help students succeed in college? For an institution of higher education focused on improving student success outcomes, developing a definition of success on that particular campus constitutes an essential first step. Once the end goal is clear, the institution can develop a holistic, student-centered strategy across all dimensions of the student experience, from the classroom to support services to campus operations to relationships with the broader community , with all designed to foster measurable improvements in persistence rates, time to graduation, and completion rates see figure 2.
In the sections below, we highlight some innovative and effective strategies for improving student success across each dimension of the student experience, and we describe the foundational capacities that institutions should develop if they are to drive meaningful improvements. The proportion of students coming to college from wealthy or middle-class families—students who tend to be well-equipped to complete their postsecondary degree—is shrinking.
Before long, a majority of US schoolchildren will likely be raised in low-income households see figure 3. Among low-income graduates who attend college, many will be the first in their families to do so. These students often face an especially tough path to graduation. For students from low-income families, financing is not the only factor standing in the way of higher learning.
The study found that in schools where more than 75 percent of students receive free or reduced lunch, a proxy for income level, average literacy scores are far below the Organisation for Economic Co-operation and Development OECD average. By contrast, students attending schools where fewer than 10 percent receive free or reduced lunch tend to have the highest literacy scores in the world.
Beyond inadequate academic preparation, first-generation college students may not be able to rely on family or friends for advice about higher education. This can result in an additional burden of constructing a support network of mentors, role models, and advisors all on their own. Without suitable advice and counseling, these students may make decisions that adversely affect their circumstances—and thus, their education. The lecture-based model for learning has characterized higher education since its inception.
But, with better technology and a much deeper understanding of how students learn, educators are starting to personalize learning. They are combining leading elements of traditional teaching with digital technology, using analytics to tailor the curriculum to individual learners, and focusing on competencies rather than credit hours to help students graduate sooner.
Here we examine a few of the most promising innovations designed to improve learning outcomes—each rooted in the idea that students come to college with different levels of knowledge, learn in different ways, and progress at varying paces. The Center for Digital Education reports that blended or hybrid education models improve comprehension and test scores for 84 percent of students. A US Department of Education analysis found blended learning to be more effective than conventional face-to-face classes or online learning models.
As part of a broad initiative to redesign courses across the curriculum, Missouri State University, for example, implemented a flipped classroom model for its Introductory Psychology course. Before the change, the course was taught in a traditional lecture format. Under the new model, students read course materials and completed online assignments before coming to class, where seven staff members a full-time instructor, a graduate assistant or adjunct instructor, and five undergraduate learning assistants worked with about students per section.
Through the new format, a higher staff-to- student ratio, and other improvements, the university saw the number of students earning As or Bs in Introductory Psychology increase by 31 percent in conjunction with a drop of 10 percent in the cost of delivering the course. Blended learning classrooms can help instructors reduce in-class time by as much as one-half and use class time more efficiently. Confronted with a large number of students who were not college-ready in mathematics—a key predictor of success at Arizona State University ASU —the university launched a math readiness program in the fall of , using adaptive learning technology.
Students work through the program at their own pace, aided by an instructor. The adaptive system uses student data to continually assess what a student knows, remediate any proficiency gaps identified, and reassess student mastery of course concepts, giving each student a personalized learning path. Instructors gain an in-depth view of which students are on- and off-track and why, so they can intervene in a timely way.
Instructors also see which concepts students are struggling with across the board, so they can focus class time on mastering those concepts. According to Phil Regier, executive vice provost and dean of ASU Online, students' performance in entry-level math helps predict whether they will graduate from the university. Nontraditional students come from a variety of backgrounds and situations that typically do not lend themselves to the old model of higher education.
They have varying levels of education and experience, likely cannot afford four years to complete a degree, may need to work part-time or full-time, and often must juggle family and other responsibilities while completing their studies. For these students, competency-based models are emerging as an attractive alternative to the traditional credit-hour model. Rather than using the number of credit hours completed as the yardstick for success, competency-based degree programs focus on whether students actually master the material.
Competency-based degrees reward prior experience and measure learning through demonstrated proficiency. The number of institutions offering competency-based degrees has grown in recent years to include some large public universities, such as the University of Wisconsin, Purdue University, the University of Texas, the University of Michigan, and Northern Arizona University. The University of Wisconsin, the first major public university to offer a competency-based program, allows working adults with some college experience to finish their degrees through online courses and competency testing.
Registering for courses, securing financial aid, developing strong study skills, mastering difficult course material— students must overcome a wide variety of obstacles on the path to graduation. Student services that are effectively targeted and delivered in a timely fashion can do much to help students along and produce better outcomes.
Lack of financial resources is a major reason why students drop out of college. Some institutions, for example, assign students a financial aid counselor when they receive their acceptance, while others require students to complete their financial aid applications before they register or enroll. Arizona State University, for example, designed a series of carefully crafted, timely email messages to remind students— and in some cases, their parents—to submit the financial aid application. This strategy increased filings by the priority deadline by 72 percent.
It also increased the number of FAFSA applications submitted by the start of the following school year from 67 percent to 73 percent. In Georgia, the state covers the tuition at a Georgia institution for any eligible student who maintains a 3. Most, they found, were maintaining averages of just under 3. Students who lost support rarely graduated on time, if at all. The goal is to prevent these students from dropping out. In addition to maintaining a GPA of 2. Sometimes multiple factors cause students to fall behind.
Identifying students who are at risk of dropping out or falling behind and targeting interventions for them can be a tough task. Take Bucknell University, for example. Starting with the class of , Bucknell has been using predictive modelling to identify students who need extra help getting through their first year of college see figure 5. A code that indicates a problem such as poor attendance, low grades, or lack of campus engagement prompts the university to intervene. For example, a student who struggles in a class during the first weeks of the semester might get a prompt to seek out tutoring, receive a list of available tutoring services, or be sent a personal message from a tutor who can provide help.
Students are more likely to graduate on time if they have structured pathways to guide them. Having an academic plan when they first matriculate, a clear idea of which program and courses to choose, and timely support can all help them stay on track.
The STAR Guided Pathways Systems use technology developed by the university to give students a clear and streamlined route to graduation, by enabling them to track their progress, review requirements, and explore the impact of scheduling and changes in major on the time it will take them to graduate.
While more institutions are beginning to offer structured pathway programs that provide a clear road map to on-time graduation, too many colleges still operate on a self-service model. Students left on their own to choose from among a wide variety of disconnected courses, programs, and support services often have a hard time navigating their way to a diploma. Quite a few never make it. Tutoring can help to bridge the gap between student knowledge and course material.
Peer-to-peer or peer-led tutoring has been shown to help students bridge knowledge gaps. The University of Texas at El Paso, in a year pilot program started in , replaced one hour of lecture in a large STEM course with more than students with many, small two-hour peer-led team learning workshops, taught by intensively trained undergraduate students who had previously excelled in the course. A year study of this pilot showed that this program produced a greater than 15 percent increase in the weighted average of the passing rate.
Similar to tutoring, coaching can have positive effects on student persistence and completion. It has proven to be particularly helpful in supporting low-income and first-year students. Colleges and universities should adapt to the needs of a diverse, dynamic, and changing student population by providing flexible services and a greater sense of connection.
When students fail to graduate, sometimes the ordinary obstacles of daily life are to blame. Conflicts with work schedules, unreliable child care, lack of transportation, and unpredictable class schedules can all obstruct students in their progress toward their degrees. Campus officials should do their best to help students work around those challenges. In , more than one-third of students who enrolled in college attended part-time.
Part-time students need greater control over the hours they spend on campus, so that they can better manage their personal and academic obligations. Flexible, predictable schedules help prevent students from dropping out and encourage more students to enroll full-time. Institutions can help by designing more student-friendly class schedules. For example, they might design schedules in morning or afternoon blocks—for instance, from a. For students with obligations off-campus, these blocks can be easier to manage than a schedule of or minute courses punctuated by hours of free time.
Schedule blocks also help students form learning communities and working groups, offering vital student- to-student support and a strong sense of connectedness to faculty and institutions. Students enrolled in the program take a single course at a time, meeting for a three- or four-hour block for 18 days. Once students complete the course, they move on to the next four-credit block, enabling them to earn the same amount of credit as they would under a traditional multi-class system.
Structured scheduling can be even more beneficial when applied to entire programs. Once students choose their programs, college officials can decide on the required sequence of courses and then block those courses in coherent, connected schedules. When institutions deliver services such as advising, counseling, and financial aid only through face-to-face meetings during normal business hours, students who have jobs, families, and other off-campus responsibilities are less apt to take advantage of them.
To broaden access to services, colleges and universities are adopting a growing number of digitally enabled student services, in addition to traditional in-person services offered on campus. Johns Hopkins University, for instance, offers Skype-based advising sessions. While most institutions deliver basic digital services such as course registration, library resources, and financial aid information, colleges and universities should consider an integrated approach to digitizing these services, and they should add more complex services, such as intrusive advising.
As part of their strategies to harness technology for student advising, institutions should not forget about mobile computing, the ubiquitous communications platform of the current generation. A one-stop mobile app offers a crucial channel for accessing campus services and communicating with advisors, mentors, and counselors. Students can use this app to plan their schedules; manage their study time; keep track of assignments; form study groups; get information about campus events, clubs, and services; organize activities; communicate with individuals and groups; and a great deal more.
As first-year students started using the app in large numbers, it helped them find roommates, connect with on-campus activities, and obtain help from upperclassmen, all of which helped ease the transition to university life. Many individuals and organizations—on- and off-campus—can help students along the path to success.
A college that forges relationships with outside entities offers its students an edge in their academic careers and beyond. An institution might, for example, partner with high schools to help prepare students for college. It could collaborate with peer institutions to share leading practices, or to implement strategies cost-effectively.
Support from a variety of stakeholders, coordinated by an institution of higher learning, can help put students in a better position to succeed. Many students enter college unprepared. While 87 percent of high school students surveyed by YouthTruth said they wanted to go to college, only 45 percent felt ready to succeed there.
They may even lack the emotional stamina that college life demands. Partnerships between colleges and high schools can help ease the transition to higher education. To help improve the odds for incoming students, the AHC program worked with students who were on-track to graduate from New York City public high schools but had not met traditional benchmarks of college readiness, such as adequate SAT scores.
The program focused on preparing students for the CUNY placement exam and college-level work; helping them with college and financial-aid applications; getting them ready for college life; and assigning each student a faculty mentor, a full-time advisor, and a peer mentor who kept track of her progress during the first year of college. Students who participated in AHC scored 10 to 20 percentage points higher on the CUNY placement exam than students who were not in the program.
The University of Montana partners with high schools across Montana to help students better prepare for college-level math coursework. Research by Complete College America found that 71 percent of students in the Montana State University system do not make it through gateway-level college math classes within two years—a major deterrent to persistence.
These findings spurred the university to find a better way to prepare students for college-level math. To date, EdReady has been implemented in more than schools across Montana. Early results from a pilot found that students who used EdReady before their college math classes, compared with those who did not, earned a. Another kind of partnership allows students to earn college credits while still in high school. Early college high schools are small public schools that offer college courses, starting in ninth grade.
They are based on the theory that if you engage underrepresented students in a rigorous curriculum, with strong academic and social support, tied to the incentive of earning college credit, those students are more likely to pursue higher education. These collaborative efforts have proven extremely successful. A study by the American Institutes for Research shows that students who attend early college high school were significantly more likely to enroll in college; 25 percent of them went on to graduate, compared to just 5 percent of students who did not attend early college high schools.
While the academic program is the foundation for the El Paso success, the wraparound services available from ninth grade through college graduation really make the difference. This test site was established in by professional surveyors, and it contains 3 monuments and 37 control points. The three monuments consist of an aluminum pin, about 9 cm in diameter and 1 m long, driven into the ground so that the top is flush with the ground level. The 37 control points each consist of a brass survey cap attached to a piece of rebar about 0.
Data were collected for these three survey monuments over the span of four hours and subsequently processed using the Online Positioning User Service OPUS , which is managed by the U. Using the three monuments as a base, professional surveyors conducted a closed traverse survey of the 37 control points using a Topcon GTSD instrument. Three of the control points that we selected for this study were located within an older coniferous forest, containing Pinus echinata and Pinus taeda which were 70 to 80 years age.
The density of this area was estimated to be trees ha -1 , and the basal area was estimated to be Three other control points we selected were located within an older deciduous forest consisting of Quercus spp. The density in this area was estimated to be trees ha -1 , and the basal area was estimated to be These six control points were visited 17 times between p.
In total, this resulted in 51 independent visits to control points in the coniferous forest and 51 independent visits to control points in the deciduous forest during each season. Furthermore, an open field NSRS monument was visited 17 times during each season. When the GNSS data were collected, we randomized the forest cover type coniferous or deciduous , and further randomized the order of the three control points within each forest cover type, in an attempt to avoid potential biases.
During each visit to a control point, each of the two GNSS receivers was positioned on top of a monopod 1. Researchers consistently positioned themselves on the north side of control points as the data were collected. Furthermore, an effort was made to ensure that the internal GNSS antenna of each device was positioned directly above the control points as the data were collected.
The open field measurement was visited after the coniferous and deciduous control points were surveyed since it was about 1 km away by road. The Suunto GPS watch was always on and was therefore assumed to be ready for data collection purposes. The Suunto GPS watch collected a single waypoint at each control point during all visits. On the other hand, at each particular visit, about 15 position fixes per point were collected at 1-second intervals using the Trimble receiver.
These position fixes were subsequently averaged, prior to downloading the data from the Trimble device, to produce a single position fix during each visit to each control point. We followed this protocol to be consistent with normal field data collection practices of foresters.
The difference static horizontal position error between each determined position and the associated control point position was computed using the root mean square error RMSE , as has been done in many previous studies i. RMSE can be calculated as follows: Where n is the total number of observations in a visit; i is the i th observation of the visit; x i and y i are respectively the easting and northing of the i th observations; and x and y are the true easting and northing of the associated control point.
Unlike the standard deviation which assesses accuracy using deviations from a mean value, the RMSE was considered as a good estimator to evaluate its accuracy because it represents the deviation from the truth, not the from the mean [ 19 ]. Standard deviational ellipses were also calculated to investigate the trends of determined positions, to assess whether these are related to cover type or season. Among standard deviational ellipses parameters, the anisotropic ratio was calculated as follows: Where I a is anisotropic ratio; R and r are the length of ellipse long and short axis, respectively.
This data was used in statistical tests to determine their correlation with horizontal position error. In particular, these meteorological variables were selected due to their potential influence on GNSS signals as they pass through Earth's lower troposphere. The aforementioned local weather station reported these metrics in one-hour intervals, and thus a linear interpolation was performed to estimate their values at approximately the time of data collection.
Therefore, we reject H1 and conclude that horizontal position accuracy of the GPS watch was significantly different between two seasons. To represent how the determined GPS positions were spread around the control points, we used the standard deviational ellipse method for point data distribution analysis Fig 3. The orientation angle of the GPS watch during the leaf-on season was This small I a value suggests that the GPS points might have been distributed in a circular spread rather than an elliptical distribution Table 1.
When considered in conjunction with the area of an ellipse, the observed GPS data points for the GPS watch during the leaf-on season were widely spread from the center of the standard deviational ellipse in a circular distribution area. In contrast, during the leaf-off season, the orientation angle of the GPS watch was The ascertained area of ellipse during the leaf-off season was smaller than the area during the leaf-on season.
This indicates that the observed data points collected during the leaf-off season were more clustered and distributed more closely in relation to the control points. The point data distribution analysis for the Trimble receiver did not show any differences in response to the season. For this analysis, we obtained an orientation angle of Similarly, the distribution of observed GPS points during the leaf-off season had an orientation angle of Here, the I a values were moderately substantial, suggesting that the GPS points were comparatively spread out on the longest axis of the ellipse Fig 3.
In both seasons, the areas of the ellipses were very similar to each other, and the centers of the ellipses were very close to the control point in every case. There was no significant difference in RMSE values observed in the deciduous and coniferous forest areas. In sum, for the GPS watch, we could not reject H2; however, for the Trimble GNSS receiver, we reject H2, as we found that static horizontal position accuracy varies significantly depending on the cover type.
During the leaf-on season, the highest and lowest RSEM values were found in the deciduous forest and coniferous forest, respectively Table 1. Otherwise, during the leaf-off season, the highest and lowest RSEM values were obtained in the open field and deciduous forest, respectively.
The mean X coordinate values were negative regardless of the season and cover type, indicating that the observed points were consistently located to the west of the control point Table 1. Furthermore, the areas of the ellipses during the leaf-on season were much larger than during the leaf-off season. The highest and lowest RSEM values were found in the coniferous forest and open field respectively, regardless of the season. The mean X coordinate values were also negative regardless of season and cover type, and the areas of the ellipse were also similar to the those of the GPS watch during the leaf-off season.
For H3, the correlation coefficient r values were summarized in Table 3. These values indicate that air temperature and absolute humidity were significantly correlated with RMSE, regardless of equipment type. For both the Suunto watch and the Trimble receiver, weak positive correlations were found between the RMSE and the air temperature 0. Except for the temperature and absolute humidity, there were no other observed significant correlations between the horizontal position accuracy and meteorological factors.
In sum, we only found weak positive correlation between RMSE values and air temperature or absolute humidity. It was found that there were no significant differences observed during the leaf-off season regardless of the cover type Table 4. Otherwise, there were significant differences during the leaf-on season, and across both seasons. However, we reject H4 with respect to the leaf-on season.
Using a Trimble mapping-grade receiver as a basis of comparison, this study attempted to assess the accuracy of a recreation-grade GPS watch in response to the seasonal fluctuations and variations in the canopy cover and to investigate the correlation between the accuracy of the watch and meteorological factors that included humidity, air temperature, atmospheric pressure, and wind speed.
The accuracy of the GPS watch was found to be significantly enhanced during the leaf-off season relative to the leaf-on season. In comparison, the accuracy of mapping-grade GNSS receiver was found to not be affected by seasonal fluctuations. One study [ 5 ] also had similar results, in that the significant effect in response to the change in season was only found when it was measured using the recreation-grade GNSS receiver, not with the mapping-grade GNSS receiver.
Another study [ 17 ] conducted at the same site also found that the static horizontal accuracy was not affected by season using a mapping-grade GNSS receiver. Cumulatively, these results indicated that mapping-grade GNSS receivers might not be affected by the seasonal fluctuation, whereas the recreation-grade GNSS receivers do appear to be more sensitive to the seasonal fluctuation.
Although vegetation obstruction introduces a degree of error in the forested area through the blockage of satellite signals or through multipath signals [ 11 , 20 , 21 ], the defoliation during the winter season in the deciduous forests had a weak, but positive effect on enhancing the static horizontal position accuracy. Indeed, there was a significant increase in accuracy observed in every cover type coniferous forest, deciduous forest, and open field during the leaf-off season in this study.
In other studies, the improvement in accuracy during leaf-off season in the deciduous forest was not observed [ 5 , 11 , 17 ]. One possible explanation for the improvement of GPS watch accuracy during the leaf-off season, regardless of cover type, is the cumulative error caused by different meteorological conditions such as relatively low temperature and absolute humidity during leaf-off season that had a positive correlation with the RMSE values negative correlation with accuracy [ 16 , 19 , 22 , 23 ].
Furthermore, due to the improvements in accuracy observed during leaf-off seasons, there was no significant difference between accuracy of GPS watch and mapping-grade receiver during this season. This suggests that the GPS watch could be used to replace the mapping-grade receivers when considering cost efficiency, especially during leaf-off season.
Even though the outliers were also considered in this study, the static horizontal positions determined by the recreation-grade GNSS receiver and the mapping-grade GNSS receiver in the forested conditions had relatively low mean RMSE values in the range of 4. These results confirm that our research was conducted reliably and indicate that the watch-type recreation-grade GNSS receiver maintained similar degree of accuracy to the conventional handheld type of recreation-grade GNSS receiver in a forested area.
The significant effects cover type had on static horizontal position accuracy were only observed when the mapping-grade GNSS receiver was used, not when the GPS watch was used due to the large variances observed. However, this significant difference was only found between the open field and forested area coniferous and deciduous forest , not between the coniferous and deciduous forests.
These results contrasted with other studies that have shown a significant difference in static horizontal position accuracy when using a recreation-grade GNSS receiver among varying forest cover type [ 5 , 11 , 12 , 24 ]. However, other studies [ 16 , 17 ] did not find a significant difference in static horizontal position accuracy between forest cover types; yet, these studies employed mapping-grade GNSS receivers.
These results indicate that the cover type itself is not the main factor affecting the static horizontal position accuracy. Rather, it suggests that canopy coverage might be the primary consideration when determining the GPS receiver accuracy [ 19 ]. Indeed, the GPS accuracy was shown to be improved in a young coniferous forest relative to an old coniferous forest [ 11 , 24 , 25 ] and further accuracy improved in post-thinning conditions compared to the pre-thinning conditions [ 9 ].
We applied the standard deviational ellipse to investigate the direction or tendency of error, depending on the season and cover type; this study was the first of its type in forestry research to use GNSS data in this manner. As the standard deviational ellipse is a measure of the distribution of observed points, it provided information about the data concentration, including orientation determined by the direction of the longest axis of observed points , anisotropic ratio ratio of the longest and shortest axes , and the area of the ellipse intuitively through images and quantification [ 26 ].
Although the area of ellipse was considerably smaller during leaf-off season than during the leaf-on season regardless of GPS receiver type, a specific tendency in the orientation and the area of an ellipse was not observed in response to the cover type.
However, the mean center of each ellipse was located on the western side of the control points regardless of the GPS receiver types. There have been a few studies considering the direction of the error [ 9 , 11 , 18 ]. One study [ 9 ] using evaluations of rose diagrams confirmed that the bias in error changes in response to thinning. Our results showed that there were more points distributed on the western side of the control points as opposed to the eastern side.
Although the location of nearby trees around control points was not investigated in our study, Bettinger and Merry [ 18 ] suggested that the vegetation near the control point influenced the direction and magnitude of the positional error. Finally, while we used the average value of 15 position fixes from the Trimble device to evaluate these, the use of the original 15 position fixes may have revealed some balanced error. However, at this time we are unable to determine whether the standard deviational ellipses would have been different had we evaluated them in this manner.
Prior studies have indicated that there was no correlation between meteorological variables of the lower troposphere and static horizontal position accuracy [ 11 , 16 — 18 ]. The only study [ 16 ], among the aforementioned studies, found a significant and negative correlation between air temperature and RMSE values at the deciduous forest using a mapping-grade GNSS receiver.
In our study, however, we found a significant, but weakly positive correlation between air temperature and RMSE regardless of GPS receiver type. The air temperature appears to have receiver-specific effects on horizontal position accuracy, as there was no consistent correlation between air temperature and RMSE values in other studies, regardless of GPS receiver types.
Other studies have considered the correlation between relative humidity and RMSE [ 11 , 16 — 18 ], but this study investigated the correlation between absolute humidity and RMSE, in which it found a significant, albeit weakly positive correlation. This phenomenon could be explained by the influence atmospheric water vapor has upon the travel of the GPS signal from the satellite. This could be achieved either by delaying the GPS signal propagation, thereby reducing the signal speed, or by causing additional multipath effects [ 16 , 19 , 23 ].
Furthermore, the engrossing trend was observed that the RMSE values measured by the GPS watch were increased when the wind direction was suddenly changed. However, due to the limitations of this study, not all meteorological variables were precisely monitored at the forest, and the change in wind direction was not sufficiently quantified to determine the correlation between wind direction and RMSE values. The study presented here investigated the static horizontal position accuracy of a GPS watch under varying forest cover types, seasonal fluctuations, and meteorological conditions.
The key findings of this study were as follows: 1 the accuracy of the GPS watch was significantly affected by the season, but not by the cover type; 2 during the leaf-off season, the accuracy of the GPS watch did not differ significantly relative to the accuracy of the mapping-grade GNSS receiver; 3 the RMSE values of both GPS receivers had a significant but weakly positive correlation with air temperature and absolute humidity.
These results suggest that canopy coverage, rather than the forest cover type, might play a more critical role in governing the static horizontal position accuracy of GPS receivers in forested areas. Furthermore, the GPS watch showed improvements in static horizontal position accuracy during the leaf-off season, so that it had a similar level of accuracy to that of a mapping-grade GNSS receiver. Our results suggest that GPS watch might be able to provide an acceptable quality of locational information for forest management purposes during the leaf-off season.
However, due to the limitations inherent to a small antenna, the quality of location information might not be able to guarantee acceptable static horizontal position accuracy during the leaf-on season. This technology uses fixed and known positions to correct the GPS signal and might provide better static horizontal position accuracy in various conditions. Regarding the effects of meteorological variables, the GPS watch and the mapping-grade GNSS receiver both indicated a significant correlation between positional accuracy and two environmental variables air temperature and absolute humidity.
Accordingly, air temperature and absolute humidity should be considered when these types of GPS receivers are used in forested areas. Our study had some limitations in that 1 the GPS watch used for this study was released in , and may therefore not be a reliable indicator of the current technological state of GPS watches; 2 the locations of nearby trees were not measured to explain the distributions of observed points, and 3 the meteorological variables within the forest were not monitored in an all-encompassing manner.
Nevertheless, our study determined the accuracy of a GPS watch in various circumstances and illustrated the potential application of the GPS watch for forest management purposes, especially during the leaf-off season. Although users will need to decide whether the accuracy and reliability of a GPS watch is sufficient for their purposes, they should keep in mind that even mapping-grade GNSS receiver accuracy can vary depending on the working conditions, such as changes in canopy cover and meteorological conditions.
Given falling prices and ease-of-accessibility, the GPS watch may serve as a circumstantially attractive replacement for the mapping-grade GNSS receivers. Further research exploring the technological developments in future GPS watches is therefore a relevant and useful endeavor. We appreciate and value the thoughtful concerns and suggestions of the anonymous reviewers of this manuscript. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.
It should be noted that relatively large confidence intervals were also derived from the sample data, indicating significant variability in the observed positional errors. The RMSE was generally lowest at Points 2 and 6, which were in relatively open areas, thus there is an assumed reduction in multipath error.
The overall average horizontal position error was worst during the Low AM data collection period over both seasons. The single maximum horizontal position error observation was observed at Point 4 about m , and is likely an outlier as the next largest single observation of horizontal position error was approximately 30 m Table 3.
The minimum horizontal position error from a single observation, across all points and seasons, fell below 1 m at least once in 20 of the 48 cases 6 points, 8 data collection periods and fell below 2 m at least once in 39 of the 48 cases Table 3.
On average, time of year also did not seem to influence the average error observed in horizontal positions when WiFi capability was enabled. And as with the GPS-only data, relatively large confidence intervals were derived from the sample data at these points, indicating significant variability in the observed positional errors.
Further, the RMSE was again generally lowest at Points 2 and 6, which were in relatively open areas, thus there is an assumed reduction in multipath error. The single maximum horizontal position error observation was observed at Point 5 about 39 m when WiFi was enabled Table 5 , and the single minimum horizontal position error observation was observed at Point 6 about 11 cm.
The minimum horizontal position error from a single observation, across all points and seasons, fell below 1 m in 15 of the 48 cases, and fell below 2 m in 35 of the 48 cases Table 5. However, we could not reject the null hypothesis when only considering the data collected from a single sample point. The error was also statistically significantly different when considering all sets of observations of horizontal position error observed during AM, Leaf-off, and high data collection periods High AM and High PM separately data collection periods.
Sample Point 1 was the only point where there were many instances of detection of significant differences among the GPS-only and WiFi-enabled data. While we cannot be completely certain what is causing the significant differences at this sample point, Point 1 is unique in that it is surrounded on each side by multistory buildings which may be increasing the positional error when GPS-only data was collected.
However, each of these buildings house WiFi access points which may lead to a stronger WiFi signal at this point reducing the positional error when the WiFi is enabled. In examining the frequency of the error across all points, interesting patterns emerge Fig 3.
For instance, during GPS-only data collection, both Points 2 and 6 had no instances of error sampling. Similar to Point 1, the occurrence of error was mostly clustered in between 2 m and 20 m during GPS-only data collection. The error distributions at Point 4 and Point 5 were very similar between WiFi and GPS-only sampling with the majority of error falling between 2 to 10 m at Point 4 and ranging from 2 m to more than 20 m at Point 5.
The frequency of horizontal position error at Point 6 was most prominent between the 0 and 2 m and 2 to 5 m ranges for both GPS-only and WiFi data collection. In an effort to identify what might be causing the horizontal error, a correlation between the percent land cover within the 30 m sample buffer and the error was derived Table 6. A moderate positive correlation between the building land cover class and horizontal positional error was found during GPS-only data collection indicating that an increased presence of buildings led to an increase in horizontal positional error.
This correlation was more pronounced during WiFi-enabled data collection but still only moderately. Minor positive correlations were found between tree cover and low vegetation and error during both GPS-only and WiFi-enabled data collection. Additionally, a minor negative correlation was found between the percent land cover classified as sidewalks, roads, and parking lots and positional error.
While understanding the amount of horizontal error between a survey monument and a position collected by the iPhone 6 is useful, knowing the predominant direction of that error may also be important. In examining the directional error, the angle between survey monuments and positions determined by the iPhone was calculated, and some general patterns emerged. For example, directional error at Point 1 using GPS-only data predominantly ranged in cardinal direction from south to north with a majority of directional error occurring in a west to northwest direction under all data collection conditions Fig 4 , yet during data collection Period 3, there was no dominant direction of error.
When WiFi was enabled, the direction of error was comparable to GPS-only Fig 5 but most pronounced from the west to northwest. At Point 2, there were several instances where there was no dominant direction of error during GPS-only data collection periods yet when considering data collected under all data collection conditions the error most often occurred from southwest to north.
When WiFi was enabled, error typically occurred in a westerly pattern ranging from the west-southwest to north-northwest. GPS-only data collection at Point 3 revealed directional error predominantly ranging from west-northwest to the north-northeast. During two collection periods, Low AM leaf-on and Low PM leaf-on, there was no dominant directional error suggesting error was dispersed in all directions.
During WiFi data collection, error during Period 1 and Period 3 were consistently in a northerly direction clustered between north-northwest and north-northeast, and in most other cases northwest to northeast. When WiFi was enabled at Point 4, the direction of error often ranged between south-southwest to north-northwest while error was more dispersed across the cardinal directions for GPS-only data.
During GPS-only data collection at Point 5, the vast majority of data error in all data collection conditions fell between the cardinal directions of west-southwest and north. Similarly, when WiFi-enabled data collection occurred at Point 5, the majority of direction error fell between west-southwest and north-northwest. Conversely, at Point 6 when using GPS-only there were 5 different collection periods where there was no pronounced directional error, yet when directional error was pronounced, there was little consistency between data collection periods.
When WiFi was enabled at this point, the directional error lacked a dominant direction. Finally, almost uniformly, there was generally weak to no correlation between horizontal position error of the positions determined by the iPhone, and temperature, barometric pressure, wind speed, and relative humidity during the data collection effort Table 7. When considering the static horizontal position error from all data points collected, the error observed in GPS-only data was significantly different from the error observed in the WiFi- enabled data.
Further, data collected in the morning, and data collected during high WiFi use periods also indicated that GPS-only data and WiFi-enabled data had significantly different levels of horizontal position error. During only the leaf-off season were similar significant differences observed between GPS-only and WiFi-enabled data. These observations, while not significant at every data collection point, suggest that on average, enabling the iPhone to use WiFi signals to augment the determination of horizontal positions will lead to higher quality positional information.
While it was unclear how extensive the WiFi services were utilized by the iPhone, the opportunity to use these services affected positional accuracy. Interestingly, nearby buildings may have influenced the direction of error observed, due likely to multipathed signals from either the GPS satellite constellation or the WiFi signal emitting devices.
One pattern became evident when interpreting the results for data collected when the WiFi was enabled: the average positional error was greater around Point 5 than all other test points, regardless of leaf-on or leaf-off conditions, and morning or afternoon data collection efforts, the error was most pronounced at this point. Some of this could likely be explained by multipath conditions or a simple deterioration in GPS signals.
However, this survey monument was located across a two-lane street from a multistory hotel and convention center and under a large tree. Comparatively, Points 2 and 6 were frequently the data collection points with the lowest RMSE, particularly during WiFi data collection periods. During GPS-only collection, Point 6 had low positional error compared to data collected at other survey monuments.
Each of these monuments Points 2 and 6 were located in relatively open areas, and thus this may indicate that what plays the largest role in smartphone GPS data accuracy may be proximity to multistory structures, rather than increased activity on a nearby WiFi network or the presence of nearby trees. Further, our work has provided results that are similar to those provided by Garnett and Stewart [ 23 ], Weaver et al. This observational study is one of the first of its kind to examine the positional accuracy of horizontal positions determined by a smartphone during high and low human activity, and during two different seasons of the year influencing the amount and presence of nearby tree canopies.
Many of the influential factors could not be closely controlled by the study team; therefore, numerous samples were collected over each survey monument during random times of the day to understand on average the level of positional error one might expect. While protocols for data collection seem reasonable randomize the order of data collection, routinely collect data in the same manner at each monument, etc. Persistent efforts were employed to acquire information on the status of the network during the data collection periods, yet we were unable to acquire metrics regarding the WiFi signal status around the test course due to the university not allowing us access to these metrics.
As a result, our observations should reflect average performance of the smartphone under average WiFi operating conditions. This study could be complemented by further studies that focus on some of the limitations we observed. For example, we were unable to sufficiently understand why horizontal position accuracy improved during periods of time when WiFi usage was high.
Given our lack of access to the technical specifications of the WiFi network, perhaps management of the network during high use periods contributed to this result. Additional research that incorporates a measurement of the strength of the WiFi signal at each sample point would be useful.
Further, the results we observed were highly variable around each sample point, perhaps due to the heterogeneous nature of the urban environment spatial arrangement of trees, buildings, etc. A complementary study to better understand the impact of the spatial arrangement of features, similar to that of Bettinger and Merry [ 41 ] that was conducted in a forest, may further our understanding of these issues.
To further investigate the role multipath plays in error, it would be useful to set up a device at each sample point and continuously collect data over a period of time and at specified time intervals. Here, the assumption is that the error would repeat as long as the surrounding landscape buildings, trees, etc.
And finally, as computing technology continues to evolve, continued observational and hypothesis-driven studies of smartphone accuracy in urban environments will be necessary to inform society of the potential practical and scientific uses of these hand-held positional and navigational devices.
Specifically, a similar research endeavor using a newer smartphone with an improved GPS chip would be valuable. The horizontal position error associated with GPS positions determined by a smartphone is often assumed negligible by ordinary users of the technology. However, as smartphones are used more often for data collection purposes, perhaps during crowd sourcing data collection exercises or the capture of positional information through various smartphone apps, this concern may need more attention.
Our study has shown that the overall average horizontal position error of the iPhone 6 is in the 7—13 m range, depending on conditions, which is consistent with the general accuracy levels observed of recreation-grade GPS receivers in potential high multipath environments. It seemed in our study that the time of year did not influence the average horizontal position error observed when GPS-only parameters were assumed, or when WiFi was enabled.
Our observations of average horizontal position error only seemed to improve with time of day afternoon during the leaf-off season. Interestingly, horizontal position error seemed to improve in general during perceived high WiFi usage periods when more people were present within each season and during each time of day most prominently in the afternoon. In general, directional error was consistent at each data collection point during both GPS-only and WiFi collection.
The most pronounced instance of directional error occurred at Point 5 in a west to northwest direction. Data collection may have been subjected to multipath issues at some of the data collection points. We saw moderate correlation between the presence of buildings and positional error during both GPS-only and WiFi-enabled data collection.
Finally, weather conditions had little to no influence on the accuracy of data collected. We would like to thank the Warnell School of Forestry and Natural resources for their continued support. We also thank the anonymous reviewers for their time and effort. Browse Subject Areas?
Click through the PLOS taxonomy to find articles in your field. Abstract An iPhone 6 using the Avenza software for capturing horizontal positions was employed to understand relative positional accuracy in an urban environment, during two seasons of the year, two times of day, and two perceived WiFi usage periods. Funding: The author s received no specific funding for this work. Introduction Smartphones have become ubiquitous tools of the human race, as millions of people now go about their days with small GPS-capable computers in their hands or pockets.
Download: PPT. Fig 1. Location of survey monuments used for sampling on the University of Georgia campus. Statistical tests Statistical tests were employed to determine whether positional error from GPS-only and WiFi-enabled data were significantly different. Using nineteen collection categories Table 1 , the following hypotheses were tested: H o : The horizontal position errors of the GPS-only and WiFi data are not distributed differently.
Table 1. Results In examining all horizontal position error derived during the GPS-only data collection effort, the minimum positional error was 0. Table 2. Table 3. Minimum and maximum horizontal position error by collection period using GPS only. Table 4. Table 5.
Minimum and maximum distance horizontal position error by collection period using WiFi. Fig 3. Table 6. Fig 4. Rose diagrams illustrating directional error of data collected using GPS-only capabilities of an iPhone 6. Fig 5. Rose diagrams illustrating directional error of data collected using WiFi enabled capabilities of an iPhone 6.
Table 7. Discussion When considering the static horizontal position error from all data points collected, the error observed in GPS-only data was significantly different from the error observed in the WiFi- enabled data. Conclusions The horizontal position error associated with GPS positions determined by a smartphone is often assumed negligible by ordinary users of the technology.
Supporting information. S1 Data. Spreadsheet containing GPS data collected. S2 Data. Weather data associated with data collection. Acknowledgments We would like to thank the Warnell School of Forestry and Natural resources for their continued support. References 1. Das RD, Winter S. A fuzzy logic based transport mode detection framework in urban environment. J Intelligent Trans Syst. View Article Google Scholar 2. Classification of automobile and transit trips from smartphone data: Enhancing accuracy using spatial statistics and GIS.
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It should feel seamless for the students. Watkins stressed the importance of coaching for all students. This is a similar idea. The most important goal for her department, said McLean, is to provide resources for every student to plan for and create a meaningful professional life.
Her department will offer training at times that work with student schedules, particularly important for graduate students who may only have evening and weekend hours to devote to such resources. This new system offers various levels of access for different audiences within a robust information platform, Fawcett added. But, in the end, it benefits the students and their path to success, which is our larger purpose.
This piece was originally published August 26, , with St.
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