Technology + Pedagogy Guide: Bringing Method to the Madness

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Shaunna Smith, Ed.D.

Dr. Smith is an Assistant Professor of Educational Technology in the Department of Curriculum and Instruction at Texas State University. Her research interests focus on technology integration strategies within K–12 and post-secondary learning environments. As a former secondary art teacher, she is particularly interested in exploring how the hands-on use of design-based technologies (e.g., digital fabrication, 3D modeling and printing, computer programming, and robotics) can impact multidisciplinary learning that transcends traditional content contexts. At her mobile makerspace, The MAKE Lab, she is currently researching how recurring experiences with these design-based technologies impact self-efficacy and positive attitudes toward failure (e.g., grit and persistence in the face of obstacles; reconceptualization of failure as a paradigm for creative learning) with teachers and K–12 students.

It is easy for educators to get lost in the madness of the overwhelming number of instructional options and technology tools available today. If we aren’t careful, we can easily become the Alice who falls down the rabbit hole into a technology wonderland, quickly becoming enamored and sidetracked with every tool as they get “curiouser and curiouser,” discouraged by the Mad Hatter who suggests a new approach to everything we’ve been doing, or frightened by the Queen of Hearts who suggests that change is unwelcome. As educators, our time is precious, and we need to be mindful of our productivity; however, we also need to learn how to leverage our own individualized knowledge and easily accessible technology in order to enhance our instruction and student learning potential.

Although published before digital technology was commonplace in education, Shulman’s (1987) theories of “pedagogical reasoning” and “pedagogical content knowledge” remind us that a teacher must remain focused on their instructional intent and interconnectedness to subject matter. Mishra and Koehler’s (2006) Technological Pedagogical Content Knowledge (TPACK) draws upon Shulman’s theories by adding considerations of technological knowledge and its connections to pedagogical knowledge and content knowledge, thus creating a context for discussing the new complexities of considerations that teachers must contend with. Content connections are found relatively easily with textbook companion websites and the like; however, making a meaningful connection between technology and pedagogy can be a little bit more complicated.

Designed as a helpful decision-making tool, the Technology + Pedagogy Guide can aid educators in instructional planning of activities that integrate instructionally appropriate technology tools to support a variety of learning contexts (the complete Technology + Pedagogy guide is available at: https://tinyurl.com/techology-pedagogy). Table 1 shows how it organizes commonly accessible and free technology tools into categories related to their essential characteristics (tool affordances) and ability to align with Bloom’s Revised Taxonomy (Krathwohl, 2002) to support student-centered learning objectives:

TECHNOLOGY CATEGORIES ESSENTIAL CHARACTERISTICS

(Tool Affordances and Instructional Purpose)

CONNECTIONS TO BLOOM’S TAXONOMY LEVELS

(Learning Objectives)

Acquisition & Investigation Tools Technology tools that allow users to capture and collect information. Remembering
Presentation & Remixing Tools Technology tools that allow users to demonstrate understanding of concepts through original expression or through remixing (editing existing content by putting a new ‘spin’ on it). Understanding

Applying

Discussion & Reflection Tools Technology tools that allow users to communicate ideas and experiences with self and/or others. Analyzing

Evaluating

Creation & Editing Tools Technology tools that allow users to generate original artifacts to demonstrate personally meaningful knowledge. Creating


Acquisition and Investigation tools
assist learners in capturing and collecting information, which is appropriate for instructional goals that align with the lower-level Bloom’s Revised Taxonomy levels of Remembering. This category of tool is perfect for the beginning stages of research projects when you want students to capture and collect information related to a topic. Leveraging digital functionality, students can use these technology tools to complete individual assignments or to co-construct as a collaborative group, with the added benefit of even being able to communicate across time and space — beyond the four walls of your classroom.

Presentation and Remixing tools assist learners in demonstrating their understanding of concepts through altering existing content and application of concepts through presenting information to others. This category is appropriate for instructional goals that align with the Bloom’s Revised Taxonomy levels of Understanding and Applying. This category of tool is perfect for brainstorming ideas and organizing concepts or presenting proposals to the class. Leveraging digital functionality, these tools can easily be worked on outside of class and can be shared with others through using URL links.

Discussion and Reflection tools assist learners in communicating ideas and experiences to themselves and/or others. This category is appropriate for instructional goals that align with the middle levels of Bloom’s Revised Taxonomy levels for Analyzing and Evaluating. This category of tool can be used to inspire diverse perspectives throughout an on-going learning module or project, as well as a culminating reflection to examine personal learning at the end of the semester. Leveraging digital functionality, these tools can easily take advantage of the ability to “comment” and “reply” to student posts as well as share URL links of creations to spark further dialogue.

Creation and Editing tools assist learners in generating original artifacts to demonstrate their own personally meaningful knowledge. This category is appropriate for instructional goals that align with the highest levels of Bloom’s Revised Taxonomy levels for Creating. This category of tool can be used to support smaller scale creative activities throughout a module or can be expanded to allow students to explore open-ended original artifact creation. Leveraging digital functionality, these tools can easily take advantage of the wide variety of free tools that can allow students to create a wide variety of media (i.e. photo editing, videography, 3D modeling, computer programming) but also easily share online with others.

Conclusion

Given the right level of support, even technology novices who are overwhelmed by the initial madness of this technology wonderland can transition into becoming confident and effective technology integrators who can select tools to amplify and transform their teaching. Through using the Technology + Pedagogy Guide, educators can focus on student-centered pedagogies by recognizing the categorical affordances and characteristics of the tools. In doing so, educators can develop a more richly constructed transference of knowledge by having an essential understanding of what qualities to look for in the ever-changing palette of technology tools in order to match pedagogical goals that will remain relevant as the technologies continue to evolve.

References

Krathwohl, D. R. (2002). A revision of bloom’s taxonomy: An overview. Theory into Practice, 41(4), 212.

Mishra, P., & Koehler, M. (2006). Technological pedagogical content knowledge: A framework for integrating technology in teacher knowledge. Teachers College Record, 108(6), 1017-1054.

Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57, 1-22.

Overcoming Mathematics and Testing Anxiety with Research-Based Strategies

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Theresa Hoang and Darolyn Flaggs

Theresa Hoang is a Ph.D. student in the Developmental Education program at Texas State University with a specialization in developmental math.  Previously, she earned her M.A. from the same program with a concentration in literacy.  She has taught learning frameworks at the college level and mathematics at the high school, and she has assisted in teaching developmental reading and developmental mathematics at Texas State University.  Her research interests include motivation of underprepared students in higher education and social psychological interventions.

Darolyn Flaggs is a Ph.D. student in the Developmental Education Program at Texas State University with a specialization in Developmental Mathematics. She received her B.S. in Mathematics at Texas Southern University and her M.Ed. in Mathematics Education at Texas State University. Her research interests include studying historically underrepresented student populations within the mathematics setting and exploring variables affecting student’s persistence to degree completion. Ms. Flaggs has taught undergraduate mathematics courses, been involved in the revision of the developmental mathematics scope and sequence, and lesson plans, and worked with FOCUS and SLAC at Texas State University. She is currently working under the research mentorship of Dr. Taylor Acee in the Department of Curriculum and Instruction.

How does mathematics and testing anxiety affect your students?  As doctoral students teaching developmental mathematics for the first time, we quickly realized the extent to which mathematics and testing anxiety was hurting our students’ academic outcomes.  During office hours, students often self-proclaimed to having anxiety about test-taking and about mathematics in general.  While not all students explicitly told us about their worries, it was sometimes intuitively clear that they struggled with mathematics and testing anxiety.  These common occurrences led us to explore deeper into what was causing students to have feelings of anxiety and what could we do as mathematics educators to help our students in these situations.

While searching through the literature, we found an incredible useful journal article that we would like to share with you entitled “Anxiety and Cognition” and written by Maloney, Sattizahn, and Beilock (2004).  In this article, Maloney et al. (2014) described how mathematics and testing anxiety affected the brain; anxiety can cause maladaptive physical responses and negative thoughts, which can take up prefrontal cortical resources and working memory that could otherwise be used for mathematics.  To combat these effects in the brain, Maloney et al. (2014) identified key strategies across a plethora of anxiety research.  These primary strategies included expressive writing (Park, Ramirez, & Beilock, 2014), arousal reappraisal (Jamieson, Mendes, Blackstock, & Schmader, 2010), stereotype threat reappraisal (Johns, Schmader, & Martens, 2005), and breathing exercises (Brunye, Mahoney, Giles, Rapp, Taylor, & Kanarek, 2013).  While in-depth information about each strategy can be found in Maloney et al.’s (2014) article, the following list will provide brief descriptions of how to implement each strategy:

  • Expressive Writing: Immediately before students take an exam, ask students to write about their feelings about the upcoming exam for 10 minutes. The goal of this activity is for students to express their negative thoughts and worries before the exam so that during the exam, students can use their working memory to think about their math problems instead of their anxieties.
  • Arousal Reappraisal: Students who perform well on tests regardless of their anxiety tend to look at stress-inducing situations as a challenge instead of a threat. So, when students begin to feel their heart rate increasing or their body sweating because of a stress-inducing situation, encourage students to interpret those signs of arousal as normal physiological responses to a challenge and that these signs can actually help with performance rather than hurt it.
  • Stereotype Threat Reappraisal: This strategy is useful for groups of people, such as women or students of color, who may experience stereotype threat, which is “the fear of acting in such a way that confirms a negative stereotype about a group to which one belongs” (Maloney et al., 2014, p. 408). Informing these students about the existence of stereotype threat and the possibility of anxiety arising from stereotype threat can help students assess why they feel anxious and perform better on exams.
  • Breathing Exercises: Encouraging students to engage in focused breathing exercises before exams, similar to the one found here, can increase student performance. By completing the breathing exercises before exams, students may be able to focus their attention better and free up cognitive resources to use during exams.

Over the past few decades, the role of developmental mathematics instructors have evolved; not only do instructors play a key role in facilitating the growth of student knowledge in mathematics, but effective instructors also address non-academic factors, such as motivation and anxiety, to further increase their students’ success.  By learning and practicing these research-based strategies proven to help students with mathematics and testing anxiety, instructors have the golden opportunity to positively impact student success.

Reference

Maloney, E. A., Sattizahn, J. R., & Beilock, S. L. (2014). Anxiety and cognition. WIREs Cognitive Science, 5(4), 403-411.

 

 

Transforming Instruction with Technology

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Nathalie Vega-Rhodes

Nathalie Vega-Rhodes is currently a professor of mathematics and the mathematics technology coordinator at Lone Star College – Kingwood. She specializes developmental education redesign and focuses on researching and create valuable resources for students and instructors. Prior to her time at Lone Star, Vega-Rhodes taught mathematics and college student success courses at other institutions around the Houston area. Vega-Rhodes earned a Bachelor of Arts degree in mathematics with a minor in geology from the University of Houston and a Master of Science degree in mathematics from the University of Houston-Clear Lake. In her spare time, she enjoys reading, traveling, and scuba diving.

Technology is advancing exponentially in our world; its use is growing in our classrooms whether we want it to or not.  Beetham and McGill (2012) observed that technology is “transforming what it means to work, study, research, express oneself, perhaps even to think.”   Bowen (2014) agrees and would further add that this growth has made course design and pedagogy more important than ever.  Given this current and irreversible trend, we must harness the benefits of this tool to enhance learning in the classroom.

As instructors, it’s incumbent upon us to leverage technology to engage students as well as organize our courses in a clear and concise manner.  Learning management systems (e.g. Moodle, Desire2Learn, Blackboard, etc.) at most institutions are a means by which instructors can manage learning and connect with students.  Clearly-named modules, checklists and release restrictions ensure access to relevant information and keep students on track.  Additional features such as Intelligent Agents allow instructors to define criteria for automated and personalized communication at critical points throughout the semester.

Other options for creating dynamic courses are college-supported software programs such as Softchalk or Webex.   For example, Softchalk can be used to create interactive lessons, while Webex can be used to meet with students virtually, thereby eliminating the age-old problem of providing timely feedback for students who are not present in a traditional face-to-face classroom.  Instructors and students can share screens to discuss concepts or work out examples, either one-on-one or in a group.  An added benefit of these software programs is that they can be integrated with most learning management systems, making for a seamless student experience.

While proper organization is unquestionably important, by itself it is insufficient.  One of the problems that instructors have traditionally faced is lack of available information, which means that instructors may not always know when to intervene or what interventions are necessary.  A valuable tool to solve these problems is the analysis capabilities in online homework systems. Easily accessible reports can be used to track progress and determine challenging concepts for individual students or the entire class.  This data can be used for evaluating current assignments or improving future courses.

In addition to online homework systems, an easy and convenient way to engage students is by harnessing the capabilities of pervasive smartphone or tablet apps.  A few favorites include Attendance (easy recording/reporting of student attendance), Show Me (easy video creation), Notability (note-taking), and Google Voice (texting/phone calls without sharing a personal phone number). Each of these apps have the potential to increase efficiency with everyday tasks.

In summary, these tools, when coupled with thoughtful implementation, can truly impact teaching and learning.  McLoughlin and Lee (2008) stated that “technological resources provide opportunities for a range of interactions, communicative exchanges, and sharing, but it is not possible to base an entire sequence of learning episodes based on tools.”  Indeed, I am able to do more and better for my students since the immediate feedback allows me to tailor specific solutions based on each student’s needs.  I look forward to increased productive interactions with my students using innovations, both present and future.

References

Bowen, J. A. (2014). The teaching naked cycle. Liberal Education100(2), 18-25.

Littlejohn, A., Beetham, H., & McGill, L. (2012).  Learning at the digital frontier: a review of digital literacies in theory and practice. Journal of Computer Assisted Learning, 28(6), 547-556. doi:10.1111/j.1365-2729.2011.00474.x

McLoughlin, C. & Lee, M. J. W. (2008). The three p’s of pedagogy for the networked society: Personalization, participation, and productivity. International Journal of Teaching and Learning in Higher Education, 20(1), 10-27.

Grading as Pedagogical Act: Three Methods for Assessing Writing That Work

 

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Lisa Hoeffner, Ph.D.

Lisa Hoeffner earned a Ph.D. in English with an emphasis in rhetoric from the University of Houston. She teaches English and Integrated Reading and Writing at McLennan Community College in Waco, Texas. She is the author of two developmental education textbooks, Common Places: Integrated Reading and Writing (McGraw-Hill, 2015) and Common Ground (McGraw-Hill, forthcoming) and speaks nationally on issues related to developmental education reform.

Anyone who has taught writing knows the dread that attends grading a stack of essays. Research suggests that grading can be a pedagogical act—an act that teaches students how to improve their writing—if practitioners take care to use effective assessment methods. Three methods are particularly commendable.

 1. Start the course with assessment. Starting with a focus on assessment helps students internalize writing standards and use them as benchmarks for their own writing (Defeyter & McPartlin, 2007). Supplying students with a rubric is not enough. One way to have students understand assessment criteria is to challenge students to verbalize the qualities of good writing. This active construction of criteria puts students in the role of participants rather than passive recipients of a rubric. Once students have articulated the criteria, they can create rubrics. Orsmond, Merry, & Reiling (2002) suggest that students can better understand the assessment process by using rubrics to score sample papers, assist in peer editing, and facilitate self-assessment.

 2. Provide effective feedback. The most effective feedback in terms of seeing growth in students’ writing skills is formative feedback (Frey & Fisher, 2013). Nonetheless, many instructors provide mainly summative feedback, such as comments on a final draft. Good feedback is also timely, understandable, personalized, positive, and capable of providing a pathway for improvement (Li & De Luca, 2014). Effective feedback can be given in any number of ways. For example, in class, instructors can offer over-the-shoulder suggestions to students engaged in writing; outside of class, students can receive brief, formative feedback by texting their proposed thesis statements to their instructors. Instead of making writing assessment one onerous, summative task that happens after the product is submitted, instructors should rethink feedback so that the bulk of it occurs during the writing process. Instructors might expect to see greater improvements by using formative micro-feedback more frequently.

 3. Finally, provide a way for students to map improvement. Grading is not a pedagogical act when graders edit their students’ papers. This is especially true for developmental writers, for these students can rarely articulate why an edit was made. Even if students can identify the reason for an edit, they do not necessarily acquire the skills they need for improvement. A more successful way to mark papers is to assess via an ongoing dialogue between student and instructor so as to facilitate improvement on future writing assignments (Rust, O’ Donovan, & Price, 2005). One way to do this is to identify two to three recurrent errors to master before the next writing assignment. Students and instructors jointly keep a writing progress log on which goals are recorded and monitored. For instance, a student may be prompted to master paragraph development and subject/verb agreement before submitting the next paper. After grading the next paper, progress is recorded on the log and goals are revised. This kind of carry-through provides accountability and allows students to map improvements in a measurable and quantitative way.

By using pedagogical grading methods, the time spent on assessment can become a valuable part of the teaching and learning process.

References

Defeyter, M. A., & McPartlin, P. L. (2007). Helping students understand essay marking criteria and feedback. Psychology Teaching Review, 13(1), 23-33.

Frey, N., & Fisher, D. (2013). A formative assessment system for writing improvement. English Journal, (1), 66.

Li, J., & De Luca, R. (2014). Review of assessment feedback. Studies in Higher Education, 39(2), 378-393.

Orsmond, P., Merry, S., & Reiling, K. (2002). The use of exemplars and formative feedback when using student derived marking criteria in peer and self-assessment. Assessment & Evaluation in Higher Education, 27(4), 309-23.

Rust, C., O’Donovan, B., & Price, M. (2005). A social constructivist assessment process model: How the research literature shows us this could be best practice. Assessment & Evaluation in Higher Education, 30(3), 231-240.

 

How to Contextualize Math Using Infographics

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Patricia Helmuth

Patricia Helmuth is an Adult Numeracy Consultant and Educator. She teaches two HSE classes, does one-on-one tutoring (in partnership with the Center for Workforce Development), and is a Professional Development Team Member for the Adult Program at Sullivan County BOCES, NY. In addition to working with students, she enjoys sharing her “numeracy adventures” at the regional, state, and national level by presenting at conferences and writing for adult education web-based resources. She currently serves as the newsletter editor for The Adult Numeracy Network.

In a traditional math classroom, where math topics may be taught in isolation, students watch the instructor model a procedure on the board and then students are expected to memorize, repeat, and practice the procedure. The trouble is, many students have difficulty connecting the procedure to real-life applications. This disconnect that students experience is evidenced in ABE/HSE classes, as well as on college campuses in developmental math classes. According to Models of Contextualization in Developmental and Adult Basic Education, “…students who want to be nurses, EMTs, firemen…. are stuck in a course that doesn’t work.” Conversely, when math is contextualized, students can develop conceptual understanding of the math.  “Research supports the fact that students understand math better when it is contextualized. It motivates and increases the students’ willingness to engage (Tabach & Friedlander, 2008) and provides concrete meaning to the math (Heid et all, 1995).” – (2015 Center for Energy Workforce Development)

In light of this research, and the implementation of the Common Core State Standards and the release of the Workforce Innovation and Opportunity Act, adult education instructors are being called upon to make changes in classroom practice that will adequately prepare students to pass new high-stakes exams and enter college and the workforce with marketable skills. How can adult educators do all this given the short amount of time that adults typically spend in class?

A great place to start is by using a variety of authentic infographics that connect to the social studies, science, or career readiness that you are already teaching. By using infographics, you are combining content knowledge, math skills, and analyzing and interpreting graphic information into one lesson! While infographics may be new to some of us in adult education, they are not new to our students. They see them all the time in the real world so it is imperative that they develop skills to decode them. Besides all that, they are fun! Students are drawn into a conversation when you display an infographic and simply ask:

  • What do you notice? What do you wonder?

Students at all ability levels can participate in a lesson that is introduced like this. Furthermore, when students share out their observations and questions it serves as a formative assessment and enables the instructor to connect what students already know with the whatever math concept the instructor has in mind to draw out of the infographic.

For specific lesson plans and ideas on how to do this, go to:

In the Adult Education classroom today, we need to do more than present our students with workbooks that include traditional examples of maps, charts, and graphs.  We need to use what our students see all around them every day: infographics.

References

Center for Energy Workforce Development (2015). Contextualized math for the energy industry. Retrieved from http://www.cewd.org/contextualized-math/

Education Development Center (EDC). (2012). Models of Contextualization in Developmental and Adult Basic Education. Retrieved from EDC website: http://bit.ly/1KAnllT

 

Program Improvement in Adult Education through Professionalization

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David Borden

David Borden, dborden@austincc.edu, currently works at Austin Community College as the Director of the Career Accelerator, a program dedicated to moving non-traditional students through career pathways–associate degree programs faster and with more supports. He holds a Master’s Degree from UNT. He has taught and managed programs in the U.S. and abroad. This article is adapted from a forthcoming book titled, Unrig the Game: A Proven, Systematic Approach to Successful College Transitions for Adult and Developmental Education Students, with co-author, Charlene Gill.

Payne et al. (2012) show that full-time Adult Basic Education instructors achieve better student performance results than part-time instructors. Unfortunately, very few program directors believe they can afford the expense of hiring full-time instructors. During my nine year tenure as the adult education director at ACC, I oversaw the increase of salaried instructors with health insurance and retirement increase from 9 to 22. During that period, we made significant investments in instructor salary and benefits, but also witnessed significant enrollment increases and performance improvements.

I believe the path to professionalizing the industry is not found in low pay and/or encouraging regions to use more volunteers. Rather, the path is by providing teachers with stable employment, health insurance, retirement plans, and sustained and systematic professional development; by engaging them in decision-making; and by moving away from a seniority system to one that rewards excellence in teaching.

Raising teacher salaries is a long term solution that is difficult to implement in the short term. In our case, salaried instructors cost 30% – 50% more than hourly instructors when you factor in health insurance and retirement plans. This expense can be hard on a limited grant budget, and impossible on a small budget. We have a large enough program (about 4,000 students served per year) that I could find places to reallocate resources. I shut down classes with low enrollment, even with long-standing, high-profile partners that didn’t appreciate being sacrificed for the greater good. Every four classes closed generated a twenty-hour-a-week, salaried instructor with full benefits. Average class sizes grew, but we still capped enrollment at 20 per class.

This strategy created a core faculty that often accrue between 30 and 50 hours of professional development per year. These faculty are engaged in curriculum development, mentoring hourly instructors, and leading workshops. Over the years, hourly and salaried instructors have seen our commitment to them, and they have returned that commitment to the program. These changes have increased our ability to recruit teachers because salaries are more competitive with staff jobs at the college; thus, our ratio of teachers with master’s degrees has doubled. In addition, we’ve reduced costs associated with attrition and training.

In conclusion, we only hire the highest quality instructors into the core faculty. We do not follow a seniority system, but rather look to fill these positions with teachers who not only are effective with students, but also demonstrate a belief in the mission of the division by collaborating well with their colleagues to make considerable contributions.

References

Payne, E.M., Reardon, R.F., Janysek, D.M., Lorenz, M., Lampi, J.P. (2012). Impact on student performance: Texas Adult Education Teacher Credential Study preliminary results. Report for The Texas Adult Education Credential Project, Texas State University

 

Doing Different in the Mathematics Classroom

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Stephanie Cockrell Andrews, Ed.D.

Dr. Stephanie Cockrell Andrews is a mathematics professor and the mathematics department lead faculty at Lone Star College-Kingwood (LSC-K).  She has earned degrees from East Texas Baptist University, Stephen F. Austin State University, and Sam Houston State University. This is her 28th year in education, where 15 of those years were in public education as a secondary mathematics teacher and counselor.  Stephanie was a 2006 Project ACCCESS fellow with the American Mathematical Association of Two-Year Colleges (AMATYC). She has received the Faculty Excellence Award at LSC-K and the Educational Leadership Doctoral Award at Sam Houston State University.  She is a member of the Delta Kappa Gamma Society International for Key Women Educators. 

In the report, Closing the Gaps by 2015: 2009 Progress Report, the Texas Higher Education Coordinating Board (THECB, 2009) stated, “Texas must take bold steps for the future success of its people” (p. ii). Being the math chair, my president was always stressing to me that we needed to increase student success (A, B, or C) in our developmental courses, to get more students to and through our gateway mathematics course—and to do it all faster! Add in the definition of insanity—attributed to several, including Einstein (Howes, 2009)—of “doing the same thing over and over again and expecting different results,” and I was determined to do something that was bold and different.

So, during 2013 – 2014, I taught Foundations of Mathematical Reasoning (FMR) and Statistical Reasoning (SR) using the curriculum from The Dana Center at The University of Texas in Austin, and it rocked my academic world. I am a dedicated, traditional algebra teacher, and I have received awards for teaching, but when I taught these courses, my life and the lives of my students changed. The New Mathways Project (NMP) courses are based on principles including to provide relevant and rigorous mathematics, help students complete college-level math courses faster and use intentional strategies that help students grow as learners (The Charles A. Dana Center, 2013).

I have always been told that, while I am teaching, I should include real-world problems, interdisciplinary activities, collaborative work, active learning, productive struggle, reading and writing. I could not get all of this included much less included well, but NMP incorporates all of these skill—all based on proven practice! I did it with NMP!  I saw it work for me and be transformational for my students.

Even though this is controversial, I believe what I experienced teaching these courses is a strong rationale that this can be done and should be done. The courses are rigorous, involve collaborative learning; are saturated with real-world problems that the students get excited about (e.g., blood-alcohol-level formula for order of operations); teach students to be much better college students and well-informed citizens; and are much more closely aligned with degree programs than college algebra for non-STEM majors.

Testimonials from students include a video from Holly at https://utexas.box.com/s/vmr9xlba4kxv66csehm35obdsm716yml.

And an article by Kaleena Steakle at https://www.theguardian.com/pearson-partner-zone/2016/aug/31/approaching-math-differently-to-change-lives.

I have been working the last two years for The Dana Center helping other professors in our state and nation implement the NMP materials, but this week, I started back in the classroom! I have three, full FMR classes, and I am extremely excited to see how the students will grow this semester and be propelled to the next steps of their careers.

References

Howes, Ryan. (2009, July 27). The definition of insanity is…perseverance vs. perseveration. Retrieved from https://www.psychologytoday.com/blog/in-therapy/200907/the-definition-insanity-is

Texas Higher Education Coordinating Board. (2009). Closing the gaps by 2015: 2009 progress report. Retrieved from http://www.thecb.state.tx.us/reports/pdf/1852.pdf

The Charles A. Dana Center. (2016). The New Mathways Project curricular materials. Retrieved from http://www.utdanacenter.org/higher-education/new-mathways-project/new-mathways-project-curricular-materials/

 

Part-Whole Study Improves Memory for Science Information

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Michelle Kiser, Ed.D.

Dr. Michelle Kiser received her Bachelor of Science, Master of Arts, and Doctorate of Education at Texas Tech University.  Michelle completed her dissertation on the “Developmental Students Sources of Self-Efficacy and the University Academic Support Program Impact.” Michelle worked as the Assistant Director of Texas Success Initiative (TSI) Developmental Education Program for five years prior to being promoted to the Director of Support Operations for Academic Retention (SOAR) in May 2009. Michelle manages four programs within SOAR: The Learning Center, Supplemental Instruction, Texas Success Initiative, and Programs for Academic Development and Retention. Michelle has been employed by Texas Tech University for over 14 years. In addition, Michelle is an adjunct instructor for the College of Education at Texas Tech University teaching Teacher Education courses in Content Area Reading.  In her spare time, Michelle volunteers for Court Appointed Special Advocates (CASA).

Segmentation of information has been shown to increase comprehension and retention of multimedia materials (Mayer & Chandler, 2001; Mayer, Dow & Mayer, 2003; Singh, Marcus & Ayres, 2012). We wondered if memory for science text could be improved by studying information in pieces and then all together.

In a part-whole study method, the person studies the text in several parts and then as a whole, rather than being presented immediately with the whole text. We conducted an experiment to determine whether a part-whole method would enable non-developmental and developmental readers to recall more from a science text compared to using a whole-text method.

Forty-three developmental college readers and 52 non-developmental college readers studied a science text about sea otters. The complete text was about 300 words and had a readability level at approximately an 8th grade level. Half the students in each group were presented with the whole text, and half were presented with the text using the part-whole method. All students studied the text for 10 minutes total. The text was presented on a computer screen, and the timing was controlled by the computer. After studying the text, students were asked what percentage of the text they thought that they comprehended, and what percentage of the text they thought they could recall. They were then asked to recall as much of the text as they could using the computer. Recall was measured using the number of idea units from the passage that each student was able to recall.

The study showed the superiority of the part-whole method when studying science texts. The non-developmental students recalled more idea units than the developmental students, but importantly, both non-developmental and developmental students recalled more idea units when using a part-whole method instead of a whole-text method.

Developmental students who used a part-whole method compared to those who used a whole-text method reported that they comprehended a greater percentage of the text.

Developmental students who used a part-whole method compared to those who used a whole-text method predicted that they would recall a greater percentage of the text—and they actually did!

Overall, the findings suggest that developmental and non-developmental readers are not qualitatively different. Rather, they engage in similar processes, but differ in the skill and effectiveness with which they apply those processes.

As Nist and Simpson point out, “[T]he complexity of learning and studying…cuts across all college students, not just developmental students or students who are struggling” (quote from Stahl, 2006, p. 21).

References

Mayer, R. E., & Chandler, P. (2001). When learning is just a click away: Does simple user interaction foster deeper understanding of multimedia messages? Journal of Educational Psychology93(2), 390.

Mayer, R. E., Dow, G. T., & Mayer, S. (2003). Multimedia learning in an interactive self-explaining environment: What works in the design of agent-based microworlds? Journal of Educational Psychology95(4), 806.

Stahl, N. A. (2006). Strategic reading and learning, theory to practice: An interview with Michele Simpson and Sherrie Nist. Journal of Developmental Education, 29 (3), 20-24, 26, 27.

 

 

Breaking Out of the e-Learning Courseware Box: Integrating Social Media

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Steven S. Vrooman, Ph.D.

Dr. Steven S. Vrooman is a Professor of Communication Studies, Chair of the Department of English and Communication Studies, and Director of General Education at Texas Lutheran University. Following his B.A. in English at Loyola Marymount University, he earned his M.A. and Ph.D. in Communication from Arizona State University. He spoke at TEDxSanAntonio on how our brains work like Twitter. He is the author of The Zombie Guide to Public Speaking and writes The MoreBrainz Blog, which offers help for public speaking and pedagogy. He can be reached via email at svrooman@tlu.edu.

We are sure e-learning works, although we often act as if all online practices are the same as we continue to investigate online vs. face-to-face modes and find them equivalent. The finding remains the same over the course of ten years (Schaik, Barker, & Beckstrand, 2003); Mativo, , & Godfrey, 2013), yet each online course seems to have different designs.  Additionally, although we also believe that social media is good for learning, Facebook, to take one platform, sometimes works (Kivunja, 2015) and sometimes does not (Moran, Seaman, & Tinti-Kane, 2011), and my reading of the studies seems to indicate that it depends on what we use it for and how.

In reviewing the growing literature on e-learning and social media and the various course practices that bridge them, it is clear, as with PowerPoint an educational generation ago, that when we drill down to exact practices, some things work (see, I’m sure, the past fifteen years of each of our teaching, right?) and some don’t (Adams, 2006). Specific analysis of specific practices is the only way forward. To paraphrase McLuhan, it’s not the medium, it’s the pedagogy.

To that end, I have used the following social media practices in class:

  1. Blogs: Students post data analysis, drafts, final projects and peer review them, publically.
  2. Public Blog Comments: Alumni/outside experts invited to critique student work.
  3. Discussion via Facebook Event: Including alumni/experts.
  4. Students Publicized Work: They did work on Instagram and shared it & blog work via Twitter, Facebook and LinkedIn.

Qualitative assessment of the outcomes of these results suggested the following positive outcomes:

  1. Better Work: Public work is better work, especially when outside voices tell them to improve it and students are promoting it.
  1. Engagement: Social media, used in certain ways, can increase engagement more than courseware, which can feel like a waste-of-time, count-my-comments-for-the-grade echo chamber.
  1. Portfolio: Students can retain their entire work to show progression or just the final versions to demonstrate their expertise.
  1. E-Learning Bonuses: Most gamified elearning practices work better on social media than in courseware. For example, debates have more at stake and engage the public. Creative projects get a larger audience and thus bigger reaction.
  1. Skillset Development: For my communication studies majors, social media skills are key. For other majors, they are more important than you might think.
  1. Alumni Engagement: Many LOVED the opportunity to reconnect with professors and students in this way and share their new skills and perspectives. Mentoring happened in many cases. And it set the stage for increased inclusion of those alumni in face-to-face events with students.

It also revealed the following challenges:

  1. Age:
    1. Nontraditional students: They had troubles: unwilling/critical of social media, self-doubt due to lack of familiarity, higher privacy concerns.
    2. Traditional students: They had troubles: difficulty adjusting to violation of “fun” space, difficulty with academic self-promotion.
  1. Sign-Ups:
    1. Technical Difficulties: Fewer than with courseware & easy to Google answers to, but signing up for accounts is surprisingly very hard for them.
    2. Secondary Accounts: Younger students often do not want classwork in their personal accounts, but second email addresses are often required for multiple accounts. Managing multiple accounts is easy for some platforms (Twitter) but hard in others (Instagram, Facebook, LinkedIn).
  1. Oversight: Hashtags are not enough to find their work. You need them to @ you or you won’t see everything.
  1. Content ABOUT Social Media is Needed: Things like how-tos, technical difficulties, privacy, etiquette, bullying/flaming, etc. probably need class time/resources to go over (however, offloading classtime experiences into social media helps offset this).

References

Adams, C. (2006). PowerPoint, habits of mind, and classroom culture. Journal of Curriculum Studies, 38, 389-411.

Kivunja, C. (2015). Innovative methodologies for 21st century learning, teaching and assessment: A convenience sampling investigation into the use of social media technologies in higher education. International Journal of Higher Education, 4 (2), 1-26.

Mativo, J. M., Hill, R. B., & Godfrey, P. W. (2013). Effects of human factors in engineering and design for teaching mathematics: A comparison study of online and face-to-face at a technical college. Journal of STEM Education: Innovations & Research, 14, 36-44.

Moran, M., Seaman, J., & Tinti-Kane, H. (2011). Teaching, learning and sharing: How today’s higher education faculty use social media. Babson Survey Research Group. ERIC: ED535130.

Van Schaik, P., Barker, P., & Beckstrand, S. (2003). A comparison of on-campus and online course delivery methods in Southern Nevada. Innovations in Education & Teaching International, 40, 5-15.

 

Teaching Writing to Students in Transition: Models for Success

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William J. Barry

While researching how robust technology use can improve students’ first-year experience, William J. Barry teaches academic research and writing at Concordia University.  He also trains adult educators in partnership with the Texas Center for the Advancement of Literacy and Learning (TCALL) and teaches first-year seminar at Texas State University, where he is a Ph.D. candidate in developmental education.

Writing helps students learn and persuade (Graham, Gillespie, & Mckeown, 2013), while supporting lifelong literacy, but learning writing challenges learners and involves a complex process.  Along the way, developing writers pass through stages, including telling only what they know, transforming the text to their own benefit, and adjusting the text for the reader’s benefit (Kellogg, 2008).

As writers acquire competency, they emphasize prospective beliefs regarding the reader’s understanding of the text (Kellogg, 2008), and they target their audience by applying elaborated strategies to structure and content problems (Hayes et al., 1987).  As per Spivey’s (1990) academic writing skills–selecting, organizing, and connecting sources–Schriver (2012) described using genre knowledge, arranging non-related text parts into a coherent document, and balancing the appropriate mix between content and target audience, according to community-specific expectations as essential skills.

Creating text, which reflects a clear understanding of reader perspective, structure, and content, requires writers to use a diverse toolkit of knowledge, skills, and strategies (Hayes & Flower, 1980).  One of the challenges educators face involves helping students acquire those tools and the ability to employ them effectively, and meeting the challenge means first explicitly teaching the skills, strategies, and knowledge relevant to academic writing.

Several supported models, including cognitive apprenticeship (Collins et al., 1989) and the socio-cognitive model (Schunk and Zimmerman, 1997) suggest sequences of learning by first observing before doing.  In other words, students must first observe a model (Zimmerman and Kitsantas, 2002), either a mastery model or a coping model (Bandura, 1997; Schunk, 1991).

Since academic writing includes building a macrostructure of the text as a first step, students need training on how the text should appear (Graham et al., 2012).  In particular, they need to learn and apply the text structure of the key genre in their community, which, for students in transitional roles, tend to be the various essays and term papers expected of a liberal arts education.  Starting by familiarizing students with the components, structure, and function(s) of such writing provides them with the essential framework within which to apply later process and skill training, translating to higher retention, better outcomes, and overall satisfaction.

References

Bandura, A. (1997). Self-Efficacy: The Exercise of Control. New York: Freeman.

Collins, A., Brown, J.S., & Newman, S.E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.) Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 453-494). Hillsdale, NJ: Lawrence Erlbaum Associates.

Graham, S., Gillespie, A., & Mckeown, D. (2013). Writing: importance, development, and instruction. Reading and Writing, 26(1), 1–15.

Graham, S., Mckeown, D., Kiuhara, S., & Harris, K. R. (2012). A meta-analysis of writing instruction for students in the elementary grades. Journal of Educational Psychology, 104(4), 879–896.

Hayes, J. R., & Flower, L. S. (1980). Identifying the organization of writing processes. In L. W. Gregg & E. R. Steinberg (Eds.) Cognitive Processes in Writing (pp. 3-30). Mahwah, New Jersey: Lawrence Erlbaum Associates.

Hayes, J. R., Flower, L., Schriver, K., Statman, J., & Carey, L. (1987). Cognitive processes in revision. In S. Rosenberg (Ed.) Reading, Writing, and Language Possessing (Vol. 2, pp. 176-240). Cambridge: Cambridge University Press.

Kellogg, R. T. (2008). Training writing skills: A cognitive developmental perspective. Journal of Writing Ressearch, 1(1), 1–26.

Schriver, K. (2012). “What we know about expertise in professional communication,” in Past, Present, and Future Contributions of Cognitive Writing Research to Cognitive Psychology ed. Wise Berninger V., editor. New York: Psychology Press.

Schunk, D. H. (1991). Learning Theories: An Educational Perspective. New York, NY: Merrill.

Schunk, D. H., & Zimmerman, B. J. (1997). Social origins of self-regulatory competence. Educational  Psychologist, 32, 195–208.

Spivey, N. N. (1990). Transforming texts constructive processes in reading and writing. Written Communication, 7, 256–287.

Zimmerman, B. J., & Kitsantas, A. (2002). Acquiring writing revision and self-regulatory skill through observation and emulation. Journal of Educational Psychology, 94, 660.