After writ­ing my last post, I recalled an excerpt from a book that I had recently read.  I dug through the book today and located the sec­tion that I had pre­vi­ously found so humor­ous. (I need all the humor I can get this week since I’m not in beau­ti­ful San Diego attend­ing ISTE with friends and col­leagues!)  The fol­low­ing list can be found in Rethink­ing Edu­ca­tion in the Age of Tech­nol­ogy by Collins and Halver­son (pg. 30).  Their list high­lights the many exam­ples of how edu­ca­tion has been very resis­tant to change.

• From a principal’s pub­li­ca­tion in 1815: “Stu­dents today depend on paper too much.  They don’t know how to write on a slate with­out get­ting chalk dust all over them­selves.  They can’t clean a slate prop­erly. What will they do when they run out of paper?”
• From the jour­nal of the National Asso­ci­a­tion of Teach­ers, 1907: “Stu­dents today depend too much upon ink.  They don’t know how to use a pen knife to sharpen a pen­cil.  Pen and ink will never replace the pencil.”
• From Rural Amer­i­can Teacher, 1928: “Stu­dents today depend upon store bought ink.  They don’t know how to make their own.  When they run out of ink they will be unable to write words or ciphers until their next trip to the set­tle­ment.  This is a sad com­men­tary on mod­ern education.”
• From Fed­eral Teach­ers, 1950: “Ball­point pens will be the ruin of edu­ca­tion in our coun­try.  Stu­dents use these devices and then throw them away.  The Amer­i­can val­ues of thrift and fru­gal­ity are being dis­carded.  Busi­nesses and banks will never allow such expen­sive luxuries.”
• From a sci­ence fair judge in Apple Class­room of Tomor­row chron­i­cles, 1988: “Com­put­ers give stu­dents an unfair advan­tage.  There­fore, stu­dents who used com­put­ers to ana­lyze data or cre­ate dis­plays will be elim­i­nated from the sci­ence fair.”

Photo credit: San­dor on Flickr

I read this list and won­der how future edu­ca­tors will view our resis­tance to change.  How will they view our adher­ence to seat time rather than com­pe­tency based instruc­tion? How will they view our rigid school sched­ule?  How will they view our assess­ment sys­tem that uses let­ter grades?  This list could go on and on, but it becomes evi­dent quickly when reflect­ing on our sys­tem that we do many things that don’t make much sense other than to stay in line with the cur­rent system.

Nick Sauers

As many of you have may have seen by now, there is a viral video that shows the relent­less bul­ly­ing of a 68-year old bus mon­i­tor by what appear to be mid­dle school stu­dents.  The stu­dents are ter­ri­bly cruel, and the video goes on for 10 min­utes.  At the time of writ­ing this post, the YouTube video has been seen by over 7 mil­lion view­ers and increas­ing quickly.  The main­stream news media has also picked up on this story and it has gen­er­ated con­ver­sa­tions about the nature of youth in our coun­try. It cer­tainly seems that when some­thing awful like this hap­pens, we (espe­cially the media) dwell on the neg­a­tive.  Our soci­ety seems to suf­fer from the “The Sky is Falling!” syn­drome as can be evi­denced by one com­ment under the video.

Every time I watch this video, my faith in the future gen­er­a­tion wilts away slowly. So these are the future lead­ers? I may as well say good­bye now.

As awful as the expe­ri­ence that appears in this video, the pos­i­tive response online has been over­whelm­ing.  The “Lets Give Karen — The bus mon­i­tor — H Klein A Vaca­tion!” web­site was cre­ated in an effort to raise $5,000 to send Karen on a vaca­tion.  Cur­rently, the site has raised  nearly $650,000 from over 26,000 donors. No, those num­bers were not typos!

So what does this have to do with tech­nol­ogy in education?

“The Sky is Falling!” com­plex I men­tioned above seems to be extremely preva­lent when in comes to tech­nol­ogy in edu­ca­tion.  We, and espe­cially the media, seem to con­stantly focus on all the neg­a­tive things hap­pen­ing with tech­nol­ogy.  Schools block YouTube because of videos like this.  Face­book is seen as evil at many schools because of the harass­ing that may occur, or because of inap­pro­pri­ate rela­tion­ships between stu­dents and teach­ers.  Social media in gen­eral is con­sid­ered a very dan­ger­ous place for students.

We often don’t con­sider the pos­i­tive things hap­pen­ing with all of those tech­nolo­gies.  What about YouTube videos that have allowed stu­dents to cre­ate a global audi­ence? How about the teacher who dras­ti­cally increased com­mu­ni­ca­tion with stu­dents through a class Face­book page? I con­stantly find myself chal­leng­ing schools to help their stu­dents cre­ate a pos­i­tive dig­i­tal foot­print rather than teach­ing their stu­dents to be invis­i­ble online. Does your school oper­ate with a “The Sky is Falling!” men­tal­ity, or does it chal­lenge stu­dents to cre­ate a pos­i­tive dig­i­tal footprint?

Nick Sauers

If you read the pre­vi­ous posts, you received a quick overview of the find­ings from my dis­ser­ta­tion.  As a recap, some of those major find­ings were:

1. Schools that were 1:1 looked dif­fer­ent than non-1:1 schools in Iowa on     multiple characteristics.
2. Teach­ers at 1:1 schools reported that their stu­dents used tech­nol­ogy more fre­quently than their non-1:1 peers.
3. Teach­ers at 1:1 schools reported that they inte­grated tech­nol­ogy at higher lev­els than their non-1:1 peers.
4. Teach­ers at 1:1 schools reported higher com­pe­tency scores with tech­nol­ogy than their non-1:1 peers.

So with the find­ings that I do have, it gen­er­ates ques­tions about their value.  So what?  Why does this mat­ter?  My response to these ques­tions is from a lead­er­ship and pol­icy per­spec­tive.    School lead­ers should care about find­ings two through four.  IF those find­ings are things they would like to see in their schools, 1:1 may be a great ini­tia­tive for them.  How­ever, if they don’t value those char­ac­ter­is­tics, a 1:1 pro­gram may not be worth­while to them.  My belief, as you have prob­a­bly guessed, is that those things are important.

I WANT stu­dents using tech­nol­ogy more fre­quently at schools!

I WANT teach­ers inte­grat­ing more tech­nol­ogy into their teaching!

I WANT teach­ers who are more com­pe­tent with technology!

As is often the case, this research  may gen­er­ate more ques­tions than answers. Now that some of these find­ings have been iden­ti­fied, it would be extremely help­ful to get at the “why” of these results.  Are 1:1 teach­ers report­ing higher tech­nol­ogy com­pe­tency because of addi­tional train­ing or is it sim­ply access to tech­nol­ogy? A com­bi­na­tion of the two is prob­a­bly likely. This ques­tion is just one of many that I’ve gen­er­ated in my head.

Nick Sauers

*As you read this post, you may real­ize that it is very sim­i­lar to the pre­vi­ous post.  Although find­ings are sim­i­lar, this post relates to a dif­fer­ent variable.

The final research ques­tion in my dis­ser­ta­tion focused on teacher’s reported com­pe­tency with tech­nol­ogy.  Teach­ers were asked two ques­tions on the sur­vey to address this ques­tion.  The find­ings from this ques­tion indi­cate that 1:1 teach­ers reported higher com­pe­tency scores than their non-1:1 peers on this study. On aver­age, teach­ers at 1:1 schools reported scores that were .29 of a stan­dard devi­a­tion higher than their non-1:1 peers.  This may be one of the more inter­est­ing find­ings from my study.  Teach­ers at 1:1 schools reported that they are much more com­pe­tent with tech­nol­ogy than their non-1:1 peers. This find­ing may lead to many more ques­tions. Do 1:1 teach­ers report higher com­pe­tency because of better/more pro­fes­sional devel­op­ment?  Are 1:1 teach­ers forced to increase com­pe­tency because they have a class­room full of stu­dents with a com­put­ing device? I hope to ana­lyze the pro­fes­sional devel­op­ment com­po­nent in more detail within the next year.  My sur­vey does include some infor­ma­tion that may help me address that question.

Like in my pre­vi­ous post, there were some addi­tional find­ings from this ques­tion that may be inter­est­ing, but they should be inter­preted cau­tiously.  The find­ings I’ll describe below were not related to 1:1 sta­tus, but rather teacher age and con­tent area.

For the age vari­able, teach­ers were grouped into five age cat­e­gories with 20–30 being the youngest age group.  The other teach­ers fell into groups that each cov­ered ten years.  The find­ings from this indi­cated that when com­pared with the 20–30 year age group, the tech­nol­ogy com­pe­tency scores from all of the other groups were sig­nif­i­cantly lower than that youngest group (see note below if you’re inter­ested in why I com­pared to that youngest group).

Teach­ers were also placed into one of ten con­tent area vari­ables.  The com­pe­tency scores of teach­ers from each con­tent area were com­pared to lan­guage arts teach­ers (see note below if you’re inter­ested in why I com­pared to Lan­guage Arts).  Com­pe­tency scores from teach­ers in three of the con­tent areas were lower than the scores of lan­guage arts teach­ers.  Those areas were For­eign Lan­guage, Math, and PE/Health.

I should again stress that my study was designed to ana­lyzed the impact of 1:1 on my research ques­tions.  The age and con­tent area find­ings are not related to 1:1 status.

Nick Sauers

Note:

For those of you famil­iar with regres­sion mod­els in sta­tis­tics, you may under­stand that you need to iden­tify a ref­er­ence cat­e­gory when using a dummy vari­able.  For those of you less famil­iar with regres­sion mod­els, it is impor­tant to under­stand what a dummy vari­able is. A dummy vari­able is a vari­able that would hold a value of 0 (no) or 1 (yes) for each item.  For exam­ple, if I asked a teacher if they taught Fine Arts, they would answer either yes (1) or no (0).  Each respon­dent was coded 0 or 1 for each con­tent area and age cat­e­gory. The next thing I had to do was to iden­tify a ref­er­ence cat­e­gory to com­pare to those dummy vari­ables.  I selected the youngest age group because of the per­cep­tion that some have about younger edu­ca­tors being more com­fort­able with tech­nol­ogy.  I selected Lan­guage Arts for the con­tent area vari­able because much of the 1:1 research indi­cates that some of the largest aca­d­e­mic gains have been in that area.

The sec­ond research ques­tion in my study attempted to ana­lyze the tech­nol­ogy inte­gra­tion occur­ring at 1:1 schools.  Each edu­ca­tor had an “inte­gra­tion” score which was gen­er­ated from 14 ques­tions about the use of var­i­ous tech­nol­ogy tools in the class­room.  Teach­ers responded to the ques­tion about inte­gra­tion of the var­i­ous tools on a 4-point Lik­ert scale that ranged from “Not at all” to “A large extent”.  The find­ings from this ques­tion were also quite pow­er­ful. On aver­age, teach­ers at 1:1 schools reported scores that were 0.28 of a stan­dard devi­a­tion higher than teach­ers at non-1:1 schools on the 4-point scale rep­re­sent­ing how much they inte­grated tech­nol­ogy into their class­rooms.  In non-statistical terms, this is a large dif­fer­ence.  1:1 edu­ca­tors in the study were inte­grat­ing tech­nol­ogy at much higher lev­els than their non-1:1 peers. This find­ing may be of great inter­est to school lead­ers who are decid­ing how and if they should strive to increase tech­nol­ogy inte­gra­tion in their school.

There were some addi­tional find­ings from this ques­tion that may be inter­est­ing, but they should be inter­preted cau­tiously.  The find­ings I’ll describe below were not related to 1:1 sta­tus, but rather teacher age and con­tent area.

For the age vari­able, teach­ers were grouped into five age cat­e­gories that each cov­ered a ten year age span with 20–30 being the youngest age group. The find­ings from this indi­cated that when com­pared with the 20–30 year age group, the tech­nol­ogy inte­gra­tion scores from all of the other groups were sig­nif­i­cantly lower than that youngest group (see note below if you’re inter­ested in why I com­pared to that youngest group).  Inter­est­ingly, this same find­ing was not evi­dent in the vari­able for time.  This may indi­cate that older teach­ers are will­ing to use tech­nol­ogy they are com­fort­able with, but they haven’t been pro­vided the train­ing that allows them to achieve a higher inte­gra­tion score.

Teach­ers were also placed into one of ten con­tent area vari­ables.  The inte­gra­tion scores of teach­ers from each con­tent area were com­pared to lan­guage arts teach­ers (see note below if you’re inter­ested in why I com­pared to Lan­guage Arts).  Inte­gra­tion scores from teach­ers in five of the con­tent areas were lower than the scores of lan­guage arts teach­ers.  Those areas were Fine Arts, For­eign Lan­guage, Math, PE/Health, and Science.

I should again stress that my study was designed to ana­lyze the impact of 1:1 on my research ques­tions.  The age and con­tent area find­ings are not related to 1:1 status.

Nick Sauers

Note:

For those of you famil­iar with regres­sion mod­els in sta­tis­tics, you may under­stand that you need to iden­tify a ref­er­ence cat­e­gory when using a dummy vari­able.  For those of you less famil­iar with regres­sion mod­els, it is impor­tant to under­stand what a dummy vari­able is. A dummy vari­able is a vari­able that would hold a value of 0 (no) or 1 (yes) for each item.  For exam­ple, if I asked a teacher if they taught Fine Arts, they would answer either yes (1) or no (0).  Each respon­dent was coded 0 or 1 for each con­tent area and age cat­e­gory. The next thing I had to do was to iden­tify a ref­er­ence cat­e­gory to com­pare to those dummy vari­ables.  I selected the youngest age group because of the per­cep­tion that some have about the younger edu­ca­tors being more com­fort­able with tech­nol­ogy.  I selected Lan­guage Arts for the con­tent area vari­able because much of the 1:1 research indi­cates that some of the largest aca­d­e­mic gains have been in that area.

In the last post, I wrote about how I com­pared 1:1 schools to the rest of the schools in the state.  That process enabled me to use a tech­nique called propen­sity score match­ing to iden­tify con­trol schools that would look sim­i­lar to 1:1 schools on mul­ti­ple mea­sures (22 vari­ables).  After com­plet­ing that process I had iden­ti­fied my 37 treat­ment (1:1 schools) and 73 con­trol (non-1:1 schools).  Sur­veys were then sent to teach­ers at each of those schools, and I received approx­i­mately 1,000 com­plete responses.  The sur­vey responses were then used to answer the three major research ques­tions from my study.

The first research ques­tion is one that may seem to have a very obvi­ous answer.  I wanted to inves­ti­gate whether there were major dif­fer­ences in the amount of time that 1:1 and non-1:1 stu­dents used tech­nol­ogy. How­ever, schools have often invested in tech­nolo­gies that have failed to reach wide-scale adop­tion or use (Cuban, 1986; Saet­tler, 2004). My lit­er­a­ture review focused on many of those wide scale tech­nol­ogy imple­men­ta­tions that didn’t result in increased time using tech­nol­ogy. Project Red actu­ally reported that in 40% of 1:1 schools, stu­dents did not use the tech­nol­ogy on a daily basis. My expe­ri­ences in edu­ca­tion also pro­vide much anec­do­tal evi­dence about how increased tech­nol­ogy doesn’t always amount to increased stu­dent use.  One exam­ple I’ve often seen is the dust filled tech­nol­ogy room in the media cen­ter.  Another quite obnox­ious exam­ple is the inter­ac­tive white board that has turned into a bul­letin board.

Teach­ers were able to select their answers from a 7-point Lik­ert scale.  The responses ranged from “Not at all” to “Daily.”  On aver­age, teach­ers at 1:1 schools reported scores that were over 1 point higher than teach­ers at non-1:1 schools on the 7-point scale rep­re­sent­ing how much time stu­dents use tech­nol­ogy. Although my study didn’t allow me to quan­tify the num­ber of min­utes stu­dents used tech­nol­ogy, it did allow me to acknowl­edge that a major dif­fer­ence existed between stu­dents at 1:1 schools and those that weren’t at 1:1 schools. This find­ing may be fairly pow­er­ful for school lead­ers who believe that their stu­dents should be using tech­nol­ogy on a reg­u­lar basis. The table below high­lights the num­ber of responses for each of the choices.  As you can see, there were fewer 1:1 teach­ers who responded, but their scores were much higher on the scale overall.

Cuban, L. (1986). Teach­ers and machines:  The class­room use of tech­nol­ogy since 1920. New York, N.Y.: Teach­ers Col­lege Press.

Saet­tler, P. (2004). The evo­lu­tion of Amer­i­can edu­ca­tional tech­nol­ogy. Green­wich, CT: Infor­ma­tion Age Publishing.

Nick Sauers

I pre­vi­ously posted my ini­tial find­ings about the look of 1:1 in Iowa in Jan­u­ary. This post should hope­fully pro­vide some addi­tional insight.  Like many stud­ies, one of my first tasks was to iden­tify con­trols and treat­ments.  Iden­ti­fy­ing my treat­ment schools was fairly easy.  I wanted to study 1:1 schools that had a 1:1 sta­tus prior to the cur­rent cal­en­dar year.  After elim­i­nat­ing five of those schools for var­i­ous rea­sons, I was left with 37 1:1 schools in my study.  The next major chal­lenge was iden­ti­fy­ing con­trol schools for the study.  Because I believed 1:1 schools in Iowa may look dif­fer­ent than other schools, I wanted to iden­tify con­trol schools that were sim­i­lar to the treat­ment schools.  In order to iden­tify those schools, I used propen­sity score match­ing.  A sim­ple def­i­n­i­tion of propen­sity score match­ing is that it is a tech­nique to com­pare apples to apples.

This leads to some of the inter­est­ing find­ings from the first part of my study.  I looked at nearly 150 school level char­ac­ter­is­tics com­par­ing 1:1 schools to the rest of the schools in the state. Those school char­ac­ter­is­tics served as vari­ables through­out my study. Even­tu­ally, I ended up elim­i­nat­ing many of those vari­ables because they reported the same thing.  For exam­ple, I had numer­ous dif­fer­ent vari­ables that all basi­cally reported major dis­ci­pline occur­rences or teacher pay.  Even­tu­ally I reduced the num­ber of school level vari­ables to 22.  Of those vari­ables, there were 11 that were very dif­fer­ent between 1:1 schools and the rest of the schools in the state.  Those vari­ables and the means for each group are listed on the table below. It is a bit con­fus­ing, but impor­tant to note that these data are from 2007-08.  It would have been improper to use cur­rent data because that data may have been impacted by a schools tran­si­tion to 1:1.  In other words, these data aren’t results of a 1:1 pro­gram.  They instead attempt to cap­ture the types of schools that have made the tran­si­tion to 1:1 in the state of Iowa.

My next group of posts will focus on some of the find­ings from my dis­ser­ta­tion. This post will hope­fully pro­vide you with a bit of an overview of the project. My dis­ser­ta­tion was titled 1:1 Lap­top Impli­ca­tions and Dis­trict Pol­icy Con­sid­er­a­tions, and it was intended to answer some sys­tems level ques­tions about what is hap­pen­ing at 1:1 schools.  The study focused on 1:1 schools in Iowa, and I col­lected data from mul­ti­ple sources.  Data about the schools and their staffs were col­lected through the Iowa Depart­ment of Edu­ca­tion and the Com­mon Core of Data.  Addi­tional teacher data were col­lected using a tool adapted from one cre­ated by Hutchi­son and Reinking’s instru­ment.  I was very for­tu­nate that nearly 1000 teach­ers fully com­pleted the sur­vey and over 100 schools were included in the study, 36 which were 1:1 schools.  I tried to write the series in a way that would be mean­ing­ful to prac­ti­tion­ers, and not focus on my sta­tis­ti­cal meth­ods and tech­niques. I’d be happy to chat more about my method­ol­ogy if you’re interested.

Nick Sauers

nck0208@gmail.com