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Research article

A ranking comparison of the traditional, online and mixed laboratory mode learning objectives in engineering: Uncovering different priorities


  • Received: 13 September 2023 Revised: 29 November 2023 Accepted: 05 December 2023 Published: 28 December 2023
  • The laboratory, an integral component of engineering education, can be conducted via traditional, online or mixed modes. Within these modes is a diverse range of implementation formats, each with different strengths and weaknesses. Empirical evidence investigating laboratory learning is rather scattered, with objectives measurement focused on the innovation in question (e.g., new simulation or experiment). Recently, a clearer picture of the most important laboratory learning objectives has formed. Missing is an understanding of whether academics implementing laboratories across different modes think about learning objectives differently. Using a survey based on the Laboratory Learning Objectives Measurement instrument, academics from a diverse range of engineering disciplines from across the world undertook a ranking exercise. The findings show that those implementing traditional and mixed laboratories align closely in their ranking choices, while those implementing online-only laboratories think about the objectives slightly differently. These findings provide an opportunity for reflection, enabling engineering educators to refine the alignment of their teaching modes, implementations and assessments with their intended learning objectives.

    Citation: Sasha Nikolic, Sarah Grundy, Rezwanul Haque, Sulakshana Lal, Ghulam M. Hassan, Scott Daniel, Marina Belkina, Sarah Lyden, Thomas F. Suesse. A ranking comparison of the traditional, online and mixed laboratory mode learning objectives in engineering: Uncovering different priorities[J]. STEM Education, 2023, 3(4): 331-349. doi: 10.3934/steme.2023020

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  • The laboratory, an integral component of engineering education, can be conducted via traditional, online or mixed modes. Within these modes is a diverse range of implementation formats, each with different strengths and weaknesses. Empirical evidence investigating laboratory learning is rather scattered, with objectives measurement focused on the innovation in question (e.g., new simulation or experiment). Recently, a clearer picture of the most important laboratory learning objectives has formed. Missing is an understanding of whether academics implementing laboratories across different modes think about learning objectives differently. Using a survey based on the Laboratory Learning Objectives Measurement instrument, academics from a diverse range of engineering disciplines from across the world undertook a ranking exercise. The findings show that those implementing traditional and mixed laboratories align closely in their ranking choices, while those implementing online-only laboratories think about the objectives slightly differently. These findings provide an opportunity for reflection, enabling engineering educators to refine the alignment of their teaching modes, implementations and assessments with their intended learning objectives.



    In this paper, we consider the Schrödinger operators

    L=+V(x),xRn,n3,

    where Δ=ni=122xi and V(x) is a nonnegative potential belonging to the reverse Hölder class RHq for some qn2. Assume that f is a nonnegative locally Lq(Rn) integrable function on Rn, then we say that f belongs to RHq (1<q) if there exists a positive constant C such that the reverse Hölder's inequality

    (1|B(x,r)|B(x,r)|f(y)|qdy)1qC|B(x,r)|B(x,r)|f(y)|dy

    holds for x in Rn, where B(x,r) denotes the ball centered at x with radius r< [1]. For example, the nonnegative polynomial VRH, in particular, |x|2RH.

    Let the potential VRHq with qn2, and the critical radius function ρ(x) is defined as

    ρ(x)=supr>0{r:1rn2B(x,r)V(y)dy1},xRn. (1.1)

    We also write ρ(x)=1mV(x),xRn. Clearly, 0<mV(x)< when V0, and mV(x)=1 when V=1. For the harmonic oscillator operator (Hermite operator) H=Δ+|x|2, we have mV(x)(1+|x|).

    Thanks to the heat diffusion semigroup etL for enough good function f, the negative powers Lα2(α>0) related to the Schrödinger operators L can be written as

    Iαf(x)=Lα2f(x)=0etLf(x)tα21dt,0<α<n. (1.2)

    Applying Lemma 3.3 in [2] for enough good function f holds that

    Iαf(x)=RnKα(x,y)f(y)dy,0<α<n,

    and the kernel Kα(x,y) satisfies the following inequality

    Kα(x,y)Ck(1+|xy|(mV(x)+mV(y)))k1|xy|nα. (1.3)

    Moreover, we have Kα(x,y)C|xy|nα,0<α<n.

    Shen [1] obtained Lp estimates of the Schrödinger type operators when the potential VRHq with qn2. For Schrödinger operators L=Δ+V with VRHq for some qn2, Harboure et al. [3] established the necessary and sufficient conditions to ensure that the operators Lα2(α>0) are bounded from weighted strong and weak Lp spaces into suitable weighted BMOL(w) space and Lipschitz spaces when pnα. Bongioanni Harboure and Salinas proved that the fractional integral operator Lα/2 is bounded form Lp,(w) into BMOβL(w) under suitable conditions for weighted w [4]. For more backgrounds and recent progress, we refer to [5,6,7] and references therein.

    Ramseyer, Salinas and Viviani in [8] studied the fractional integral operator and obtained the boundedness from strong and weak Lp() spaces into the suitable Lipschitz spaces under some conditions on p(). In this article, our main interest lies in considering the properties of fractional integrals operator Lα2(α>0), related to L=Δ+V with VRHq for some qn2 in variable exponential spaces.

    We now introduce some basic properties of variable exponent Lebsegue spaces, which are used frequently later on.

    Let p():Ω[1,) be a measurable function. For a measurable function f on Rn, the variable exponent Lebesgue space Lp()(Ω) is defined by

    Lp()(Ω)={f:Ω|f(x)s|p(x)dx<},

    where s is a positive constant. Then Lp()(Ω) is a Banach space equipped with the follow norm

    fLp()(Ω):=inf{s>0:Ω|f(x)s|p(x)dx1}.

    We denote

    p:=essinfxΩp(x) and p+:=esssupxΩp(x).

    Let P(Rn) denote the set of all measurable functions p on Rn that take value in [1,), such that 1<p(Rn)p()p+(Rn)<.

    Assume that p is a real value measurable function p on Rn. We say that p is locally log-Hölder continuous if there exists a constant C such that

    |p(x)p(y)|Clog(e+1/|xy|),x,yRn,

    and we say p is log-Hölder continuous at infinity if there exists a positive constant C such that

    |p(x)p()|Clog(e+|x|),xRn,

    where p():=lim|x|p(x)R.

    The notation Plog(Rn) denotes all measurable functions p in P(Rn), which states p is locally log-Hölder continuous and log-Hölder continuous at infinity. Moreover, we have that p()Plog(Rn), which implies that p()Plog(Rn).

    Definition 1.1. [8] Assume that p() is an exponent function on Rn. We say that a measurable function f belongs to Lp(),(Rn), if there exists a constant C such that for t>0,

    Rntp(x)χ{|f|>t}(x)dxC.

    It is easy to check that Lp(),(Rn) is a quasi-norm space equipped with the following quasi-norm

    fp(),=inf{s>0:supt>0Rn(ts)p(x)χ{|f|>t}(x)dx1}.

    Next, we define LipLα,p() spaces related to the nonnegative potential V.

    Definition 1.2. Let p() be an exponent function with 1<pp+< and 0<α<n. We say that a locally integrable function fLipLα,p()(Rn) if there exist constants C1,C2 such that for every ball BRn,

    1|B|αnχBp()B|f(x)mBf|dxC1, (1.4)

    and for Rρ(x),

    1|B|αnχBp()B|f(x)|dxC2, (1.5)

    where mBf=1|B|Bf. The norm of space LipLα,p()(Rn) is defined as the maximum value of two infimum of constants C1 and C2 in (1.4) and (1.5).

    Remark 1.1. LipLα,p()(Rn)Lα,p()(Rn) is introduced in [8]. In particular, when p()=C for some constant, then LipLα,p()(Rn) is the usual weighted BMO space BMOβL(w), with w=1 and β=αnp [4].

    Remark 1.2. It is easy to see that for some ball B, the inequality (1.5) leads to inequality (1.4) holding, and the average mBf in (1.4) can be replaced by a constant c in following sense

    12fLipLα,p()supBRninfcR1|B|αnχBp()B|f(x)c|dxfLipLα,p().

    In 2013, Ramseyer et al. in [8] studied the Lipschitz-type smoothness of fractional integral operators Iα on variable exponent spaces when p+>αn. Hence, when p+>αn, it will be an interesting problem to see whether or not we can establish the boundedness of fractional integral operators Lα2(α>0) related to Schrödinger operators from Lebesgue spaces Lp() into Lipschitz-type spaces with variable exponents. The main aim of this article is to answer the problem above.

    We now state our results as the following two theorems.

    Theorem 1.3. Let potential VRHq for some qn/2 and p()Plog(Rn). Assume that 1<pp+<n(αδ0)+ where δ0=min{1,2n/q}, then the fractional integral operator Iα defined in (1.2) is bounded from Lp()(Rn) into LipLα,p()(Rn).

    Theorem 1.4. Let the potential VRHq with qn/2 and p()Plog(Rn). Assume that 1<pp+<n(αδ0)+ where δ0=min{1,2n/q}. If there exists a positive number r0 such that p(x)p when |x|>r0, then the fractional integral operator Iα defined in (1.2) is bounded from Lp(),(Rn) into LipLα,p()(Rn).

    To prove Theorem 1.3, we first need to decompose Rn into the union of some disjoint ball B(xk,ρ(xk))(k1) according to the critical radius function ρ(x) defined in (1.1). According to Lemma 2.6, we establish the necessary and sufficient conditions to ensure fLipLα,p()(Rn). In order to prove Theorem 1.3, by applying Corollary 1 and Remark 1.2, we only need to prove that the following two conditions hold:

    (ⅰ) For every ball B=B(x0,r) with r<ρ(x0), then

    B|Iαf(x)c|dxC|B|αnχBp()fp();

    (ⅱ) For any x0Rn, then

    B(x0,ρ(x0))Iα(|f|)(x)dxC|B(x0,ρ(x0))|αnχB(x0,ρ(x0))p()fp().

    In order to check the conditions (ⅰ) and (ⅱ) above, we need to find the accurate estimate of kernel Kα(x,y) of fractional integral operator Iα (see Lemmas 2.8 and 2.9, then use them to obtain the proof of this theorem; the proof of the Theorem 1.4 proceeds identically).

    The paper is organized as follows. In Section 2, we give some important lemmas. In Section 3, we are devoted to proving Theorems 1.3 and 1.4.

    Throughout this article, C always means a positive constant independent of the main parameters, which may not be the same in each occurrence. B(x,r)={yRn:|xy|<r}, Bk=B(x0,2kR) and χBk are the characteristic functions of the set Bk for kZ. |S| denotes the Lebesgue measure of S. fg means C1gfCg.

    In this section, we give several useful lemmas that are used frequently later on.

    Lemma 2.1. [9] Assume that the exponent function p()P(Rn). If fLp()(Rn) and gLp()(Rn), then

    Rn|f(x)g(x)|dxrpfLp()(Rn)gLp()(Rn),

    where rp=1+1/p1/p+.

    Lemma 2.2. [8] Assume that p()Plog(Rn) and 1<pp+<, and p(x)p() when |x|>r0>1. For every ball B and fLp(), we have

    B|f(x)|dxCfLp(),χBLp(),

    where the constant C only depends on r0.

    Fo the following lemma see Corollary 4.5.9 in [10].

    Lemma 2.3. Let p()Plog(Rn), then for every ball BRn we have

    χBp()|B|1p(x),if|B|2n,xB,

    and

    χBp()|B|1p(),if|B|1.

    Lemma 2.4. Assume that p()Plog(Rn), then for all balls B and all measurable subsets S:=B(x0,r0)B:=B(x1,r1) we have

    χSp()χBp()C(|S||B|)11p,   χBp()χSp()C(|B||S|)11p+. (2.1)

    Proof. We only prove the first inequality in (2.1), and the second inequality in (2.1) proceeds identically. We consider three cases below by applying Lemma 2.3, and it holds that

    1) if |S|<1<|B|, then χSp()χBp()|S|1p(xS)|B|1p()(|S||B|)1(p)+=(|S||B|)11p;

    2) if 1|S|<|B|, then χSp()χBp()|S|1p()|B|1p()(|S||B|)1(p)+=(|S||B|)11p;

    3) if |S|<|B|<1, then χSp()χBp()|S|1p(xS)|B|1p(xS)|B|1p(xS)1p(xB)C(|S||B|)1(p)+=C(|S||B|)11p, where xSS and xBB.

    Indeed, since |xBxS|2r1, by using the local-Hölder continuity of p(x) we have

    |1p(xS)1p(xB)|log1r1log1r1log(e+1|xSxB|)log1r1log(e+12r1)C.

    We end the proof of this lemma.

    Remark 2.1. Thanks to the second inequality in (2.1), it is easy to prove that

    χ2Bp()CχBp().

    Lemma 2.5. [1] Suppose that the potential VBq with qn/2, then there exists positive constants C and k0 such that

    1) ρ(x)ρ(y) when |xy|Cρ(x);

    2) C1ρ(x)(1+|xy|ρ(x))k0ρ(y)Cρ(x)(1+|xy|ρ(x))k0/(k0+1).

    Lemma 2.6. [11] There exists a sequence of points {xk}k=1 in Rn such that Bk:=B(xk,ρ(xk)) satisfies

    1) Rn=kBk,

    2) For every k1, then there exists N1 such that card {j:4Bj4Bk}N.

    Lemma 2.7. Assume that p()P(Rn) and 0<α<n. Let sequence {xk}k=1 satisfy the propositions of Lemma 2.6. Then a function fLipLα,p()(Rn) if and only if f satisfies (1.4) for every ball, and

    1|B(xk,ρ(xk))|αnχB(xk,ρ(xk))p()B(xk,ρ(xk))|f(x)|dxC,forallk1. (2.2)

    Proof. Let B:=B(x,R) denote a ball with center x and radius R>ρ(x). Noting that f satisfies (1.4), and thanks to Lemma 2.6 we obtain that the set G={k:BBk} is finite.

    Applying Lemma 2.5, if zBkB, we get

    ρ(xk)Cρ(z)(1+|xkz|ρ(xk))k0C2k0ρ(z)C2k0ρ(x)(1+|xz|ρ(x))k0k0+1C2k0ρ(x)(1+Rρ(x))C2k0R.

    Thus, for every kG, we have BkCB.

    Thanks to Lemmas 2.4 and 2.6, it holds that

    B|f(x)|dx=BkBk|f(x)|dx=kG(BBk)|f(x)|dxkGBBk|f(x)|dxkGBk|f(x)|dxCkG|Bk|αnχBkp()C|B|αnχBp().

    The proof of this lemma is completed.

    Corollary 1. Assume that p()P(Rn) and 0<α<n, then a measurable function fLipLα,p() if and only if f satisfies (1.4) for every ball B(x,R) with radius R<ρ(x) and

    1|B(x,ρ(x))|αnχB(x,ρ(x))p()B(x,ρ(x))|f(x)|dxC. (2.3)

    Let kt(x,y) denote the kernel of heat semigroup etL associated to L, and Kα(x,y) be the kernel of fractional integral operator Iα, then it holds that

    Kα(x,y)=0kt(x,y)tα2dt. (2.4)

    Some estimates of kt are presented below.

    Lemma 2.8. [12] There exists a constant C such that for N>0,

    kt(x,y)Ctn/2e|xy|2Ct(1+tρ(x)+tρ(y))N,x,yRn.

    Lemma 2.9. [13] Let 0<δ<min(1,2nq). If |xx0|<t, then for N>0 the kernel kt(x,y) defined in (2.4) satisfies

    |kt(x,y)kt(x0,y)|C(|xx0|t)δtn/2e|xy|2Ct(1+tρ(x)+tρ(y))N,

    for all x,y and x0 in Rn.

    In this section, we are devoted to the proof of Theorems 1.3 and 1.4. To prove Theorem 1.3, thanks to Corollary 1 and Remark 1.2, we only need to prove that the following two conditions hold:

    (ⅰ) For every ball B=B(x0,r) with r<ρ(x0), then

    B|Iαf(x)c|dxC|B|αnχBp()fp();

    (ⅱ) For any x0Rn, then

    B(x0,ρ(x0))Iα(|f|)(x)dxC|B(x0,ρ(x0))|αnχB(x0,ρ(x0))p()fp().

    We now begin to check that these conditions hold. First, we prove (ⅱ).

    Assume that B=B(x0,R) and R=ρ(x0). We write f=f1+f2, where f1=fχ2B and f2=fχRn2B. Hence, by the inequality (1.3), we have

    BIα(|f1|)(x)dx=BIα(|fχ2B|)(x)dxCB2B|f(y)||xy|nαdydx.

    Applying Tonelli theorem, Lemma 2.1 and Remark 1.2, we get the following estimate

    BIα(|f1|)(x)dxC2B|f(y)|Bdx|xy|nαdyCRα2B|f(y)|dyC|B|αnχBp()fp(). (3.1)

    To deal with f2, let xB and we split Iαf2 as follows:

    Iαf2(x)=R20etLf2(x)tα21dt+R2etLf2(x)tα21dt:=I1+I2.

    For I1, if xB and yRn2B, we note that |x0y|<|x0x|+|xy|<C|xy|. By Lemma 2.8, it holds that

    I1=|R20Rn2Bkt(x,y)f(y)dytα21dt|CR20Rn2Btn2e|xy|2t|f(y)|dytα21dtCR20tn+α21Rn2B(t|xy|2)M/2|f(y)|dydtCR20tMn+α21dtRn2B|f(y)||x0y|Mdy,

    where the constant C only depends the constant M.

    Applying Lemma 2.1 to the last integral, we get

    Rn2B|f(y)||x0y|Mdy=i=12i+1B2iB|f(y)||x0y|Mdyi=1(2iR)M2i+1B|f(y)|dyCi=1(2iR)Mχ2i+1Bp()fp().

    By using Lemma 2.4, we arrive at the inequality

    Rn2B|f(y)||x0y|MdyCi=1(R)M(2i)nnp+MχBp()fp()CRMfp()χBp(). (3.2)

    Here, the series above converges when M>nnp+. Hence, for such M,

    |R20etLf2(x)tα21dt|CRMfp()χBp()R20tMn+α21dtC|B|αn1fp()χBp().

    For I2, thanks to Lemma 2.8, we may choose M as above and NM, then it holds that

    |R2etLf2(x)tα22dt|=|R2Rn2Bkt(x,y)f(y)dytα22dt|CR2Rn2BtαnN22ρ(x)Ne|xy|2t|f(y)|dydtCρ(x)NR2tαnN22Rn2B(t|xy|2)M/2|f(y)|dydt.

    As xB, thanks to Lemma 2.5, ρ(x)ρ(x0)=R. Hence we have

    |R2etLf2(x)tα21dt|CRNR2tM+αnN21dtRn2B|f(y)||x0y|Mdy.

    Since M+αnN<0, the integral above for variable t converges, and by applying inequality (3.2) we have

    |R2etLf2(x)tα21dt|C|B|αn1fp()χBp(),

    thus we have proved (ⅱ).

    We now begin to prove that the condition (ⅰ) holds. Let B=B(x0,r) and r<ρ(x0). We set f=f1+f2 with f1=fχ2B and f2=fχRn2B. We write

    cr=r2etLf2(x0)tα21dt. (3.3)

    Thanks to (3.1), it holds that

    B|Iα(f(x))cr|BIα(|f1|)(x)dx+B|Iα(f2)(x)cr|dxC|B|αn1χBp()fp()+B|Iα(f2)(x)cr|dx.

    Let xB and we split Iαf2(x) as follows:

    Iαf2(x)=r20etLf2(x)tα21dt+r2etLf2(x)tα21dt:=I3+I4.

    For I3, by the same argument it holds that

    I3=|r20etLf2(x)tα21dt|C|B|αn1fp()χBp().

    For I4, by Lemma 2.9 and (3.3), it follows that

    |r2etLf2(x)tα21dtcr|r2Rn2B|kt(x,y)kt(x0,y)||f(y)|dytα21dtCδr2Rn2B(|xx0|t)δtn/2e|xy|2Ct|f(y)|dytα21dtCδrδRn2B|f(y)|r2t(nα+δ)/2e|xy|2Ctdttdy.

    Let s=|xy|2t, then we obtain the following estimate

    |r2etLf2(x)tα21dtcr|CδrδRn2B|f(y)||xy|nα+δdy0snα+δ2esCdss.

    Notice that the integral above for variable s is finite, thus we only need to compute the integral above for variable y. Thanks to inequality (3.2), it follows that

    |r2etLf2(x)tα21dtcr|CδrδRn2B|f(y)||xy|nα+δdyCi=1Rαn(2i)αnp+δχBp()fp()C|B|αnnfp()χBp(),

    so (ⅰ) is proved.

    Remark 3.1. By the same argument as the proof of Theorem 1.3, thanks to Lemma 2.2 we immediately obtained that the conclusions of Theorem 1.4 hold.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    Ping Li is partially supported by NSFC (No. 12371136). The authors would like to thank the anonymous referees for carefully reading the manuscript and providing valuable suggestions, which substantially helped in improving the quality of this paper. We also thank Professor Meng Qu for his useful discussions.

    The authors declare there are no conflicts of interest.



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  • Author's biography Sasha Nikolic received a B.E. degree in telecommunications and a PhD in engineering education from the University of Wollongong, Australia, in 2001 and 2017, respectively. He is a Senior Lecturer of Engineering Education at the University of Wollongong. His interest is developing career-ready graduates involving research in teaching laboratories, artificial intelligence, industry engagement, work-integrated learning, knowledge management, communication, and reflection. Dr Nikolic has been recognised with many awards, including an Australian Award for University Teaching Citation in 2012 and 2019, and a 2023 AAEE Engineering Education Research Design Award. He is a member of the executive committee of AAEE and an Associate Editor for AJEE and EJEE; Sarah Grundy is an education-focused lecturer at the School of Chemical Engineering, The University of New South Wales. Sarah predominantly teaches design subjects at all levels (undergraduate to postgraduate). Sarah has over 15 years of experience in Research & Development, Manufacturing, and project management in industry. Sarah's passion is to develop students to be credible engineers and make their impact in whatever industry through authentic learning practices; Dr. Rezwanul Haque is a Senior Lecturer specialising in Manufacturing Technology at the University of the Sunshine Coast. As an inaugural member of the AAEE Academy, he has contributed significantly to the academic community. In 2019, Dr. Haque served as an Academic Lead at the School of Science and Technology, overseeing the launch of two new Engineering programs and reviewing existing ones. His dedication to learning and teaching earned him the prestigious Senior Fellow status at the Higher Education Academy (UK) in the same year. His research focuses on Engineering Education and material characterisation through neutron diffraction; Sulakshana Lal has a PhD in Engineering Education from Curtin University, Perth, WA, Australia. Her research focused on comparing the learning and teaching processes of face-to-face and remotely-operated engineering laboratories. With a keen interest in the intersection of technology and education, Sulakshana has published several articles in reputable journals and also presented her work at national and international engineering education conferences. Her expertise lies in understanding the nuances of different laboratories pedagogical settings and harnessing technology to enhance laboratory learning outcomes. Sulakshana is passionate about sharing her knowledge and helping educators and students navigate the evolving landscape of engineering education; Dr. Ghulam M. Hassan is Senior Lecturer in Department of Computer Science and Software Engineering at The University of Western Australia (UWA). He received his PhD from UWA. He completed MS and BS from Oklahoma State University, USA and University of Engineering and Technology (UET) Peshawar, Pakistan, respectively. His research interests are multidisciplinary problems, including engineering education, artificial intelligence, machine learning and optimisation in different fields of engineering and education. He is the recipient of multiple teaching excellence awards and is awarded AAEE Engineering Education Research Design Award 2021 & 2023; Scott Daniel is a Senior Lecturer in Humanitarian Engineering at the University of Technology Sydney, and serves as Deputy Editor at the Australasian Journal of Engineering Education and on the Editorial Boards of the European Journal of Engineering Education, the African Journal of Teacher Education and Development, and the Journal of Humanitarian Engineering. Scott uses qualitative methodologies to explore different facets of engineering education, particularly humanitarian engineering. He won the 2019 Australasian Association for Engineering Education Award for Research Design for his work with Andrea Mazzurco on the assessment of socio-technical thinking and co-design expertise in humanitarian engineering; Dr. Marina Belkina is Lecturer and First Year Experience Coordinator at Western Sydney University. She has taught various subjects and courses (Foundation, Diploma, first and second years of Bachelor's Degree, online Associate Degree). She has implemented numerous projects to support learning, including: Creating the YouTube channel Engineering by Steps, Leading the development of HD videos for the first-year engineering courses, Developing iBook for physics, creating 3D lectures and aminations for Engineering Materials, and conducting research focused on exploring student's barriers to Higher Education; Sarah Lyden completed her BSc-BE (Hons) at the University of Tasmania in 2011. From 2012 to 2015 she was a PhD candidate with the School of Engineering and ICT at the University of Tasmania. From March 2015 to February 2018 Sarah was employed as the API Lecturer in the field of power systems and renewable energy. Since 2018, Sarah has been employed as Lecturer in the School of Engineering. Sarah has been a member of the School of Engineering and ICT's STEM education and outreach team; Dr. Thomas F. Suesse completed his MSc (Dipl-Math) degree in mathematics at the Friedrich-Schiller-University (FSU) of Jena, Germany, in 2003. Dr Suesse then worked as a research fellow at the Institute of Medical Statistics, Informatics and Documentation (IMSID) and FSU. In 2005 he went to Victoria University of Wellington (VUW), New Zealand, to start his PhD in statistics and his degree was conferred with his thesis titled, 'Analysis and Diagnostics of Categorical Variables with Multiple Outcomes' in 2008. In 2009 Dr Suesse started working as a research fellow at the Centre for Statistical and Survey Methodology (CSSM) at the University of Wollongong. He was appointed as a lecturer at UOW in 2011 and promoted to senior lecturer in 2015. Currently he is at FSU on a research on a research fellowship
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