Relationships between the perceived value of instructional techniques and academic motivation.
Key Words: academic motivation, instructional techniques, course websites
In order to enhance the value of a college education, it is important to identify the relationship between students' receptiveness to various instructional techniques and individual differences in academic motivation. Teachers and scholars have long been attentive to how the choice of specific teaching techniques (such as lectures, assignments, and experiential exercises) may impact classroom climate and performance (McKeachie, 1974). However, relationships between specific instructional techniques and student motivation have received less attention (Hammer, 2005). In addition, in recent years, many university faculty have developed comprehensive course websites that provide access to lecture notes, grades, review sheets, example exam questions, links, and so on. With the advent of the internet and increased use of course websites, it is becoming increasingly important to determine the impact of website usage on student motivation and learning (Cox & Rogers, 2005; Roblyer & Knezek, 2003). Indeed, Robinson (2004) reports that as students become more connected to the electronic world, faculty members will find themselves compelled to incorporate technology in the classroom. Similarly, Miller, Martineau, and Clark (2000) conclude that educators have very little choice but to incorporate technology as widespread student computer usage expands and social, structural, and resource barriers inevitably diminish. Similarly, Grasha (2000) encourages faculty to develop a conceptual framework for incorporating technology in the classroom, as it is important for faculty to explore the fit between technology and their philosophy of teaching and learning. Thus, it is important to develop an understanding of how the mix of instructional technologies employed is related to students' motivation, learning, and performance. We designed the current research to examine the relationship between the perceived value of different teaching techniques and college students' academic motivation. Specifically, we focused on the relationships that course website usage, traditional lecture, and active learning techniques have with distinct aspects of academic motivation.
We propose that instructional techniques do not impact all students equally, and that students with different profiles of academic motivation will prefer and value different instructional techniques. For years, scholars across numerous disciplines have analyzed various instructional methods and sought to identify those that are most effective. This prior research and theory suggests that pedagogical choices can have a significant impact on student learning, eagerness to consider new information, and ability to apply information to new situations. First, research has shown that students learn in many different ways and one method is not enough to reach all students (McKeachie, 1974). For example, research on individual differences in learning styles (e.g., Biggs, 1993) suggests that students tend to adopt either a surface, deep, or achieving learning style. Similarly, research on multiple types of intelligence (e.g., Steinberg, Torff, & Grigorenko, 1998) suggests learning is enhanced when learning strategies are well matched to students' specific abilities. Second, research has demonstrated that students differ in their relative levels of intrinsic and extrinsic motivation, such that some students are relatively disengaged, whereas others display intellectual curiosity and a hunger for learning (Deci & Ryan, 1985). These differences in motivation could lead students to be either willing or unwilling learners depending on how they are taught, and students are likely to differ in the instructional techniques that they find most engaging (Sutton & Wheatley, 2003). Third, research on integrating technology into the classroom demonstrates that it can improve student interest by making learning more of an active process (Grasha, 2000). Indeed, research examining teachers' and students' perceptions regarding the integration of technology into instructional techniques suggests that the benefits may outweigh the costs and barriers involved (Miller, Martineau, & Clark, 2000).
Thus, a variety of perspectives converge to suggest that there are individual differences in students' academic motivation, that different students may value different instructional techniques, and that website and technology usage should be considered in the mix of instructional techniques that may influence academic motivation. Stated differently, the strength of the relationship between instructional techniques and academic motivation will depend on what aspect of motivation is examined, such that students' academic motivation should be enhanced when there is a good fit or match between the specific aspects of a student's academic motivation and the instructional environment.
In the current research, we endeavor to take the vital first step of documenting the relationship between instructional techniques and academic motivation, but do not aim to disentangle the causal direction of this relationship. We acknowledge that these influences are likely to be reciprocal in nature, and may develop recursive properties over time due to the interactive process of teaching and learning. Thus, specific instructional techniques could certainly influence motivation as a function of student preferences for educational environments. Yet, at the same time, student differences in types and levels of academic motivation could also drive their preferences for specific instructional techniques.
Web-Based Instructional Techniques.
A number of researchers have focused on the ability to supplement traditional lecture-based courses with computer technology. Professors now have access to computers at home and at work, answer emails, post course material on the web, and research resources with which to link, in addition to preparing lectures. Similarly, college students are now very comfortable using computers, emailing assignments, responding to online quizzes, participating in electronic chats, exploring linked information, and so on (e.g., Forsyth & Archer, 1997; Gardner, 2002). For example, Cox and Rogers (2005), Keane (2002), and Leuhold (1999) suggest that computer and internet access provides teachers with the ability to go beyond traditional offerings and supplement them with on-line lectures, tests, simulations and games, on-line group discussion, audio and video links, problem-solving activities, and providing real-time feedback on post-lecture quizzes. Driver (2002) found that MB A students perceived an online course as superior to both a traditional or televised course, and felt that there was greater social interaction and a greater sense of community than in a traditional course. Similarly, Beets and Lobingier (2001) found that student learning and attendance was highest when the presentation mode matched the students' preference. Most educators (Rother, 2004; Iding, Crosby, and Speital, 2002; Witt, 2003; Peluchette & Rust, 2005) believe that incorporating technology into teaching is likely to add significant value to their teaching, enhance teaching effectiveness and increase students' academic performance.
However, there are some pros and cons specific to web-based teaching techniques and academic motivation. A1-Bataineh and Brooks (2003) suggest that although technology is moving in the direction of virtual learning via the internet it is important not to lose the interpersonal contact between teachers and students and amongst students themselves. Teachers will need to adopt instructional strategies that maintain a fine balance between the use of technology and other modes of teaching that address the social and psychological needs of students (e.g., Becker & Ravitz, 1999; Hung, 2001; Mioduser, Nachmias, Lahav, & Oren, 2000).
Instructional Techniques and Learning. Prior research and theory suggests that pedagogical choices can have a significant impact on various aspects of student learning, such as eagerness to consider new information and ability to apply information to new situations. Thus, students' preferences for various instructional techniques (which could be influenced by their academic motivation) need to be taken into account when considering student learning. Indeed, research has shown that students learn in many different ways and one method is not enough to reach all students (e.g., Biggs, 1993; Sternberg et al. 1998). Indeed, a number of scholars have emphasized the importance of deploying a variety of teaching strategies and incorporating more active techniques into one's repertoire. For example, Astin (1984) suggests that faculty members who are able to increase student involvement, both physically and psychologically, are more likely to enhance student learning and motivation. Similarly, Diaz-Lefebvre (2004) cautions against teaching methods that encourage rote memorization as they are likely to lead to low motivation, poor performance, and an inability to apply course material to real life.
Further highlighting the importance of different student approaches to learning, Bransford and his colleagues (Bransford et al., 1982; Bransford, Sherwood, Vye, & Rieser, 1986; Schwartz & Bransford, 1998) have emphasized the active role students can take in their own learning. They highlight the role teachers can play in enhancing students' metacognition or awareness of themselves as learners. Teachers who help students understand the strategies they use and how these can be improved are more likely to engage students more effectively in the classroom. Teachers using instructional techniques that encourage students to reflect on their own learning, provide them with feedback, give them a chance to review material, and encourage them to take responsibility for their own learning tend to increase learning. Bransford et al. (1998) recommend a problem based approach to teaching and learning that connects classroom activities to underlying concepts and builds scaffolds by deepening understanding, providing contrasting scenarios, and using narratives that end in open questions. In relation to the current research, students' relative involvement with or attraction to specific instructional techniques (as influenced by their academic motivation) could well impact their actual learning.
Similar conclusions about the importance of using a variety of teaching techniques to enhance student learning have also emerged from a number of studies done in various academic disciplines. For example, Reeves & Francis (2002) found that a problem-based learning technique, though not as structurally efficient as a lecture, tended to make pharmacy students more inquisitive, and also increased their retention of material, their ability to apply material to new contexts, and their ability to solve problems. In examining how family therapy is taught, Maynard (1996) reported that a student-centered experiential method involving video material, presentations, writing, and asking questions encouraged learning and critical thinking. Chanson (2004) found that including fieldwork as a method of teaching hydraulic engineering topics to Australian undergraduates enhanced both the value of the fieldwork to students and its relevance to employers. In the healthcare field, Vaughn, Gonzalez del Rey, and Baker (2001) have advocated an instructional technique called microburst learning that combines role-plays, experiential activities, group discussions, and simulations into one session, with each segment presented in a short burst. Vaughn et al. report that microburst learning helps in maintaining students' attention span, accommodating various learning styles, and motivating students.
Instructional Techniques and Academic Motivation. The choice of specific instructional techniques can also have a high degree of relevance to students' academic motivation. For example, McKeachie's (1987; Pintrich, McKeachie, & Lin, 1987) research on teaching effectiveness and teacher training suggests that it is possible to motivate students to be excited about learning by creating active learning experiences. Students who are able to see a connection between concepts from lecture and real life situations are more likely to become curious and eager to know more. Further, McKeachie, Lin, Moffett, & Daugherty (1978) compared four teaching styles (Expert, Authority, Facilitator, Person) in relation to students' thinking, motivation, and attitudes towards learning. They found that students whose instructors used a student-centered style (Facilitator, Person) were more motivated to participate in class and were more likely to register for additional psychology courses relative to students whose teachers used the Expert or Authority styles. Similarly, Hammer (2005) explains how showing concern for students, having a positive attitude, and communicating concern for their learning and success can be very motivating (see also Sutton & Wheatley, 2003).
Dunn & Dunn (1979) describe a body of work demonstrating the importance of matching teaching methods to various student characteristics to enhance academic motivation and achievement. They emphasize the importance of training teachers in the use of a wide variety of instructional techniques so as to reach a larger number of students. For example, a teacher who typically uses the lecture method can learn to use the small group discussion or case analysis techniques. Their research suggests that students who may not be initially motivated can become motivated when taught in a way that complements how they learn.
It is especially important to consider student differences in levels of intrinsic and extrinsic motivation when selecting instructional techniques. Deci and Ryan (1985) identify three major motivational drives: intrinsic motivation, extrinsic motivation and amotivation. Intrinsically motivated students are likely to be energized, persistent, and genuinely interested in learning. Intrinsic motivation can be maintained and also increased if students are given experiences in the classroom that make them feel competent and capable of accomplishing tasks. Teachers who use instructional techniques that foster intrinsic motivation are more likely to be effective. Vallerand & Bissonnette (1992) describe how intrinsic motivation can be increased when students engage in activities that are self-determined and that they find to be creative, interesting, and associated with positive emotions. On the other hand, students can become demotivated when faced with situations or experiences that make them feel incompetent, not in control, threatened, and pressured. For example, Bransford et al. (1982) compared successful and unsuccessful students and noted that successful students tended to spontaneously take on an active role in learning by elaborating on given information and relating it to previous experiences.
Similarly, Grasha (1994) provides a conceptual framework of various teaching styles (expert, formal authority, personal model, facilitator, and delegator) and emphasizes the importance of varying these styles to match students' knowledge and motivation in order to promote student interest and learning. He explains that the expert/formal authority style is most suitable when students are not familiar with the content of the course and the teacher is willing to manage all the classroom activities. On the other hand, when students are knowledgeable, and motivated to take initiative and responsibility for their learning, a teacher could use a combination of expert/facilitating/delegating styles. Such matching of teaching strategy to students' preferences is more likely to result in student engagement in the classroom. Similarly, Miller (1991) underscores the importance of taking into account student differences in personality, motivation, and learning styles and adjusting teaching techniques so as to appeal to a broader range of students.
The Current Research
Prior research has highlighted a number of potential positive and negative attributes of course website usage and has also identified a number of potential linkages between specific instructional techniques and various student outcomes. However, very little research has directly examined how preferences for various instructional strategies are related with individual differences in students' academic motivation. The current research was designed to directly examine the linkages between the perceived value of various instructional techniques and individual differences in academic motivation. Given the dearth of prior research on this issue, our research was exploratory in nature. Thus, we did not make specific predictions about the likely relationships between each of the 16 AMI subscales and each of the three instructional techniques. In stead, we adopted the general reasoning that students would be most receptive to those teaching techniques that overlap with the elements of academic life that they find most motivating, and that the resulting pattern of relationships would be amenable to a motivational fit explanation.
We collected data from a sample of 172 undergraduates at a large, public, Midwestern university who participated to receive extra credit points or to complete a course requirement. The current research was part of a larger study examining academic motivation in relation to a variety of personality and situational factors. A previous article reports the results for relationships between academic motivation and the Big Five personality dimensions, and also presents a principal components factor analysis of the sixteen AMI subscales (for details see Komarraju & Karau, 2005). The current paper focuses on relationships between the AMI and specific instructional strategies. The instructional strategies data and analyses are new and previously unpublished. All participants were enrolled in courses in psychology or business. Nearly all of the courses in both departments maintained websites that provided details about the syllabus, contact information and hours for the instructor and teaching assistants, lecture notes, and grade information. In addition, many of the websites included additional resources such as sample tests, review sheets, course-related assignments (e.g., statistical, spreadsheet, or case assignments), feedback about items missed on exams, links to publisher-provided support information (e.g., quizzes, internet links), or links to full-text articles. Very few course websites incorporated online discussion.
To measure perceptions of different instructional techniques, we created scales assessing the perceived value of course websites (6 items), active learning (4 items), and traditional lectures (2 items, see Table 1). We also included additional items to assess computer access, computer usage, frequency and type of course website usage, and perceptions of overall course website usefulness (see Table 2). Students were also asked to complete the Academic Motivations Inventory (Moen & Doyle, 1977) that consists of 90 items and includes 16 dimensions of academic motivation. The sixteen dimensions are thinking, persisting, achieving, facilitating anxiety, debilitating anxiety, grades orientation, economic orientation, desire for self-improvement, demanding, influencing, competing, approval, affiliating, withdrawing, dislike school, and discouraged about school (see Table 3 for Alpha levels).
We first examined descriptive information to obtain a profile of students' computer access and usage (see Table 2). The data revealed that a large majority of students had readily available computer access and used the computer to contact the instructor as well as fellow students, and to access course materials. These results also showed that the vast majority of courses maintained websites, and students used websites most commonly to get course information, check grades, access sample tests, and download notes. Finally, 93% of students reported finding websites useful, and 91% said they would like to see more classes make information available via websites.
We then conducted correlation analyses to examine relationships between the 16AMI subscales and the three instructional strategies. As shown in Table 3, a complex pattern of statistically significant correlations of modest magnitude (ranging from .17 to .32) emerged, suggesting that students' preferences for specific instructional strategies are indeed related to their profiles of academic motivation. Specifically, the perceived value of traditional lectures was positively related with thinking, persisting, achieving, facilitating anxiety, grades orientation, desire for improvement, and affiliation motives, and negatively related with disliking school. The perceived value of in-class activities was positively related with thinking, desire for improvement, influencing, approval, and affiliation motives, and negatively related with withdrawal. Finally, the perceived value of course websites was positively related with thinking, grades orientation, desire for improvement, influencing, approval, and affiliation.
In order to clarify these relationships in a more parsimonious manner, we examined the relationship between the instructional strategies and three key components of the AMI that we had identified in our previous article (Komarraju & Karau, 2005). Specifically, we had previously found that principal components factor analysis of the sixteen subscales of the AMI produces three factors (with eigenvalues of 4.94, 2.79, and 1.71) explaining 59% of the variance. Engagement (alpha = .79) includes six subscales from the original instrument: thinking, facilitating anxiety, desire for self-improvement, influencing, approval, and affiliating. Avoidance (alpha = .75) includes six subscales: debilitating anxiety, demanding, economic orientation, withdrawing, disliking school, and discouraged about school. Achievement (alpha = .80) includes four subscales: persisting, achieving, grades orientation, and competing. Thus, in the current analyses, we examined correlations between instructional strategies and these three broader dimensions of academic motivation. As shown in Table 4, engagement was positively related with the perceived value of all three instructional techniques. Achievement motivation was positively correlated with the perceived value of traditional lectures and course websites, but had no relationship with experiential exercises. Finally, avoidance was not significantly related with any of the three instructional techniques.
Finally, to further understand how the mix of these three instructional techniques influenced engagement, avoidance, and achievement motivation, we conducted a series of stepwise, forward regression analyses. For each analysis, gender was included in the first step of the analysis as a possible control variable, but was not significant in any of the analyses. As shown in Table 5, engagement and achievement motivation were each influenced by a different pattern of significant predictors. Specifically, when looking at engagement motivation, 19% of the variance was explained by all three instructional techniques (Course Websites, Active Learning, and Traditional Lecture), F (3,129) = 10.08, p<.001, adjusted [R.sup.2] = .17. Course websites emerged as the strongest predictor, explaining 10% of the variance in engagement motivation. For achievement motivation, only one instructional technique (Traditional Lecture) was a significant predictor explaining 7 % of the variance, F (1,133) = 9.79, p<.01, adjusted [R.sup.2] = .06.
The results of our study clearly suggest that various teaching techniques are significantly associated with distinct aspects of students' academic motivation. This is an important finding, considering that the relative match between learning preferences and academic environments has been found to play an important role in student learning (e.g., Bransford et al. 1982; Grasha, 2000, Pintrich, McKeachie, & Lin, 1987). By understanding relationships between instructional techniques and various elements of academic motivation, teachers may be able to enhance student involvement, retention, and application. Indeed, our results clearly suggest that students enter the classroom with different types of academic motivation and drives that make them differentially receptive to specific instructional techniques.
Our factor analysis revealed three profiles of academic motivation, each associated with unique learning preferences: engagement, avoidance, and achievement motivation. Specifically, engagement was positively associated with all three instructional techniques. The perceived value of websites, active learning, and traditional lectures all explained significant variance in engagement motivation, with perceived value of websites explaining the greatest variability. From a teaching standpoint, engaged students appear to be ideal and seem to be motivated by a variety of strategies. Thus, in relation to the AMI subscales, students who enjoy thinking about what they are learning and desire self-improvement are likely to respond to a larger variety of teaching strategies.
In contrast, avoidance was not significantly associated with any of the instructional techniques. Thus, avoidant students pose a special challenge to effective teaching. They are more likely to experience anxiety and worry about not doing well, to dislike school, to feel discouraged about their studies, and to be withdrawn or disengaged from the learning process. These are the students who seem least likely to respond to any of the teacher's instructional techniques, whether it is the traditional lecture, in-class activities or web-based learning.
Interestingly, achievement was positively correlated with the perceived value of both traditional lectures and course websites, but had no relationship with the perceived value of active learning. However, in our regression analysis, the perceived value of traditional lectures was the only instructional technique that emerged as a significant predictor of achievement motivation, with the perceived value of course websites no longer a significant predictor when controlling for the other two instructional techniques. This suggests that achievement-oriented students--who tend to be more persistent, competitive, achievement focused, and grades oriented--seem to prefer learning via traditional lectures. Perhaps they prefer a more structured and predictable learning environment and are not comfortable with learning opportunities involving role-plays, case studies, and small group discussions.
One important implication of these results is that the inclusion of course websites is most likely to appeal to students who are high in engagement .Another important implication is that the lecture method does appeal to students who are motivated to learn (i.e., students who are either engaged or achievement motivated). This is heartening to note, as many courses are still based primarily on the lecture method due to its structural efficiency. Teachers who are aware of these differences could combine various teaching strategies to connect with a wider range of students and be more sensitive to the advantages of trying new instructional techniques. These results support existing research that highlights the importance of a good fit between a student's motivation, personality, learning style and how he or she is taught (e.g., Biggs, 1993; McKeachie et al. 1978).
Our study also sheds further light on how computers are actually being used in university courses. Consistent with other recent research (e.g., Mioduser et al. 2000), we found that students have widely available access to computers and use them frequently to contact the instructor and other students, to access the internet, to check grades, and to download course materials and lecture notes. It seems that the typical course website, at least at the university studied, presents basic course information and serves as a resource for getting course materials as needed. The use of more interactive technologies, such as online discussion groups is far less common. Nevertheless, nearly all of our student sample found the websites useful and would like to see more classes with websites.
We should also acknowledge that our study had some limitations that might be addressed in future research. Most important, the AMI had low to moderate levels of internal consistency on several subscales (see Table 3). Hence, conclusions on those specific subscales should be considered tentative. Although the AMI is useful for examining many aspects of academic motivation, the Academic Motivation Scale (AMS; Vallerand, Pelletier, Blais, Briere, Senecal, & Vallieres, 1992) provides an alternative measure with good psychometric properties. Also, although we have established a number of significant relationships between instructional techniques and academic motivation, we note that a number of these relationships were fairly small in magnitude, particularly when examined at the level of the 16 AMI dimensions. Our measures of instructional techniques, created specifically for this study, were also fairly basic in nature. More comprehensive measures could be developed to address additional aspects of instructional techniques.
It will also be important for future research to examine the relative contribution of academic motivation on student learning in light of other key variables such as cognitive ability, thinking styles, learning styles, cultural factors, and personality (e.g., Church & Katigbak, 1992; Schmeck & Geisler-Brenstein, 1991; Zhang, 2002). In particular, the work of Bransford and his colleagues (Bransfordet al., 1982, Bransford, Sherwood, Vye, & Rieser, 1986, Schwartz & Brantford, 1998) emphasizes the potential for teachers to enhance student learning through the use of problem-based learning and other techniques. We also acknowledge that our study cannot establish the causal direction of the relationships between instructional techniques and academic motivation. It might also be desirable for future research to examine a wider variety of students to allow stronger conclusions about generalizability. Nevertheless, we take the vital first step of documenting interesting linkages that vary across different types of academic motivation. Future research could seek to shed additional light on the nature of the complex and dynamic relationships between academic motivation and other components of teaching and learning.
In summary, our results suggest that students do differ in terms of their preferences for various instructional techniques and this appears to be related to their level and type of academic motivation in the classroom. Teachers could capitalize on this knowledge by seeking to match instructional techniques to specific types of students, although this may be difficult, especially in large classes. Alternatively, teachers could employ multiple techniques in order to ensure that all students are being reached, at least some of the time. Motivating students who typically do not respond to certain strategies (lectures, in-class activities, or use of web based teaching) and capturing their attention and interest probably is a teacher's greatest challenge. Our results suggest that, by attending to the relative match between instructional techniques and the patterns of academic motivation exhibited by specific students, teachers may be able to support and enhance student engagement and achievement.
Al-Bataineh, A. & Brooks, L. (2003). Challenges, advantages, and disadvantages of instructional technology in the community college classroom. Community College Journal of Research and Practice, 27, 473-484.
Astin, A. W. (1984). Student involvement: A development theory for higher education. Journal of College Student Development, 40, 518-529.
Becker, H. J. & Ravitz, J. (1999). The influence of computer and interact use on teachers' pedagogical practices and perceptions. Journal of Research on Computing in Education, 31, 356-385.
Beets, D, & Lobingier, P. G. (2001). Pedagogical techniques: Student performance and preferences. Journal of Education for Business, 76, 231-235.
Biggs, J. B. (1993). What do inventories of students' learning processes really measure? A theoretical review and clarification. British Journal of Psychology, 63, 3-19.
Bransford, J.D., Sherwood, R., Vye, N., Rieser, J. (1986). Teaching thinking and problem solving research foundations. American Psychologist, 41, 1078-1089.
Bransford, J. D., Stein, B. S., Vye, N. J., Franks, J. J., Auble, P. M., Mezynski, K. J., & Perfetto, G. A. (1982). Differences in approaches to learning: An overview. Journal of Experimental Psychology: General, 111, 390-398.
Chanson, H. (2004). Enhancing students' motivation in the undergraduate teaching of hydraulic engineering: Role of field works. Journal of Professional Issues in Engineering Education & Practice, 130, 259-268.
Church, T.A., & Katigbak, M. S. (1992). The cultural context of academic motives. Journal of Cross Cultural Psychology, 23, 40-58.
Cox, J. R. & Rogers, J. W. (2005). Enter: The (Well-Designed) Lecture. The Teaching Professor, 19, 1-2.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum Press.
Diaz-Lefebvre, R. (2004). Multiple intelligences, learning for understanding, and creative assessment: Some pieces to the puzzle of learning. Teachers College Record, 106, 49-57.
Driver, M. (2002). Investigating the benefits of web-centric instruction for student learning - An exploratory study of an MBA course. Journal of Education for Business, March/April, 236-245.
Dunn, R. S. & Durra, K. J. (1979). Learning styles/Teaching styles: Should they ... can they ... be matched? Educational Leadership, 36, 238-244.
Forsyth, D. R. & Archer, C. R. (1997). Technologically assisted instruction and student mastery, motivation, and matriculation. Teaching of Psychology, 24, 207-213.
Gardner, E. (2002). It's new, it's improved, it's time. University Business, December, 39-42.
Grasha, A. F. (1994). A matter of style: The teacher as expert, formal authority, personal model, facilitator, delegator. College Teaching, 42(4), 12-19.
Grasha, A. F. (2000). Integrating teaching styles and learning styles with instructional technology. College Teaching, 48, 2-11.
Hammer, E. Y. (2005). From the laboratory to the classroom and back: The science of interpersonal relationships informs teaching. Journal of Social and Clinical Psychology, 24, 3-10.
Hung, D. (2001). Theories of learning and computer-mediated instructional technologies. Education Media International, 38, 281-287.
Iding, M., Crosby, M.E. & Speitel, T. (2002). Teachers and technology: Beliefs and practices. International Journal of Instructional Media, 29, 153-170.
Keane, J. (2002). Where educators stand in the technology revolution. T.H.E. Journal, 30, 38-40.
Komarraju, M., & Karau, S. J. (2005). The relationship between the Big Five personality traits and academic motivation. Personality and Individual Differences, 39, 557-567.
Leuthold, J. H. (1999). Is Computer-Based Learning Right for Everyone? Annual Proceedings of the Hawaii International Conference on System Sciences.
Maynard, P. E. (1996). Teaching family therapy: Do something different. American Journal of Family Therapy, 24, 195-205.
McKeachie, W. J. (1974). Instructional Psychology. Annual Review of Psychology, 25, 161-194.
McKeachie, W. J. (1987). Teaching ,Teaching Teaching, and Research on Teaching. Teaching of Psychology, 14, 135-139.
McKeachie, W. J., Lin, Y., Moffett, M. M., & Daugherty, M. (1978). Effective teaching: Facilitative vs. directive style. Teaching of Psychology, 5, 193-194.
Miller, A. (1991). Personality types, learning styles, and educational goals. Educational Psychology, 11, 217-239.
Miller, J. W., Martineau, L. P. & Clark, R. C. (2000). Technology infusion and higher education: Changing teaching and learning. Innovative Higher Education, 24, 227-241.
Mioduser, D., Nachmias, R., Lahav, O., & Oren, A. (2000). Web-based learning environments: Current pedagogical and technological state. Journal of Research on Computing in Education, 33, 55-77.
Moen, R., & Doyle, K. O., Jr. (1977). Construction and development of the academic motivations inventory (AMI). Educational and Psychological Measurement, 37, 509-512.
Peluchette, J. V. & Rust, K. A. (2005). Technology use in the classroom: Preferences of management faculty members. Journal of Education for Business, March/April, 200-205.
Pintrich, P. R., McKeachie, W. J., & Lin, Y. (1987). Teaching a course in learning to learn. Teaching of Psychology, 2, 81-86.
Reeves, J. A. & Francis, S. (2002). Comparison between two methods of teaching hospital pharmacists about adverse drug reactions: Problem-based learning versus a didactic lecture. Pharmacy Education, 1, 25-35.
Robinson, P. (2004). Individualized training brings mainstream faculty on board. Distance Education, 8, 1-3.
Roblyer, M. D. & Knezek, G.A. (2003). New millennium research for educational technology: A call for a national research agenda. Journal of Research on Technology in Education, 36, 60-70.
Rother, C. (2004). Evaluating technology's role in the classroom. T.H.E. Journal, 32, 185-190.
Schmeck, R. & Geisler-Brenstein, E. (1991). Serf-concept and learning: The Revised-Inventory of Learning Processes. Educational Psychology, 11, 343-363.
Schwartz, D. L., & Bransford, J.D. (1998). A time for telling. Cognition and Instruction, 16, 475-522.
Steinberg, R. J., Torff, B., & Grigorenko, E. L. (1998). Teaching triarchically improves school achievement. Journal of Educational Psychology, 90, 374-384.
Sutton, R. E. & Wheatley, K. F. (2003). Teachers' emotions and teaching: A review of the literature and directions for future research. Educational Psychology Review, 15, 327-358.
Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briere, N. M., Senecal, C., & Vallieres, E. F. (1992). The Academic Motivation Scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52, 1003-1017.
Vallerand, R. J. & Bissonnette, R. (1992). Intrinsic, extrinsic, and amotivational styles as predictors of behavior: A prospective study. Journal of Personality, 60, 599-620.
Vaughn, L., Gonzalez del Rey, J., & Baker, R. (2001). Microburst teaching and learning. Medical Teacher, 23, 39-43.
Witt, P. L. (2003). Enhancing classroom courses with internet technology: Are course web sites worth the trouble? Community College Journal of Research and Practice, 27, 429-438.
Zhang, L. (2002). Measuring thinking styles in addition to personality traits? Personality and Individual Differences, 33, 445-458.
Meera Komarraju, Department of Psychology, Southern Illinois University at Carbondale. Steven J. Karau, Department of Management, Southern Illinois University at Carbondale.
Correspondence should be addressed to Meera Komarraju, Department of Psychology, Southern Illinois University, Carbondale, IL 62901-6502. email: email@example.com.
Table 1 Items Used To Measure Three Instructional Techniques: Website Usage, Active Learning, and Traditional Lecture Instructional Techniques and items No. of items Alpha Course Websites 6 .87 Increased my learning Increased my interest in the course Increased my involvement in the course Increased my satisfaction with the course Increased my information about the course Increased my motivation in the course Active Learning 5 .77 In class debates In-class cases In-class group discussion In-class role-playing exercises In-class group presentations Traditional 2 .69 Lectures Lectures with examples Table 2 Student Profile of Computer and Course Website Usage Factor Percentage Computer Access Own a computer 81% Have access to a computer at home 92% Use a computer on campus 79% Computer Usage Email Instructor 86% Email other students or friends 98% Go online at least once per day 84% Course Website Do courses have websites? 98% Check course information on the web? 94% Check grades online? 94% Access sample tests? 92% Download lecture notes? 69% Use website for discussion groups? 19% Overall view of course websites Find website useful 93% Would like to see more classes with websites 91% Table 3 Correlations Between AMI Subscales and Three Instructional Techniques Website Active Traditional Motive Alpha Usage Learning Lecture Thinking .76 .23 ** .22 * .23 ** Persisting .68 .12 -.02 .21 Achieving .88 .11 .16 .32 ** Facilitating anxiety .54 .15 .15 .18 * Debilitating anxiety .72 .12 .01 .14 Grades orientation .78 .24 ** .06 .23 ** Economic orientation .55 .10 -.02 -.09 Self-improvement .68 .23 ** .23 ** .20 * Demanding .53 .01 .03 .03 Influencing .63 .23 ** .32 ** .16 Competing .66 .09 .00 .01 Approval .82 .23 ** .17 * .16 Affiliating .45 .30 ** .17 * .30 ** Withdrawing .69 -.07 -.32 ** -.09 Disliking school .77 -.12 -.05 -.20 * Discouraged about school .71 -.03 .04 -.03 * p<.05 ** p<.01 Table 4 Correlations Between Three subscales of Academic Motivation and Three Instructional Academic Motivation Subscales Perceived value of Instructional Techniques Engagement Avoidance Achievement Course Websites .32 ** .01 .21 * Active Learning .28 ** -.07 .08 Traditional Lecture .27 ** -.04 .26 ** Note. N ranges from 132 to 139. * p<. p05 ** p<01 Table 5 Forward Multiple Regression Results With Instructional Techniques Regressed on Academic Motivation Factors Change in Factor Step Predictor Beta [R.sup.2] [R.sup.2] Engagement 1 Course Websites .26 ** .10 .10 2 Active Learning .21 ** .16 .05 Traditional 3 Lecture .19 * .19 .03 Traditional Achievement 1 Lecture .26 ** .07 .07 * p <.05 ** p <.01
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|Author:||Komarraju, Meera; Karau, Steven J.|
|Publication:||Journal of Instructional Psychology|
|Date:||Mar 1, 2008|
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