Attitudes & Effects of Vocational School Students’ Classroom Smartphone Use in

the Spanish Castilla-Leon Region

Lifen Cheng lfcheng@usal.es

Alejandro López u118967@usal.es

University of Salamanca

November 2nd-4th, 2016

• Smartphones offer countless ICT applications that enable users to open up to new form of access to knowledge and information networks at any time and anywhere (Traxler, 2009).

• This leads to multiplication of possibilities for teaching method

innovations that favour learning processes (Milrad and Spikol,2007; Wu & Wang, 2009; Williams & Pence,2011; Robly & Doering, 2013).

FOR

Controversies about classroom smartphone use:

• Smartphone is regarded as bothering, source for distraction and cheating if allowed in classrooms. Hand held smartphone encourages texting, surfing and other activities that are unrelated to education (Campell, 2005).

• Technologies do not improve academic performance. Smartphones and other devices cannot replace the ability of teachers to engage easily distracted students (Levine et al, 2012; Drozdenko et al., 2012; Espejo et al. 2013).

AGAINST

• Teachers of a later generation feel more comfortable with smartphones use.

• This leads them to favour introducing innovative teaching methods based on “m-learning” (mobile learning).

(Thomas & Orthober, 2011; Thomas & McGee, 2012; Johnson et al., 2012; Thomas, O’Bannon & Bolden, 2013).

THE 3RD VIEW

Main interests of this study:

• What are students’ attitudes toward smartphone use in classrooms?

• Does smartphone use improve students’ academic performance?

Exploratory Hypotheses

H1: More positive attitudes => [smartphone use in classrooms] => more intensive use for [learning purposes].

H2a: More intensively smartphone use for [learning purposes] => [higher engagement] in school activities.

H2b: More intensively using smartphone for [learning purposes] =>

[better academic performance] + [higher satisfaction] with studies.

H3: [Younger] students using smartphone more intensive for learning purposes => [higher academic performance] outcomes than [Elder ones].

METHODOLOGY ●A self-report questionnaire with 49 items was used as study

instrument: – 10 items on attitudes toward smartphone use in the

classroom – 12 items on students’ participation in school activitieis– 12 items on smartphone use intensity for learning– 8 items about self-assessment of satisfaction with school

studies – 6 items on demographic data– 1 item on the participants’ latest average academic grade

information

●303 voluntary vocational school students responded the questionnaire. 98% are smartphone owners.

RESULTSH1: Students showing more positive attitudes toward smartphone use in classrooms (=against restricction policies) will also use it more intensively for learning purposes.

The opposite is also true…

Perception Dimensions

Apps for education

Classmate networking

Messaging to teachers

r sig r sig r sig

Phone ring bothering -.002 .977 -.02 .777 -.04 .453

Favor restrictive policies -.25 .000 -.28 .000 -.01 .916

Complaints about use -.09 .114 -.13 .024 .03 .575

Smartphone as a problem .04 .511 .01 .925 -.02 .781N = 303; ***p <.001; **p < .01; *p < .05

H2-a: Students using smartphones more intensively for learning purposes will be more highly engaged in school activities

The results show significant positive associations between the single factor “student’s engagement” (grouping the 12 original items at Cronbach α= .70) and their smartphone use for “education purposes” (r=.29; p<.001); “classmate networking” (r=.25; p<.001); “communication with teachers” (r=.17; p<.001). On the other hand…

Smartphone Uses in Classroom

Self-assessment (group integration, doubts solved by teachers)

r p

Apps for Education .08 .164

Classmate Networking .10 .097

Messaging with Teachers -.06 .339N = 303; *** p < .001; ** p < .01: * p < .05

H2-b: Students using smartphones more intensively for learning purposes will have better academic performance and show higher satisfaction with their studies.

Hypothesis not supported

Applications Classmate networkin

g

Messaging teachers

r sig r sig r sigSatisfaction about studies .04 .466 .08 .197 .01 .115

Satisfaction about classes -.01 .863 .06 .311 .002 .966

Average grade -.03 .707 -.12 .059 .09 .156

Grade rating -.02 .728 -.14 .035 .09 .170

N = 303; ***p <.001; **p < .01; *p < .05; +p <0.1

H3: Younger students using smartphones more intensively for learning purposes than older students will show higher academic performance outcomes. Hypothesis not supported

● Age (M) has a significant positive effect on elder students’ academic performance, B(AgeR) (b2)= .25, p=.003.

X

M

XM

Y

ey

1

b1 = -1.53, p =.076

b2 = .256, p =.003

b3 = -3.84, p =.026

● The smartphone use in the classroom (X) seems to produce a negative effect on the academic performance in general.

B (Smartphone Apps UseR) (b1)= -1.538, p= .076, with marginal significance.

● The interaction of both (XM) still presents significant negative effect on the students academic performance in general, B(Int_1) (b3)= -3.847, p= .026]

Academic performance outcomes of students with different age and classroom use of smartphone apps for education

B(+ 21 =.05) = -3.462p = .006

B(- 20 = -.05) = .385p = .745

m= 2.39

m=2.05

m=1.94 m=1.98

● The elder students perform better learning ability when the less smartphone they use in the classroom, m= 2.39 vs m= 2.05, p<,01.

● An opposite effect is observed in younger students, m= 1.94 vs m=1.98, those who use smartphone more intensively show a slightly academic improvement. But it is proved non statistically significant, p =.745

Analysis of bootstrapping path model: two mediator model

e M 1

e Y

1

c´ = .18, *p = .035

a1 = .26*p = .050

 

Age

a2 = .24*p = .015

Academicperformanc

e 

e  M 2

d2 1 = .06 p = .178

Self-Engagement

 1

b1 = .11**p = .007

b2 = .07 p = .191

M1

M2

X

Attitudes to policiesof smartphone use restriction

 

Y

1

Positive attitude to “restriction policies” with a significant effect on their “academic performance”, Brestric(b1) = .11, p < .01.

A significant positive effect of students’ age on their attitudes toward school restriction policies of smartphone use in the classroom school, Bage (a1)= .26, p = .050

“Students’ age” indicates a significant positive effect on their “self-engagement” in learning process, Bage (a2) = .24, p< .05

Also on their “engagement but with no statistical significance”, Brestric (d21) = .06,

p = .178.

“Self-engagement” has positive effect on “academic performance”. However it is not proved to be statistically significant, Bengage(b2) = .07, p=.191.

“Elder age” produces a significant positive effect on students’ “academic performance”, Bage(c')=.18, p< .05.

Direct Effects

e M 1

e Y

1

a1 = .26*p = .050

 

Age

a2 = .24*p = .015v

Academicperformance

 

e  M 2

d2 1 = .06 p = .178

Self-engagement

 1

b1 = .11**p = .007

b2 = .07 p = .191

M1

M2

X

Attitudes to policies of smartphone use restriction

 

Y c = .23, **p = .008

1

Indirect Effects

1st indirect effect: “students’ age” on “academic performance” through “restriction attitude” (X→ M1→Y), estimated as Ind2= a1 b1= (.261) (.113) =.029. The resulting indirect effect is significant with B= .029, SE=.02, 95% CI [.002, .079],

3rd indirect effect: “students’ age” on “academic performance” through [“restriction attitude” + “self-engagement”] influence in serial (X→M1→M2→Y), which in turn influences “academic performance”, estimated as a1 d21 b2, with B= .001, SE=.002, 95% CI [.000, .009], with no statistical significance.

2nd indirect effect: students’ ages on academic performance through self-engagement alone (X→ M2→Y), estimated as a2 b2, producing an effect with B= .018, SE=.017, 95% CI [-.005, .06], presenting no statistical significance.

Total indirect effect: A serial multiple mediator model X→Y, estimated as c the sum of all specific indirect effects, resulting a significant Total effect =.049, SE=.024, 95% CI [.010, .109].

Students’ age in the present study has proved to be a significant predicting variable that determines attitudes towards smartphone use in the classroom.

Conclusion

The path analysis outcomes indicate that the “elder students’ group” with a “more supportive attitude” toward school “restrictive policies” for smartphone use in the classroom achieve “better academic performance”.

The “younger students’ group” appears to improve slightly their academic performance while increasing smartphone use intensity in classroom. However, this effect has not been found consistent

The finding reveals a certain existing generational barrier that may prevent smartphone APPs from becoming innovated teaching-learning tools for educational goals, particularly, in the vocational schools in Spanish Castilian & Leon Area.

Thank you for your attention!