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Are You Inspired or Overwhelmed? The Benefits of Teachers Setting Challenging Expectations

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Title: Are You Inspired or Overwhelmed? The Benefits of Teachers Setting Challenging Expectations
Language: English
Authors: Robert J. Mills (ORCID 0000-0002-5936-8737), Emily R. Fyfe, Tanya Beaulieu, Maddy Mills
Source: Instructional Science: An International Journal of the Learning Sciences. 2024 52(4):693-709.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 17
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Teacher Expectations of Students, Academic Achievement, Teaching Methods, Communication (Thought Transfer), College Students, Information Management, Data Analysis, Assignments, Coding, Authentic Learning
DOI: 10.1007/s11251-023-09658-0
ISSN: 0020-4277
1573-1952
Abstract: Teachers form expectations that can influence their students' performance, and there are a variety of ways these expectations can be communicated. In the current study, we tested a novel method for communicating expectations via examples of student work--examples that contain basic, entry-level work and communicate low, but manageable expectations or examples that contain complex, advanced-level work and communicate high and challenging expectations. Across three semesters, 91 college students in a data management course completed a class assignment that involved exploratory coding activities. Prior to the assignment, students were randomly assigned to view basic or advanced examples of student work. Students assigned to the advanced-examples condition reported higher perceptions of task difficulty and frustration, but they also exhibited higher levels of performance in terms of the complexity of their own work. Results suggest that setting challenging expectations can create a desirable difficulty that ultimately benefits students' performance in an authentic learning environment.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1431017
Database: ERIC
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  Value: <anid>AN0178415865;isl01aug.24;2024Jul16.05:34;v2.2.500</anid> <title id="AN0178415865-1">Are you inspired or overwhelmed? The benefits of teachers setting challenging expectations </title> <p>Teachers form expectations that can influence their students' performance, and there are a variety of ways these expectations can be communicated. In the current study, we tested a novel method for communicating expectations via examples of student work—examples that contain basic, entry-level work and communicate low, but manageable expectations or examples that contain complex, advanced-level work and communicate high and challenging expectations. Across three semesters, 91 college students in a data management course completed a class assignment that involved exploratory coding activities. Prior to the assignment, students were randomly assigned to view basic or advanced examples of student work. Students assigned to the advanced-examples condition reported higher perceptions of task difficulty and frustration, but they also exhibited higher levels of performance in terms of the complexity of their own work. Results suggest that setting challenging expectations can create a desirable difficulty that ultimately benefits students' performance in an authentic learning environment.</p> <p>Keywords: Teacher expectancy; Peer examples; Task difficulty; Programming activity</p> <p>Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</p> <p>Teachers have a significant influence on their students, particularly through their implicit or explicit communication of expectations (Century, [<reflink idref="bib10" id="ref1">10</reflink>]; Jussim & Harber, [<reflink idref="bib24" id="ref2">24</reflink>]). The way teachers communicate their expectations—including whether they expect their students to succeed or fail, or to feel challenged or overwhelmed—can impact student perceptions and performance in the classroom. However, the effects of these expectations can vary depending on how they are communicated. The current study presents a randomized experiment conducted in an authentic online college class to investigate how teachers' expectations affect students' task performance and perceived task difficulty. Specifically, this study examines a novel method of communicating expectations by providing different examples of peers' work that exemplify either basic entry-level skills or complex advanced-level skills. The experiment aims to shed light on how teacher expectations can influence college student performance in data and Structured Query Language (SQL) coding activities on authentic class assignments.</p> <hd id="AN0178415865-2">Teacher expectancy effects</hd> <p>Research on teacher expectations in learning dates back at least 50 years to a classic study conducted by Rosenthal and Jacobson ([<reflink idref="bib37" id="ref3">37</reflink>]) which examined the relationship between teacher expectancy and academic success. The research was guided by the idea that a teacher's expectations for a student's performance can influence that student's performance on a target task, regardless of that student's actual abilities. That is, if the teacher expects a student to succeed, then that student is likely to succeed, but if a teacher expects a student to fail, then that student is likely to fail. Research on teacher expectancy effects focuses on both input and output variables. Input variables represent the factors that create a teacher's given expectation, and output variables represent the ways the teacher communicates that expectation to the student (Harris & Rosenthal, [<reflink idref="bib17" id="ref4">17</reflink>]).</p> <p>Previous work has tended to focus on input variables at the <emph>individual</emph> student level, such as a student's prior grades or in-class performance (Friedrich et al., [<reflink idref="bib14" id="ref5">14</reflink>]). But these student-level factors are often shaped by teachers' biases and stereotypes (Reyna, [<reflink idref="bib35" id="ref6">35</reflink>]; Tenenbaum & Ruck, [<reflink idref="bib44" id="ref7">44</reflink>]), which can lead to an unfair and uneven learning environment. For example, preconceived teacher expectations tend to favor men over women in mathematics, which can lead teachers to close off the possibility of success for a female in their class (Jaremus et al., [<reflink idref="bib22" id="ref8">22</reflink>]). To avoid these concerns at the individual level, teacher expectancies can be implemented at the <emph>class</emph> level (or broader) to convey appropriate expectations for a whole group of students (Szumski & Karwowski, [<reflink idref="bib42" id="ref9">42</reflink>]).</p> <p>The current research focuses on class-level teacher expectations and strategies to communicate desired outcomes. Strategies for communicating teacher expectations vary and can be communicated to students in a direct or indirect manner. Direct teacher expectancy often includes written or verbal statements. For instance, in a problem-solving setting, Fyfe & Brown ([<reflink idref="bib15" id="ref10">15</reflink>]) manipulated teacher expectations directly by telling students that the problems would be easy and likely solved correctly (expecting success) or that the problems would be hard and likely solved incorrectly (expecting failure). Other examples of direct expectancies include clearly stated learning objectives, documented activity goals, praising student progress, and using exams to evaluate learning (Alonso-Tapia & Ruiz-Diaz, [<reflink idref="bib2" id="ref11">2</reflink>]; Toksoy & Acar, [<reflink idref="bib45" id="ref12">45</reflink>]).</p> <p>Teachers can also communicate their expectations indirectly with strategies like classroom grouping, tone of voice, prompting and waiting, the quantity of interaction, differential feedback, and differential activities and questions (Braun, [<reflink idref="bib9" id="ref13">9</reflink>]). These indirect methods aim to help foster students' self-expectations by meticulously crafting learning experiences, which in turn, influence student behavior (Harris & Rosenthal, [<reflink idref="bib17" id="ref14">17</reflink>]; Mantei & Kervin, [<reflink idref="bib27" id="ref15">27</reflink>]). Another indirect strategy that can be used to convey teacher expectancy is through the use of examples of student work in the classroom (Alonso-Tapia & Ruiz-Diaz, [<reflink idref="bib2" id="ref16">2</reflink>]), which is the focus of the current study.</p> <hd id="AN0178415865-3">Using peer examples to set expectations</hd> <p>Using examples of student work is an important design consideration that has strong potential to facilitate learning. Examples are useful for communicating, illustrating, clarifying, portraying, and differentiating ideas (Mills, [<reflink idref="bib4" id="ref17">4</reflink>]; Merrill, [<reflink idref="bib30" id="ref18">30</reflink>]). A longstanding literature has focused on the use of worked examples that showcase a correct solution to a problem see Atkinson et al. ([<reflink idref="bib3" id="ref19">3</reflink>]). From a cognitive perspective, these worked examples often "aid in skill acquisition because they help learners understand how to apply a procedure to solve a particular program and transfer that knowledge to similar problems" (Scheiter, [<reflink idref="bib38" id="ref20">38</reflink>], p. 907). Incorporating these types of examples in education can also improve affective variables such as students' self-confidence and self-efficacy (Hoogerheide et al., [<reflink idref="bib19" id="ref21">19</reflink>]; Menon, [<reflink idref="bib28" id="ref22">28</reflink>]; Panigrahi et al., [<reflink idref="bib33" id="ref23">33</reflink>]). Using example-based instructional strategies has proven to be highly effective when it comes to teaching problem-solving within programming courses (Menon, [<reflink idref="bib28" id="ref24">28</reflink>]). This pedagogical approach aligns seamlessly with previous research in the field of programming course instruction, which emphasizes the value of structured problem-solving methods (Jiang, [<reflink idref="bib23" id="ref25">23</reflink>]; Tan et al., [<reflink idref="bib43" id="ref26">43</reflink>]; Zhang et al., [<reflink idref="bib50" id="ref27">50</reflink>]; Zhang et al., [<reflink idref="bib49" id="ref28">49</reflink>]).</p> <p>One additional benefit of these examples is that they are often framed as student work (Loehr et al., [<reflink idref="bib26" id="ref29">26</reflink>]) and can introduce forms of peer learning (e.g., "Here's Juan's strategy"). Too often in education, learning is viewed as a one-way process where instructors possess and disseminate the knowledge required for academic success. However, instructional strategies that incorporate opportunities for students to learn from each other can also be beneficial (Aksop & Özdemir, [<reflink idref="bib1" id="ref30">1</reflink>]; Jackson & Bruegmann, [<reflink idref="bib21" id="ref31">21</reflink>]; Merrill & Gilbert, [<reflink idref="bib31" id="ref32">31</reflink>]). For example, prior research has established that the success or failure of classmates directly impacts peer performance (Hoxby, [<reflink idref="bib20" id="ref33">20</reflink>]), and peer modeling can be more beneficial for self-efficacy and learning than teacher modeling (Schunk & Hanson, [<reflink idref="bib39" id="ref34">39</reflink>]). Further, experienced programming instructors emphasize the benefits of collaborative learning in the coding education process, highlighting that students achieve the best outcomes when they engage with their peers to facilitate their learning (Zhang et al., [<reflink idref="bib49" id="ref35">49</reflink>]). Thus, using examples of student work—whether the students are fictitious peers or authentic classmates—may be a useful strategy not only for demonstrating specific solutions to problems, but also for setting expectations. That is, these peer examples can communicate what model students can do or should be able to do.</p> <p>One difficult decision point is choosing which peer examples to use and whether they showcase basic or advanced work, especially when establishing an appropriate level of teacher expectancy class-wide among students of varying abilities. There are some potential advantages to displaying example student work at the basic level, which includes strategies or skills that are not too complex and are likely attainable for all students in the class. If a task is perceived as too challenging, student engagement may suffer (Clark, [<reflink idref="bib11" id="ref36">11</reflink>]). For example, research by Patall et al. ([<reflink idref="bib34" id="ref37">34</reflink>]) tracked daily study diaries of hundreds of students and found a decrease in perceived competence on days when coursework was more challenging. Using basic peer examples may benefit a wide range of students by helping them perceive the task as manageable, helping them avoid disappointment, and by building confidence in their capacity to succeed (Hoogerheide et al., [<reflink idref="bib18" id="ref38">18</reflink>]; Menon, [<reflink idref="bib28" id="ref39">28</reflink>]). In other words, this approach may help set everyone's expectations for success and avoid overwhelming students with expectations beyond their capabilities.</p> <p>However, there are also some potential advantages to displaying example student work at the advanced level, which includes complex strategies or skills that go beyond some students' current capacities. Providing challenging examples can help avoid underestimating students, which can reduce opportunities for growth and result in an underwhelming experience for top-performing students (Bergold & Steinmayer, [<reflink idref="bib6" id="ref40">6</reflink>]). In fact, overestimating student capacity—and therefore setting really high expectations—may promote rigor and positively impact performance. For example, a longitudinal study by Szumski and Karwoski ([<reflink idref="bib42" id="ref41">42</reflink>]) found that higher expectations of entire classes were positively correlated with improved individual math performance levels. Providing examples of advanced peer work may also help students see what the next level of achievement looks like and therefore give them more specific, concrete learning goals. Ultimately "the specific choice and treatment of examples are critical as they may shape students' understandings by facilitating or hindering learning" (Zaslavsky, [<reflink idref="bib48" id="ref42">48</reflink>], p. 252).</p> <hd id="AN0178415865-4">Current study</hd> <p>The goal of the current study was to provide novel, experimental evidence on how teachers' expectations affect college students' task performance and perceptions in an authentic online class. We focused on teacher expectations communicated indirectly via examples of student work in a controlled experiment by manipulating the complexity of peer examples (basic versus advanced) used in an exploratory SQL coding activity. We were particularly interested in how these varying levels of peer examples influenced the quality and complexity of student performance, as well as students' perceptions of their performance, task difficulty, frustration, interest, and motivation to continue learning SQL.</p> <p>The exploratory coding activity used in this study included 100,000 invoices contained in an SQL data set (Siggard et al., [<reflink idref="bib40" id="ref43">40</reflink>]). Students were tasked to explore the invoices by deriving SQL questions and coding solutions to summarize the data in different ways. Before starting the exploratory activity, we manipulated teacher expectations by randomly assigning students to view peer examples that were either basic (setting low, manageable expectations for success) or advanced (setting high expectations for challenge). This manipulation represents a novel approach to implementing teacher expectancy by using peer examples as an implicit method to convey expectations: The first group encountered basic coding expectations using peer coding examples to build confidence across varying levels of student proficiencies, and the second group encountered challenging coding expectations using advanced peer coding examples to establish high teacher expectancy. This method takes advantage of the underutilized but powerful peer-to-peer learning that can occur in classrooms. The findings have the potential to inform educators regarding the appropriate level (basic or advanced) of peer example to present to students in order to convey teacher expectations and facilitate student success.</p> <p>After the condition manipulation, we obtained two measures of student performance on the exploratory task (i.e., the quality of their code and the complexity of their code), as well as five measures of their perceptions related to the task (i.e., perceived task difficulty, frustration, performance, interest, and motivation). We expected that students who viewed the advanced peer examples (and therefore had high, challenging expectations) would have increased perceptions of task difficulty and frustration. This would confirm the manipulation and demonstrate that students in the advanced condition expected and experienced a more difficult task. However, given the competing theoretical accounts from the literature, our hypotheses regarding the effects of condition on the remaining measures were exploratory. On the one hand, students who viewed the basic peer examples may see the task as more manageable and feel empowered to succeed, in which case they may have increased performance, perceptions of performance, interest and motivation. On the other hand, students who viewed the advanced peer examples may see the challenging expectations as motivating and feel inspired to attempt complex coding, in which case they may have increased performance, perceptions of performance, interest, and motivation.</p> <hd id="AN0178415865-5">Method</hd> <p></p> <hd id="AN0178415865-6">Participants</hd> <p>Participants included 91 undergraduate students from a large Western university who participated in the research. The university had a total enrollment of 54,852 in 2021, and the student population was 83% white, 6% Hispanic/Latino, with the remainder being American Indian, Alaska Native, Asian, African American, and Positive Islanders, or two or more races. The study sample comprised 34 female individuals, 54 male individuals, 1 individual identifying as gender variant/non-conforming, and 2 nonresponses. Within the sample, 3% of participants fell within the age range of 18 to 19 years, 35% were between 20 and 21 years old, 38% were between 22 and 23 years old, and 24% were above the age of 23. 82% of the participants were majoring in data analytics and information systems. The remaining participants majored in other areas, including accounting, math, statistics, or general business. The majority of students were juniors (30%) or seniors (66%), but the sample also included one sophomore and two graduate students. All students were enrolled in an online advanced database management/SQL course, and data collection occurred over three semesters (i.e. Fall 2021, Spring 2022, Fall 2022). The research assignment (an exploration coding activity) was embedded into the online course using the same procedures across all three semesters, and students earned ten extra credit points for participating in the study. Prior to completing the exploration activity, all students in the course accessed a Qualtrics survey that presented the informed consent document. Thus, all students were informed about the nature of the research prior to participation and provided explicit written consent to participate. Students who opted out (i.e., did not consent) could complete a different assignment to earn the ten extra credit points. Approximately 72% of students enrolled in the advanced database courses opted in to participate in the study (91 out of 127 total students across the three semesters).</p> <hd id="AN0178415865-7">Design</hd> <p>This experiment had a randomized between-subjects design. Each consenting student was randomly assigned to one of two groups for this activity: basic peer examples (<emph>n</emph> = 45) versus advanced peer examples (<emph>n</emph> = 46).</p> <hd id="AN0178415865-8">Materials</hd> <p>Materials for the study were included in an online module in the Canvas course site. The materials were organized into five sections that the students accessed in sequence: (<reflink idref="bib1" id="ref44">1</reflink>) a demographic questionnaire, (<reflink idref="bib2" id="ref45">2</reflink>) an initial reading activity (<reflink idref="bib3" id="ref46">3</reflink>), exposure to peer coding examples, (<reflink idref="bib4" id="ref47">4</reflink>) the primary exploratory activity, and (<reflink idref="bib5" id="ref48">5</reflink>) a post-activity questionnaire on student perceptions. The condition manipulation occurred within the peer coding examples.</p> <hd id="AN0178415865-9">Demographic questionnaire</hd> <p>The first section included a pre-activity questionnaire using Qualtrics that focused on demographic information. It assessed self-reported gender, age, major in college, and level in college. For each question, participants selected from a set of three to five options.</p> <hd id="AN0178415865-10">Reading activity</hd> <p>The second section included an initial reading activity. Students were assigned to read a brief article that illustrated how SQL was used to explore trends in data. Specifically, students read an article titled, "Beer and Diapers: The Impossible Correlation" by Swoyer ([<reflink idref="bib41" id="ref49">41</reflink>]). The article generally noted that a positive correlation was found between beer and diaper sales at a specific store location, but indicated that the findings were not generalizable or necessarily actionable (Swoyer, [<reflink idref="bib41" id="ref50">41</reflink>]). Students had to write several sentences that summarized the article and submit this as an assignment on Canvas. The goal of the reading was to help students think about how SQL can identify trends, anomalies, errors, and potential fraud in data. Also, given that this activity occurred prior to the condition manipulation, it allowed us to use student responses to this assignment to verify the two conditions were balanced in prior performance.</p> <hd id="AN0178415865-11">Peer coding examples</hd> <p>The third section included exposure to examples of student work. Participants were instructed to access a dataset that contained 100,000 invoices and to <emph>run sample code written by former students</emph>. Participants were provided with three example sets of codes. Their assignment was to run the provided code and to "<emph>do your best to follow the logic of the code and what the former student is attempting to accomplish</emph>". Students had to submit screen captures of the output generated by the code along with their explanation of what was happening and what coding techniques were being used in each of the three examples. The participants were randomly assigned to one of two conditions for this activity to indirectly manipulate the teacher's expectations for the exploratory activity: a set of three basic peer examples or a set of three advanced peer examples. Figure 1 displays some of the examples.</p> <p>Graph: Fig. 1 Example peer codes by condition</p> <hd id="AN0178415865-12">Primary exploratory activity</hd> <p>The fourth section included the main exploration activity. Students were once again instructed to access the dataset that contained 100,000 invoices (see Fig. 2). Students were encouraged to be creative as they used SQL code to summarize the data and attempt to locate anomalies, trends, or unique findings. They were specifically told that the CIO is interested in learning whether the primary customer is male or female, learning who are the best customers, and learning what months produce the most invoices. In addition, participants were encouraged to make up company rules (e.g., all invoices are due 60 days from the purchase date) that need to be investigated and to <emph>use the coding examples from the previous step</emph> to develop their own creative and powerful ideas. Finally, a related customer table was included if students wanted to connect data to customer information using CustomerID as the primary/foreign key link. Participants submitted their exploratory coding questions, coding solutions, and screen captures, as well as a summary paragraph that detailed what they found out about the invoices. This assignment represented our primary outcome measure of student performance.</p> <p>Graph: Fig. 2 Screenshot of the invoices dataset</p> <hd id="AN0178415865-13">Questionnaire on student perceptions</hd> <p>The fifth and final section included a post-activity questionnaire with five questions using Qualtrics. The questions all had Likert scale responses with five options and were adapted from Robinson ([<reflink idref="bib36" id="ref51">36</reflink>]) to assess perceptions of task difficulty, frustration, perceived performance, interest, and motivation. The questions were anchored in the following ways: task difficulty (I thought this task was very easy/I thought this task was very hard), frustration (I felt very relaxed during this task/I felt very frustrated during this task), performance (I performed very poorly on this activity/I performed very well on this activity), interest (This activity was very uninteresting/This activity was very interesting), and motivation (I have very low interest in doing more tasks like this/I have very high interest in doing more tasks like this).</p> <hd id="AN0178415865-14">Procedure</hd> <p>Students accessed the module called "SQL Exploration Activity" in their Canvas course site, just as they would do for any routine assignment in the course. The instructions made it clear that the activity was worth 35 points toward their course grade, with the potential to earn 10 extra credit points if they agreed to participate in the research study and complete brief pre- and post-activity questionnaires. To begin the module, students linked to the informed consent document in Qualtrics, which gave them the explicit choice to opt-in or opt-out of the research study. Then, students completed the five sections within the module in sequence. These sections were generally self-paced, but timing recommendations were provided to guide the completion of the module. Specifically, students completed the demographic questionnaire (2 min), the initial reading assignment (15 min), the peer examples (15 min), the primary exploratory activity (2 h), and the student perceptions questionnaire (5 min). Students who opted out of the research study were allowed to skip the demographic questionnaire as well as the student perceptions questionnaire and could access a different assignment to earn the additional 10 extra credit points (to ensure students did not feel coerced into participation).</p> <p>The data were collected via the Canvas assignment submissions and via Qualtrics. A faculty colleague housed the consent form and questionnaires in a separate Qualtrics account, allowing for a double-blind study. That is, even though the course instructor had access to the assignment submissions on Canvas, the instructor did not know which students had consented to be in the study because the responses to the consent form and questionnaires were in Qualtrics. Near the end of the semester, the faculty colleague sent a list of students who participated in the study so they could be granted the 10 extra credit points toward their final course grade.</p> <p>This procedure was implemented across three consecutive semesters of this online advanced database management course. After the three semesters had concluded, a graduate student researcher assigned a random identifying number to each participant and used this number to identify their submissions to each portion of the module. This process made it possible for researchers to score student submissions without knowing the participant's treatment group. Once the scoring was complete, the treatment group variable was added to the dataset.</p> <hd id="AN0178415865-15">Coding</hd> <p>Our analytic goal was to test the effects of condition on two metrics of performance during the primary exploratory activity and five subjective perception variables. The perception variables (i.e., task difficulty, frustration, performance, interest, and motivation) were objectively scored on scales from one to five based on the participants' selection. To measure performance, we scored students' coding questions, coding solutions, and screen captures during the primary exploratory activity in terms of <emph>quality</emph> and in terms of <emph>complexity</emph>.</p> <p>Coding <emph>quality</emph> scores ranged from 1 to 3 and were subjectively assigned based on three criteria taken from the text used in the class, "T-SQL Fundamentals" by Ben-Gan ([<reflink idref="bib5" id="ref52">5</reflink>]). Specifically, the quality score was based on (<reflink idref="bib1" id="ref53">1</reflink>) code functionality (i.e., code executes and functions as expected), (<reflink idref="bib2" id="ref54">2</reflink>) code structure (i.e. follows Ben-Gan's structure guidelines), and (<reflink idref="bib3" id="ref55">3</reflink>) code completeness (i.e., using correct aliases, ending coding statement with a semicolon, and correct formatting). Coding <emph>complexity</emph> scores ranged from 1 to 3 and were subjectively assigned based on the coding commands present in students' solutions. Commands that students encountered frequently in their introductory and advanced SQL courses were considered less complex than the commands that students encountered rarely. Table 1 includes sample coding commands at different complexity classifications and Table 2 includes an example of student work at each coding complexity level.</p> <p>Given the subjective nature of the scoring, we established the reliability of the scoring process by having two expert raters—one representing academics and the other industry—collaboratively create the initial grading rubrics, work through sample responses to help ensure consistency, and discuss any issues. Each reviewer independently evaluated twenty-one student samples blind to the other reviewer's scores, and inter-rater agreement was high. Specifically, results from a bivariate correlation using Kendall's tau-b indicated a correlation coefficient of <emph>r</emph><subs><emph>t =</emph></subs> 0.857, <emph>p</emph> ≤ 0.05 for scoring coding quality, and a correlation coefficient of <emph>r</emph><subs><emph>t =</emph></subs> 0.779, <emph>p</emph> ≤ 0.05 for scoring coding complexity. With acceptable levels of inter-rater reliability determined, each reviewer then scored approximately half of the remaining sample data.</p> <p>Table 1 Sample coding complexity classifications</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Level 1: below expectations</p></th><th align="left"><p>Level 2: meets expectations</p></th><th align="left"><p>Level 3: exceeds expectations</p></th></tr></thead><tbody><tr><td align="left"><p>Aggregate functions</p><p>DISTINCT</p><p>HAVING</p><p>TOP</p><p>DATEDIFF()</p><p>FORMAT</p><p>GROUP BY</p><p>LIKE</p><p>MONTH</p><p>BETWEEN</p><p>IN/NOT IN</p><p>JOINS</p></td><td align="left"><p>VIEWS</p><p>MOD Function</p><p>ROUND Function</p><p>SQL Statistics</p><p>DATENAME</p><p>LEFT/RIGHT Functions</p><p>SUBQUERY</p><p>CASE Expressions</p><p>DATE FORMAT/ADD/AVG</p><p>CONCAT</p></td><td align="left"><p>Window functions</p><p>CTE</p><p>Stored Procedure</p><p>Trigger</p><p>Derived Table</p><p>EXISTS</p><p>Variables</p><p>Correlated Subquery</p><p>Set Operators</p><p>Advanced Stats Beyond 3330</p></td></tr></tbody></table> </ephtml> </p> <p>Table 2 Student example of each complexity classification</p> <p> <ephtml> <table frame="hsides" rules="groups"><tbody><tr><td align="left"><p>Level 1: below</p><p>Question: On average, which gender has higher invoice amounts?</p><p>SQL Concept:</p><p>Aggregate Function</p></td><td align="left"><p><inline-graphic href="MediaObjects/11251_2023_9658_Figa_HTML.gif" /></p></td></tr><tr><td align="left"><p>Level 2: meets</p><p>Question: Which month produced the most invoices?</p><p>SQL Concept:</p><p>DATENAME</p></td><td align="left"><p><inline-graphic href="MediaObjects/11251_2023_9658_Figb_HTML.gif" /></p></td></tr><tr><td align="left"><p>Level 3: exceeds</p><p>Question: What is the median amount from the invoices?</p><p>SQL Concept:</p><p>Derived Table</p></td><td align="left"><p><inline-graphic href="MediaObjects/11251_2023_9658_Figc_HTML.gif" /></p></td></tr></tbody></table> </ephtml> </p> <p>The experts also scored students' responses to the initial reading assignment, which required the submission of a brief summary and reflection. This assignment did not represent a primary outcome measure, but it occurred prior to the condition manipulation and allowed us to check that random assignment worked well and that students in both conditions were well-matched in terms of prior performance. The quality score for this assignment ranged from 1 to 3 and was primarily based on students accurately summarizing the article with sufficient detail. Each reviewer independently evaluated twenty-one student samples and inter-rater agreement was high. Specifically, results from a bivariate correlation using Kendall's tau-b indicated a correlation coefficient of <emph>r</emph><subs><emph>t =</emph></subs> 0.945, p ≤ 0.05. With acceptable levels of inter-rater reliability determined, each reviewer then scored approximately half of the remaining sample data.</p> <hd id="AN0178415865-16">Results</hd> <p>We analyzed the collected data in a series of steps. We first ran several preliminary analyses to verify that the assignment of students to groups was random, and also that there were no systematic differences between each phase of data collection (i.e., across the three semesters). Then, we reported the descriptive statistics related to the collected variables. Finally, we used a series of statistical tests for our hypotheses about the effects of condition on students' coding performance during the exploratory activity and on students' perceptions.</p> <hd id="AN0178415865-17">Preliminary analyses</hd> <p>We assessed whether random assignment produced approximately equivalent groups by examining their summaries of the initial reading activity, which were submitted prior to any condition manipulation. An independent t-test on their quality scores (out of 3) revealed no significant difference in the quality of reading report submissions between the 45 participants in the basic example group (<emph>M</emph> = 2.80, <emph>SD</emph> 0.41) and the 46 participants in the advanced example group (<emph>M</emph> = 2.83, <emph>SD</emph> 0.38), <emph>t</emph>(<reflink idref="bib89" id="ref56">89</reflink>) = 0.316, <emph>p</emph> =.753. We next confirmed that characteristics of the students were equivalent across the two groups using Chi-Square tests. Specifically, we found that there were no statistically significant group differences with respect to gender, <emph>X</emph><sups><emph>2</emph></sups>(<reflink idref="bib2" id="ref57">2</reflink>) = 1.445, <emph>p</emph> =.485, year in school (i.e., sophomore, junior, senior), <emph>X</emph><sups><emph>2</emph></sups>(<reflink idref="bib3" id="ref58">3</reflink>) = 1.361, <emph>p</emph> =.715, major, <emph>X</emph><sups><emph>2</emph></sups>(<reflink idref="bib4" id="ref59">4</reflink>) = 2.351, <emph>p</emph> =.672, and age group, <emph>X</emph><sups><emph>2</emph></sups>(<reflink idref="bib3" id="ref60">3</reflink>) = 4.682, <emph>p</emph> =.197.</p> <p>We also assessed whether the students enrolled across the three different semesters were approximately equivalent. We compared their quality scores (out of 3) on the initial reading activity and found no significant differences between first semester students (<emph>M</emph> = 2.81, <emph>SD</emph> 0.40) and second semester students (<emph>M</emph> = 2.81, <emph>SD</emph> 0.40), <emph>t</emph>(<reflink idref="bib56" id="ref61">56</reflink>) = 0.046, <emph>p</emph> =.964, between second semester students and third semester students (<emph>M</emph> = 2.82, <emph>SD</emph> 0.39), <emph>t</emph>(<reflink idref="bib57" id="ref62">57</reflink>) = − 0.101, <emph>p</emph> =.920, or between first semester students and third semester students, <emph>t</emph>(<reflink idref="bib63" id="ref63">63</reflink>) = − 0.058, <emph>p</emph> =.954.</p> <hd id="AN0178415865-18">Descriptive statistics</hd> <p>Before testing our hypotheses, we calculated descriptive statistics for the seven outcome variables and computed the bivariate correlations (Table 3). We examined our data for outliers and multicollinearity. We reviewed the correlations between the dependent variables, and none were above 0.700, indicating that multicollinearity between our sets of dependent variables was not an issue. A check of outliers was performed by examining the maximum Mahalanobis distance between our sets of dependent variables, and no outliers were found.</p> <p>Table 3 Descriptive statistics and bivariate correlations for the primary outcome variables</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Variable</p></th><th align="left"><p><italic>M</italic></p></th><th align="left"><p><italic>SD</italic></p></th><th align="left"><p>1</p></th><th align="left"><p>2</p></th><th align="left"><p>3</p></th><th align="left"><p>4</p></th><th align="left"><p>5</p></th><th align="left"><p>6</p></th><th align="left"><p>7</p></th></tr></thead><tbody><tr><td align="left"><p>1. Quality</p></td><td char="." align="char"><p>2.14</p></td><td char="." align="char"><p>0.461</p></td><td align="left"><p>–</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>2. Complexity</p></td><td char="." align="char"><p>2.02</p></td><td char="." align="char"><p>0.802</p></td><td align="left"><p>0.142</p></td><td align="left"><p> –</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>3. Difficulty</p></td><td char="." align="char"><p>3.35</p></td><td char="." align="char"><p>0.748</p></td><td align="left"><p>0.005</p></td><td align="left"><p>0.211</p></td><td align="left"><p>--</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>4. Frustration</p></td><td char="." align="char"><p>2.93</p></td><td char="." align="char"><p>0.865</p></td><td align="left"><p> − 0.009</p></td><td align="left"><p>0.096</p></td><td align="left"><p>0.481<sup>**</sup></p></td><td align="left"><p> –</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>5. Performance</p></td><td char="." align="char"><p>3.62</p></td><td char="." align="char"><p>0.806</p></td><td align="left"><p>0.080</p></td><td align="left"><p>0.109</p></td><td align="left"><p>− 0.276<sup>*</sup></p></td><td align="left"><p>− 0.371<sup>**</sup></p></td><td align="left"><p> –</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>6. Interest</p></td><td char="." align="char"><p>4.22</p></td><td char="." align="char"><p>0.668</p></td><td align="left"><p>0.162</p></td><td align="left"><p> − 0.005</p></td><td align="left"><p> − 0.099</p></td><td align="left"><p> − 0.140</p></td><td align="left"><p>0.358<sup>**</sup></p></td><td align="left"><p> –</p></td><td align="left" /></tr><tr><td align="left"><p>7. Motivation</p></td><td char="." align="char"><p>3.91</p></td><td char="." align="char"><p>0.814</p></td><td align="left"><p>0.148</p></td><td align="left"><p> − 0.059</p></td><td align="left"><p> − 0.170</p></td><td align="left"><p>− 0.301<sup>**</sup></p></td><td align="left"><p>0.279<sup>*</sup></p></td><td align="left"><p>0.492<sup>**</sup></p></td><td align="left"><p> –</p></td></tr></tbody></table> </ephtml> </p> <p>Quality and complexity represent students' performance scores on the primary exploratory activity that range from 1 to 3. Difficulty, frustration, performance, interest, and motivation represent students' self-reported perception scores that range from 1 to 5</p> <hd id="AN0178415865-19">Manipulation check</hd> <p>We verified that the manipulation was effective. We expected that students who viewed the advanced peer examples would have increased perceptions of task difficulty and frustration relative to students who viewed basic peer examples. We used a one-way multivariate analysis of variance (MANOVA) with perception of difficulty and frustration entered as the dependent variables and condition entered as the between-subject factor. Seventeen students did not complete the post-activity questionnaire and so these analyses are based on the 74 students with relevant data (n = 40 in the basic examples condition, n = 34 in the advanced examples condition). Using Pillai's Trace values as a caution against any violations of assumptions, the model revealed a significant main effect of condition, <emph>F</emph>(<reflink idref="bib2" id="ref64">2</reflink>,<reflink idref="bib71" id="ref65">71</reflink>) = 3.127, <emph>p</emph> =.0499, η<subs>p</subs><sups>2</sups> = 0.081. Further analysis showed that both perception of task difficulty, <emph>F</emph>(<reflink idref="bib1" id="ref66">1</reflink>,<reflink idref="bib72" id="ref67">72</reflink>) = 5.109, <emph>p</emph> =.027, η<subs>p</subs><sups>2</sups> = 0.066, and frustration, <emph>F</emph>(<reflink idref="bib1" id="ref68">1</reflink>,<reflink idref="bib72" id="ref69">72</reflink>) = 4.030, <emph>p</emph> =.048, η<subs>p</subs><sups>2</sups> = 0.053, were significantly different between the two groups. Students in the advanced example condition had higher perception of task difficulty and frustration than students in the basic example condition (see Table 4). Thus, we confirmed that the manipulation of the peer coding examples was effective.</p> <hd id="AN0178415865-20">Competing hypotheses</hd> <p>We put forth competing hypotheses regarding whether students' performance (coding quality and complexity) and perceptions (of performance, interest, and motivation) would be higher in the basic example condition or the advanced example condition. In fact, we might find that students in one condition score higher for some of these factors, but lower in other factors. To accommodate these potential differences, we tested each variable individually using a series of independent samples t-tests. Analyses on the performance variables were based on the full sample of 91 students and analyses on the perception variables were based on the sample of 74 students who completed the post-activity questionnaire. Our tests revealed a significant difference in code complexity as students in the advanced example condition provided more complex code than students in the basic example condition, <emph>t</emph>(<reflink idref="bib89" id="ref70">89</reflink>) = 2.996, <emph>p</emph> =.004, <emph>d</emph> = 0.628. There were descriptive differences favoring the advanced example condition on code quality, but not a significant difference, <emph>t</emph>(<reflink idref="bib89" id="ref71">89</reflink>) = 1.571, <emph>p</emph> =.120, <emph>d</emph> = 0.329. There were also no significant condition differences in terms of students' perceptions of performance, <emph>t</emph>(<reflink idref="bib72" id="ref72">72</reflink>) = − 1.499, <emph>p</emph> =.138, <emph>d</emph> = − 0.350, students' interest, <emph>t</emph>(<reflink idref="bib72" id="ref73">72</reflink>) = 0.225, <emph>p</emph> =.823, <emph>d</emph> = 0.053, or student's motivation, <emph>t</emph>(<reflink idref="bib72" id="ref74">72</reflink>) = − 0.509, <emph>p</emph> =.612, <emph>d</emph> = − 0.119. See Table 4 for means and standard deviations by condition.</p> <p>Table 4 Outcome scores as a function of condition</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Outcome variable</p></th><th align="left"><p>Basic example</p></th><th align="left"><p>Advanced example</p></th><th align="left"><p>Total</p></th></tr><tr><th align="left" /><th align="left"><p><italic>M</italic> (<italic>SD</italic>)</p></th><th align="left"><p><italic>M</italic> (<italic>SD</italic>)</p></th><th align="left"><p><italic>M</italic> (<italic>SD</italic>)</p></th></tr></thead><tbody><tr><td align="left"><p>Code quality</p></td><td align="left"><p>2.07 (0.39)</p></td><td align="left"><p>2.22 (0.51)</p></td><td align="left"><p>2.14 (0.46)</p></td></tr><tr><td align="left"><p>Code complexity</p></td><td align="left"><p>1.78 (0.79)</p></td><td align="left"><p>2.26 (0.74)</p></td><td align="left"><p>2.02 (0.80)</p></td></tr><tr><td align="left"><p>Perceived difficulty</p></td><td align="left"><p>3.18 (0.75)</p></td><td align="left"><p>3.56 (0.70)</p></td><td align="left"><p>3.35 (0.75)</p></td></tr><tr><td align="left"><p>Perceived frustration</p></td><td align="left"><p>2.75 (0.81)</p></td><td align="left"><p>3.15 (0.89)</p></td><td align="left"><p>2.93 (0.87)</p></td></tr><tr><td align="left"><p>Perceived performance</p></td><td align="left"><p>3.75 (0.84)</p></td><td align="left"><p>3.47 (0.75)</p></td><td align="left"><p>3.62 (0.81)</p></td></tr><tr><td align="left"><p>Perceived interest</p></td><td align="left"><p>4.20 (0.61)</p></td><td align="left"><p>4.24 (0.74)</p></td><td align="left"><p>4.22 (0.67)</p></td></tr><tr><td align="left"><p>Perceived motivation</p></td><td align="left"><p>3.95 (0.85)</p></td><td align="left"><p>3.85 (0.78)</p></td><td align="left"><p>3.91 (0.81)</p></td></tr></tbody></table> </ephtml> </p> <p>The coding scores are based on the full sample of 91 students. The perception scores are based on the 74 students who completed the post-activity questionnaire</p> <hd id="AN0178415865-21">Discussion</hd> <p>The current study provides evidence that differences in teachers' expectations—set via peer examples—can influence student performance in an authentic college class. We manipulated the instructor's implicitly stated expectations by having students review either basic examples of peer work or advanced examples of peer work prior to a data exploration assignment—thereby showcasing what a typical student could or should be able to do. The manipulation appeared to work as students who saw the advanced peer examples reported higher levels of task difficulty and frustration relative to students who saw the basic peer examples. Importantly, the manipulation also changed students' performance levels. We found that those who encountered challenging peer examples submitted significantly more complex coding solutions on the SQL exploratory activity with just as high coding quality as those who encountered basic peer examples. These results suggest that examples of peer work represent a viable method to communicate teacher expectations, and that setting high expectations can elevate undergraduate student performance—at least in the current sample and educational context. We discuss the implications of this work, as well as future directions.</p> <p>The current findings have several implications relevant to instructional science. First, the results contribute to prior research on using peer examples as an instructional tool. Most previous studies have focused on the use of worked examples to support problem-solving skills and to facilitate the transfer of knowledge to similar problems (e.g., Booth et al., [<reflink idref="bib8" id="ref75">8</reflink>]). For example, instructors might have students explain a peer's example or compare two peers' example solutions to better understand the relevant procedures and concepts before trying to solve a related problem on their own. Our results are consistent with these benefits, but extend this work and showcase a <emph>novel</emph> function of these examples—they can convey a teacher's expectations in an implicit way by demonstrating what a typical student can do on a given problem. Given that peer examples are often underutilized in technical fields like mathematics and information systems (Cusi & Olsher, [<reflink idref="bib12" id="ref76">12</reflink>]; Gardner et al., [<reflink idref="bib16" id="ref77">16</reflink>]; Merrill, [<reflink idref="bib29" id="ref78">29</reflink>]; Scheiter, [<reflink idref="bib38" id="ref79">38</reflink>]; Zaslavsky, [<reflink idref="bib48" id="ref80">48</reflink>]), these findings can be used to support the use of examples as a form of effective peer-to-peer interaction for solving real-world problems (Hoogerheide et al., [<reflink idref="bib18" id="ref81">18</reflink>]).</p> <p>Second, the results contribute to prior research on teacher expectancy effects (Jussim & Harber, [<reflink idref="bib24" id="ref82">24</reflink>]). Research has shown that teachers form and communicate expectations about their students' capacities for success and that these expectations can influence their students in unique ways (e.g., Fyfe and Brown, [<reflink idref="bib15" id="ref83">15</reflink>]; Friedrich et al., [<reflink idref="bib14" id="ref84">14</reflink>]). This study empirically supports peer examples as a method of implicitly communicating teacher expectations in a standardized way across a classroom, and suggests that setting challenging expectations is beneficial for enhancing students' performance on a class coding assignment (Szumski & Karwoski, [<reflink idref="bib42" id="ref85">42</reflink>]). There is some concern that setting challenging expectations can overwhelm students—perhaps lowering students' self-confidence or creating a negative cycle in which they feel they cannot attain success, put in less effort, and thus do not succeed (Merton, [<reflink idref="bib32" id="ref86">32</reflink>]). However, in the current context of an online advanced database management/SQL course, providing advanced, challenging examples proved fruitful for upper-level undergraduates' performance.</p> <p>We think there are several potential mechanisms that might help explain the benefits of the challenging expectations in this context. For example, the advanced examples condition may have created a desirable difficulty (Bjork & Bjork, [<reflink idref="bib7" id="ref87">7</reflink>])—an initial learning experience that was challenging but ultimately enhanced the deep learning and retention of the material. Students who initially studied the advanced peer examples may have found it more difficult to process the coding solutions and may have exerted more cognitive effort to understand and summarize them. This effort may have produced better storage strength of the material, and when faced with their own problem to code they were better able to produce desirable results (i.e. coding solutions of high quality and even higher complexity). This account is supported by the fact that students in the advanced examples group did in fact perceive the task to be more difficult and frustrating, even though they were ultimately more successful.</p> <p>Alternatively, it may have less to do with desirable difficulties and more to do with providing a task within the students' zone of proximal development (Vygotsky, [<reflink idref="bib46" id="ref88">46</reflink>]). The zone is essentially the difference between what a student can do on their own and what they can do with an adult or peer's guidance. Although independent learning is important, Vygotsky believed it was absolutely critical for people to interact with more knowledgeable others in order to develop and push the zone further. In the current study, the peer examples with basic work may have represented tasks students could already do on their own, whereas the advanced examples may have been square in the zone of proximal development and provided a task that was just beyond what students might have done on their own. In this case, the key may have been the specific level of "advanced" peer work that produced the benefits. Although ZPD is frequently contextualized with scaffolding and children, its application is far broader (Xi & Lantolf, [<reflink idref="bib47" id="ref89">47</reflink>]).</p> <p>Regardless of the mechanism, we believe the current findings have practical implications for instructional designers. Our study suggests that peer examples represent a viable instructional strategy to convey a teacher's implicitly stated class-wide expectations. This option may be particularly beneficial when designing online courses where other expectation strategies such as classroom grouping, reinforcement, or tone of voice are challenging to implement (Braun, [<reflink idref="bib9" id="ref90">9</reflink>]; Edwards & Taasoobshirazi, [<reflink idref="bib13" id="ref91">13</reflink>]). In fact, this option is promising for broader implementation given that it represents a relatively minor change to instructional practice that does not require a complete overhaul of the course structure, though future research is needed to test this in different educational settings. Our study also suggests that using advanced peer examples and <emph>setting challenging expectations</emph> can have beneficial outcomes. Given the diversity of skill levels among students, conveying expectations class-wide can be difficult. The current results suggest that appropriate but challenging peer examples that set high expectations can be designed by considering problem-solving strategies that learners have only rarely been exposed to, but represent the next step of rigor and relevance for that particular learning activity.</p> <p>Several limitations of the current study suggest directions for future research. For example, collecting data related to the exploratory activity involved participants enrolled in an online advanced database management course. As such, the number of participants was limited to a smaller class size requiring data collection over three semesters. Replicating a similar study with larger classes and samples would allow for more conditions to be examined than the two used in this study (i.e., basic and advanced). For example, a more robust research design might include a baseline control group (no peer examples) or more varied levels of complexity that would provide better insight into a potential tipping point or sweet spot between setting high, challenging expectations and any potential negative outcomes. Other research designs could contrast whether the examples are presented as peer work (e.g., here is another students' solution) or teacher work (e.g., here is an expert's solution) to better understand the role of the peer in this context.</p> <p>Additional studies should also consider testing implicit teacher expectations via peer examples with more heterogenous samples and across more varied contexts. For example, how would participants in an introductory database management course react to basic versus advanced peer examples in a similar exploratory activity? Or are there particular learner characteristics (e.g., self-efficacy, gender, age, race, or prior knowledge) that might predict who learns best from advanced peer examples relative to basic examples? Future research could gather these additional characteristics at the individual student level and help explore the generalizability of the current conclusions to other situations and other samples.</p> <p>Finally, future studies should continue to use a variety of outcome measures that represent both cognitive and motivational aspects of learning. Here, it seemed promising that the advanced peer examples produced higher code complexity without compromising code quality, interest, or motivation. However, different ways of communicating expectations may have even stronger benefits that produce higher levels of performance and higher motivation to continue.</p> <p>Despite these limitations, the current study provides novel empirical evidence using rigorous experimental methods in an authentic online class. The results suggest that establishing high standards can be an integral part of the teaching and learning process, and they are consistent with the notion that "people tend to live up, or down, to our expectations" (Kouzes & Posner, [<reflink idref="bib25" id="ref92">25</reflink>], p. 62). We found that upper-level college students in a data management course who encountered more advanced peer examples rose to the challenge set by the instructor and lived up to the expectations by submitting work that was more complex without compromising quality. Students, at least in this context, were inspired, and not overwhelmed.</p> <hd id="AN0178415865-22">Declarations</hd> <p></p> <hd id="AN0178415865-23">Conflict of interest</hd> <p>The authors declare that there are no conflict of interest regarding the publication of this research paper. They have no financial or personal relationships with any individuals or organizations that could potentially bias their work or affect the interpretation of the findings. This declaration includes any sources of funding or support received for the research, as well as any affiliations or competing interests that could be perceived as influencing the outcome of the study.</p> <hd id="AN0178415865-24">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0178415865-25"> <title> References </title> <blist> <bibl id="bib1" idref="ref30" type="bt">1</bibl> <bibtext> Aksop A, Özdemir D. 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  Data: Are You Inspired or Overwhelmed? The Benefits of Teachers Setting Challenging Expectations
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  Data: <searchLink fieldCode="AR" term="%22Robert+J%2E+Mills%22">Robert J. Mills</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-5936-8737">0000-0002-5936-8737</externalLink>)<br /><searchLink fieldCode="AR" term="%22Emily+R%2E+Fyfe%22">Emily R. Fyfe</searchLink><br /><searchLink fieldCode="AR" term="%22Tanya+Beaulieu%22">Tanya Beaulieu</searchLink><br /><searchLink fieldCode="AR" term="%22Maddy+Mills%22">Maddy Mills</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Instructional+Science%3A+An+International+Journal+of+the+Learning+Sciences%22"><i>Instructional Science: An International Journal of the Learning Sciences</i></searchLink>. 2024 52(4):693-709.
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: 17
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  Data: 2024
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Label: Descriptors
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  Data: <searchLink fieldCode="DE" term="%22Teacher+Expectations+of+Students%22">Teacher Expectations of Students</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+Achievement%22">Academic Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Communication+%28Thought+Transfer%29%22">Communication (Thought Transfer)</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Management%22">Information Management</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Analysis%22">Data Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Assignments%22">Assignments</searchLink><br /><searchLink fieldCode="DE" term="%22Coding%22">Coding</searchLink><br /><searchLink fieldCode="DE" term="%22Authentic+Learning%22">Authentic Learning</searchLink>
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  Data: 10.1007/s11251-023-09658-0
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  Data: 0020-4277<br />1573-1952
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  Data: Teachers form expectations that can influence their students' performance, and there are a variety of ways these expectations can be communicated. In the current study, we tested a novel method for communicating expectations via examples of student work--examples that contain basic, entry-level work and communicate low, but manageable expectations or examples that contain complex, advanced-level work and communicate high and challenging expectations. Across three semesters, 91 college students in a data management course completed a class assignment that involved exploratory coding activities. Prior to the assignment, students were randomly assigned to view basic or advanced examples of student work. Students assigned to the advanced-examples condition reported higher perceptions of task difficulty and frustration, but they also exhibited higher levels of performance in terms of the complexity of their own work. Results suggest that setting challenging expectations can create a desirable difficulty that ultimately benefits students' performance in an authentic learning environment.
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  Data: 2024
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        StartPage: 693
    Subjects:
      – SubjectFull: Teacher Expectations of Students
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      – SubjectFull: Academic Achievement
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      – SubjectFull: Teaching Methods
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      – SubjectFull: Authentic Learning
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